MLS-C01 AWS Certified Machine Learning - Specialty Questions and Answers

The combination of actions that will meet the requirements with the least operational overhead is to use SageMaker Clarify to automatically detect data bias and to configure SageMaker Data Wrangler to generate a bias report. SageMaker Clarify is a feature of Amazon SageMaker that provides machine learning (ML) developers with tools to gain greater insights into their ML training data and models. SageMaker Clarify can detect potential bias during data preparation, after model training, and in your deployed model. For instance, you can check for bias related to age in your dataset or in your trained model and receive a detailed report that quantifies different types of potential bias1. SageMaker Data Wrangler is another feature of Amazon SageMaker that enables you to prepare data for machine learning (ML) quickly and easily. You can use SageMaker Data Wrangler to identify potential bias during data preparation without having to write your own code. You specify input features, such as gender or age, and SageMaker Data Wrangler runs an analysis job to detect potential bias in those features. SageMaker Data Wrangler then provides a visual report with a description of the metrics and measurements of potential bias so that you can identify steps to remediate the bias2. The other actions either require more customization (such as using SageMaker Model Monitor or SageMaker Experiments) or do not meet the requirement of detecting data bias (such as using SageMaker Ground Truth). References:

1: Bias Detection and Model Explainability – Amazon Web Services

2: Amazon SageMaker Data Wrangler – Amazon Web Services

Switching to an instance type that has a CPU: GPU ratio of 6:1 will reduce the training costs by using fewer CPUs and GPUs, while maintaining the same level of performance. The GPU idle time indicates that the CPU is not able to feed the GPU with enough data, so reducing the CPU: GPU ratio will balance the workload and improve the GPU utilization. A lower CPU: GPU ratio also means less overhead for inter-process communication and synchronization between the CPU and GPU processes. References:

Optimizing GPU utilization for AI/ML workloads on Amazon EC2

Analyze CPU vs. GPU Performance for AWS Machine Learning

To achieve high availability for Amazon SageMaker endpoints, especially with a Recovery Time Objective (RTO) of 5 minutes, it's essential to deploy the endpoints across multiple Availability Zones (AZs).​

According to the AWS documentation:​

"Create robust endpoints when hosting your model. SageMaker AI endpoints can help protect your application from Availability Zone outages and instance failures. If an outage occurs or an instance fails, SageMaker AI automatically attempts to distribute your instances across Availability Zones. For this reason, we strongly recommend that you deploy multiple instances for each production endpoint."​

Additionally:​

"If you are using an Amazon Virtual Private Cloud (VPC), configure the VPC with at least two Subnets, each in a different Availability Zone. If an outage occurs or an instance fails, Amazon SageMaker AI automatically attempts to distribute your instances across Availability Zones."​

By configuring the VPC with subnets in multiple AZs and deploying multiple instances of the endpoint, SageMaker can automatically handle failovers in case of an AZ outage, thus meeting the stringent RTO requirement.​

Residuals are the differences between the actual and predicted values of the target variable in a regression model. A histogram of residuals is a graphical tool that can help evaluate the performance and assumptions of the model. Ideally, the histogram of residuals should have a zero-centered bell shape, which indicates that the residuals are normally distributed with a mean of zero and a constant variance. This means that the model has captured the true relationship between the input and output variables, and that the errors are random and unbiased. However, if the histogram of residuals does not have a zero-centered bell shape, as shown in the image, this means that the model might have prediction errors over a range of target values. This is because the residuals do not form a symmetrical and homogeneous distribution around zero, which implies that the model has some systematic bias or heteroscedasticity. This can affect the accuracy and validity of the model, and indicate that the model needs to be improved or modified.

Residual Analysis in Regression - Statistics By Jim

How to Check Residual Plots for Regression Analysis - dummies

Histogram of Residuals - Statistics How To

A hyperparameter tuning job is a feature of Amazon SageMaker that allows automatically finding the best combination of hyperparameters for a machine learning model. Hyperparameters are high-level parameters that influence the learning process and the performance of the model, such as the learning rate, the number of layers, the regularization factor, etc. A hyperparameter tuning job works by launching multiple training jobs with different hyperparameters, evaluating the results using an objective metric, and choosing the next set of hyperparameters to try based on a search strategy. The objective metric is a measure of the quality of the model, such as accuracy, precision, recall, etc. The search strategy is a method of exploring the hyperparameter space, such as random search, grid search, or Bayesian optimization.

Among the four options, option C is the most repeatable and requires the least amount of effort to use hyperparameter optimization to increase the model’s accuracy. This option involves the following steps:

Create a hyperparameter tuning job: Amazon SageMaker provides an easy-to-use interface for creating a hyperparameter tuning job, either through the AWS Management Console, the AWS CLI, or the AWS SDKs. To create a hyperparameter tuning job, the Machine Learning Specialist needs to specify the following information:

The name and type of the algorithm to use, either a built-in algorithm or a custom algorithm.

The ranges and types of the hyperparameters to tune, such as categorical, continuous, or integer.

The name and type of the objective metric to optimize, such as accuracy, and whether to maximize or minimize it.

The resource limits for the tuning job, such as the maximum number of training jobs and the maximum parallel training jobs.

The input data channels and the output data location for the training jobs.

The configuration of the training instances, such as the instance type, the instance count, the volume size, etc.

Set the accuracy as an objective metric: To use accuracy as an objective metric, the Machine Learning Specialist needs to ensure that the training algorithm writes the accuracy value to a file called metric_definitions in JSON format and prints it to stdout or stderr. For example, the file can contain the following content:

This means that the training algorithm prints a line like this:

Amazon SageMaker reads the accuracy value from the line and uses it to evaluate and compare the training jobs.

The other options are not as repeatable and require more effort than option C for the following reasons:

Option A: This option requires manually launching multiple training jobs in parallel with different hyperparameters, which can be tedious and error-prone. It also requires manually monitoring and comparing the results of the training jobs, which can be time-consuming and subjective.

Option B: This option requires writing code to create an AWS Step Functions workflow that monitors the accuracy in Amazon CloudWatch Logs and relaunches the training job with a defined list of hyperparameters, which can be complex and challenging. It also requires maintaining and updating the list of hyperparameters, which can be inefficient and suboptimal.

Option D: This option requires writing code to create a random walk in the parameter space to iterate through a range of values that should be used for each individual hyperparameter, which can be unreliable and unpredictable. It also requires defining and implementing a stopping criterion, which can be arbitrary and inconsistent.

Automatic Model Tuning - Amazon SageMaker

Define Metrics to Monitor Model Performance

The solution C is the most likely to improve the data ingestion rate into Amazon S3 because it increases the number of shards for the data stream. The number of shards determines the throughput capacity of the data stream, which affects the rate of data ingestion. Each shard can support up to 1 MB per second of data input and 2 MB per second of data output. By increasing the number of shards, the company can increase the data ingestion rate proportionally. The company can use the UpdateShardCount API operation to modify the number of shards in the data stream1.

The other options are not likely to improve the data ingestion rate into Amazon S3 because:

Option A: Increasing the number of S3 prefixes for the delivery stream to write to will not affect the data ingestion rate, as it only changes the way the data is organized in the S3 bucket. The number of S3 prefixes can help to optimize the performance of downstream applications that read the data from S3, but it does not impact the performance of Kinesis Data Firehose2.

Option B: Decreasing the retention period for the data stream will not affect the data ingestion rate, as it only changes the amount of time the data is stored in the data stream. The retention period can help to manage the data availability and durability, but it does not impact the throughput capacity of the data stream3.

Option D: Adding more consumers using the Kinesis Client Library (KCL) will not affect the data ingestion rate, as it only changes the way the data is processed by downstream applications. The consumers can help to scale the data processing and handle failures, but they do not impact the data ingestion into S3 by Kinesis Data Firehose4.

1: Resharding - Amazon Kinesis Data Streams

2: Amazon S3 Prefixes - Amazon Kinesis Data Firehose

3: Data Retention - Amazon Kinesis Data Streams

4: Developing Consumers Using the Kinesis Client Library - Amazon Kinesis Data Streams

The XGBoost algorithm is a popular machine learning technique for classification problems. It is based on the idea of boosting, which is to combine many weak learners (decision trees) into a strong learner (ensemble model).

The XGBoost algorithm can handle imbalanced data by using the scale_pos_weight parameter, which controls the balance of positive and negative weights in the objective function. A typical value to consider is the ratio of negative cases to positive cases in the data. By increasing this parameter, the algorithm will pay more attention to the minority class (positive) and reduce the number of false negatives.

The XGBoost algorithm can also use different evaluation metrics to optimize the model performance. The default metric is error, which is the misclassification rate. However, this metric can be misleading for imbalanced data, as it does not account for the different costs of false positives and false negatives. A better metric to use is AUC, which is the area under the receiver operating characteristic (ROC) curve. The ROC curve plots the true positive rate against the false positive rate for different threshold values. The AUC measures how well the model can distinguish between the two classes, regardless of the threshold. By changing the eval_metric parameter to AUC, the algorithm will try to maximize the AUC score and reduce the number of false negatives.

Therefore, the combination of steps that should be taken to reduce the number of false negatives are to increase the scale_pos_weight parameter and change the eval_metric parameter to AUC.

XGBoost Parameters

XGBoost for Imbalanced Classification

Feature selection is the process of reducing the number of input variables to those that are most relevant for predicting the target variable. One way to do this is to run a correlation check of all features against the target variable and remove features with low target variable correlation scores. This means that these features have little or no linear relationship with the target variable and are not useful for the prediction. This can reduce the model’s complexity and improve its performance. References:

Feature engineering - Machine Learning Lens

Feature Selection For Machine Learning in Python

 AWS KMS is a service that provides encryption and key management for data stored in AWS services and applications. AWS KMS can generate and manage encryption keys that are used to encrypt and decrypt data at rest and in transit. AWS KMS can also integrate with other AWS services, such as Amazon S3 and Amazon SageMaker, to enable encryption of data using the keys stored in AWS KMS. Amazon S3 is a service that provides object storage for data in the cloud. Amazon S3 can use AWS KMS to encrypt data at rest using server-side encryption with AWS KMS-managed keys (SSE-KMS). Amazon SageMaker is a service that provides a platform for building, training, and deploying machine learning models. Amazon SageMaker can use AWS KMS to encrypt data at rest on the SageMaker instances and volumes, as well as data in transit between SageMaker and other AWS services. AWS Glue is a service that provides a serverless data integration platform for data preparation and transformation. AWS Glue can use AWS KMS to encrypt data at rest on the Glue Data Catalog and Glue ETL jobs. AWS Glue can also use built-in or custom classifiers to identify and redact sensitive data, such as credit card numbers, from the customer data1234

The other options are not valid or secure ways to encrypt the data and protect the credit card information. Using a custom encryption algorithm to encrypt the data and store the data on an Amazon SageMaker instance in a VPC is not a good practice, as custom encryption algorithms are not recommended for security and may have flaws or vulnerabilities. Using the SageMaker DeepAR algorithm to randomize the credit card numbers is not a good practice, as DeepAR is a forecasting algorithm that is not designed for data anonymization or encryption. Using an IAM policy to encrypt the data on the Amazon S3 bucket and Amazon Kinesis to automatically discard credit card numbers and insert fake credit card numbers is not a good practice, as IAM policies are not meant for data encryption, but for access control and authorization. Amazon Kinesis is a service that provides real-time data streaming and processing, but it does not have the capability to automatically discard or insert data values. Using an Amazon SageMaker launch configuration to encrypt the data once it is copied to the SageMaker instance in a VPC is not a good practice, as launch configurations are not meant for data encryption, but for specifying the instance type, security group, and user data for the SageMaker instance. Using the SageMaker principal component analysis (PCA) algorithm to reduce the length of the credit card numbers is not a good practice, as PCA is a dimensionality reduction algorithm that is not designed for data anonymization or encryption.

For a repository containing a large and dynamically scaling collection of images, Amazon S3 is ideal due to its scalability and versioning capabilities. Amazon S3 natively supports automatic scaling to accommodate increasing storage needs and allows versioning, which enables tracking and managing different versions of objects.

To meet the requirement of maintaining multiple, immediately accessible copies of data across AWS Regions, S3 Cross-Region Replication (CRR) can be enabled. CRR automatically replicates new or updated objects to a specified destination bucket in another AWS Region, ensuring low-latency access and disaster recovery. By setting up CRR with versioning enabled, the data scientist can achieve a multi-Region, scalable, and version-controlled repository in Amazon S3.

 To find the most discussed topics in the comments that are in either English or Spanish, the machine learning specialist needs to perform two steps: first, translate the comments from Spanish to English if necessary, and second, apply a topic modeling algorithm to the comments. The following options are valid ways to accomplish these steps using AWS services:

Option C: Use Amazon Translate to translate from Spanish to English, if necessary. Use Amazon Comprehend topic modeling to find the topics. Amazon Translate is a neural machine translation service that delivers fast, high-quality, and affordable language translation. Amazon Comprehend is a natural language processing (NLP) service that uses machine learning to find insights and relationships in text. Amazon Comprehend topic modeling is a feature that automatically organizes a collection of text documents into topics that contain commonly used words and phrases.

Option E: Use Amazon Translate to translate from Spanish to English, if necessary. Use Amazon SageMaker Neural Topic Model (NTM) to find the topics. Amazon SageMaker is a fully managed service that provides every developer and data scientist with the ability to build, train, and deploy machine learning (ML) models quickly. Amazon SageMaker Neural Topic Model (NTM) is an unsupervised learning algorithm that is used to organize a corpus of documents into topics that contain word groupings based on their statistical distribution.

The other options are not valid because:

Option A: Amazon SageMaker BlazingText algorithm is not a topic modeling algorithm, but a text classification and word embedding algorithm. It cannot find the topics independently from language, as different languages have different word distributions and semantics.

Option B: Amazon SageMaker seq2seq algorithm is not a translation algorithm, but a sequence-to-sequence learning algorithm that can be used for tasks such as summarization, chatbot, and question answering. Amazon SageMaker Latent Dirichlet Allocation (LDA) algorithm is a topic modeling algorithm, but it requires the input documents to be in the same language and preprocessed into a bag-of-words format.

Option D: Amazon Lex is not a topic modeling algorithm, but a service for building conversational interfaces into any application using voice and text. It cannot extract topics from the content, but only intents and slots based on a predefined bot configuration. References:

Amazon Translate

Amazon Comprehend

Amazon SageMaker

Amazon SageMaker Neural Topic Model (NTM) Algorithm

Amazon SageMaker BlazingText

Amazon SageMaker Seq2Seq

Amazon SageMaker Latent Dirichlet Allocation (LDA) Algorithm

Amazon Lex

The elbow method is a technique that we use to determine the number of centroids (k) to use in a k-means clustering algorithm. In this method, we plot the within-cluster sum of squares (WCSS) against the number of clusters (k) and look for the point where the curve bends sharply. This point is called the elbow point and it indicates that adding more clusters does not improve the model significantly. The graph in the question shows that the elbow point is at k = 4, which means that 4 is a reasonable choice for the optimal number of clusters. References:

Elbow Method for optimal value of k in KMeans: A tutorial on how to use the elbow method with Amazon SageMaker.

K-Means Clustering: A video that explains the concept and benefits of k-means clustering.

The company should configure Amazon Textract to route low-confidence predictions to Amazon Augmented AI (Amazon A2I). Amazon A2I is a service that allows you to implement human review of machine learning (ML) predictions. It also comes integrated with some of the Artificial Intelligence (AI) services such as Amazon Textract. By using Amazon A2I, the company can perform a manual review on those words that have low confidence scores before performing a business validation. This will help reduce the processing time of loan applications by avoiding errors and rework.

Option A is incorrect because Amazon SageMaker Ground Truth is not a suitable service for human review of Amazon Textract predictions. Amazon SageMaker Ground Truth is a service that helps you build highly accurate training datasets for machine learning. It allows you to label your own data or use a workforce of human labelers. However, it does not provide an easy way to integrate with Amazon Textract and route low-confidence predictions for human review.

Option B is incorrect because using an Amazon Textract synchronous operation instead of an asynchronous operation will not reduce the processing time of loan applications. A synchronous operation is a request-response operation that returns the results immediately. An asynchronous operation is a start-and-check operation that returns a job identifier that you can use to check the status and results later. The choice of operation depends on the size and complexity of the document, not on the confidence of the predictions.

Option D is incorrect because using Amazon Rekognition’s feature to detect text in an image to extract the data from scanned images is not a better alternative than using Amazon Textract. Amazon Rekognition is a service that provides computer vision capabilities, such as face recognition, object detection, and scene analysis. It can also detect text in an image, but it does not provide the same level of accuracy and functionality as Amazon Textract. Amazon Textract can not only detect text, but also extract data from tables and forms, and understand the layout and structure of the document.

Amazon Augmented AI

Amazon SageMaker Ground Truth

Amazon Textract Operations

Amazon Rekognition

The solution D is the best data visualization approach to determine the optimal value of k for the k-means clustering algorithm. The solution D involves the following steps:

Run the k-means clustering algorithm for a range of k. For each value of k, calculate the sum of squared errors (SSE). The SSE is a measure of how well the clusters fit the data. It is calculated by summing the squared distances of each data point to its closest cluster center. A lower SSE indicates a better fit, but it will always decrease as the number of clusters increases. Therefore, the goal is to find the smallest value of k that still has a low SSE1.

Plot a line chart of the SSE for each value of k. The line chart will show how the SSE changes as the value of k increases. Typically, the line chart will have a shape of an elbow, where the SSE drops rapidly at first and then levels off. The optimal value of k is the point after which the curve starts decreasing in a linear fashion. This point is also known as the elbow point, and it represents the balance between the number of clusters and the SSE1.

The other options are not suitable because:

Option A: Calculating the principal component analysis (PCA) components, running the k-means clustering algorithm for a range of k by using only the first two PCA components, and creating a scatter plot with a different color for each cluster will not accurately determine the optimal value of k. PCA is a technique that reduces the dimensionality of the data by transforming it into a new set of features that capture the most variance in the data. However, PCA may not preserve the original structure and distances of the data, and it may lose some information in the process. Therefore, running the k-means clustering algorithm on the PCA components may not reflect the true clusters in the data. Moreover, using only the first two PCA components may not capture enough variance to represent the data well. Furthermore, creating a scatter plot may not be reliable, as it depends on the subjective judgment of the data scientist to decide when the clusters look reasonably separated2.

Option B: Calculating the PCA components and creating a line plot of the number of components against the explained variance will not determine the optimal value of k. This approach is used to determine the optimal number of PCA components to use for dimensionality reduction, not for clustering. The explained variance is the ratio of the variance of each PCA component to the total variance of the data. The optimal number of PCA components is the point where adding more components does not significantly increase the explained variance. However, this number may not correspond to the optimal number of clusters, as PCA and k-means clustering have different objectives and assumptions2.

Option C: Creating a t-distributed stochastic neighbor embedding (t-SNE) plot for a range of perplexity values will not determine the optimal value of k. t-SNE is a technique that reduces the dimensionality of the data by embedding it into a lower-dimensional space, such as a two-dimensional plane. t-SNE preserves the local structure and distances of the data, and it can reveal clusters and patterns in the data. However, t-SNE does not assign labels or centroids to the clusters, and it does not provide a measure of how well the clusters fit the data. Therefore, t-SNE cannot determine the optimal number of clusters, as it only visualizes the data. Moreover, t-SNE depends on the perplexity parameter, which is a measure of how many neighbors each point considers. The perplexity parameter can affect the shape and size of the clusters, and there is no optimal value for it. Therefore, creating a t-SNE plot for a range of perplexity values may not be consistent or reliable3.

1: How to Determine the Optimal K for K-Means?

2: Principal Component Analysis

3: t-Distributed Stochastic Neighbor Embedding

The best approaches to decrease the training time of the model are C and D, because they can improve the computational efficiency and parallelization of the training process. These approaches have the following benefits:

C: Replacing CPU-based EC2 instances with GPU-based EC2 instances can speed up the training of the DeepAR algorithm, as it can leverage the parallel processing power of GPUs to perform matrix operations and gradient computations faster than CPUs12. The DeepAR algorithm supports GPU-based EC2 instances such as ml.p2 and ml.p33.

D: Using multiple training instances can also reduce the training time of the DeepAR algorithm, as it can distribute the workload across multiple nodes and perform data parallelism4. The DeepAR algorithm supports distributed training with multiple CPU-based or GPU-based EC2 instances3.

The other options are not effective or relevant, because they have the following drawbacks:

A: Replacing On-Demand Instances with Spot Instances can reduce the cost of the training, but not necessarily the time, as Spot Instances are subject to interruption and availability5. Moreover, the DeepAR algorithm does not support checkpointing, which means that the training cannot resume from the last saved state if the Spot Instance is terminated3.

B: Configuring model auto scaling dynamically to adjust the number of instances automatically is not applicable, as this feature is only available for inference endpoints, not for training jobs6.

E: Using a pre-trained version of the model and running incremental training is not possible, as the DeepAR algorithm does not support incremental training or transfer learning3. The DeepAR algorithm requires a full retraining of the model whenever new data is added or the hyperparameters are changed7.

1: GPU vs CPU: What Matters Most for Machine Learning? | by Louis (What’s AI) Bouchard | Towards Data Science

2: How GPUs Accelerate Machine Learning Training | NVIDIA Developer Blog

3: DeepAR Forecasting Algorithm - Amazon SageMaker

4: Distributed Training - Amazon SageMaker

5: Managed Spot Training - Amazon SageMaker

6: Automatic Scaling - Amazon SageMaker

7: How the DeepAR Algorithm Works - Amazon SageMaker

The company wants to predict the demand for a new product that will soon be launched, based on the sales history of similar products. This is a time series forecasting problem, which requires a machine learning algorithm that can learn from historical data and generate future predictions.

One of the most suitable solutions for this problem is to use the Amazon SageMaker DeepAR algorithm, which is a supervised learning algorithm for forecasting scalar time series using recurrent neural networks (RNN). DeepAR can handle multiple related time series, such as the sales of different products, and learn a global model that captures the common patterns and trends across the time series. DeepAR can also generate probabilistic forecasts that provide confidence intervals and quantify the uncertainty of the predictions.

DeepAR can outperform traditional forecasting methods, such as ARIMA, especially when the dataset contains hundreds or thousands of related time series. DeepAR can also use the trained model to forecast the demand for new products that are similar to the ones it has been trained on, by using the categorical features that encode the product attributes. For example, the company can use the product type, brand, flavor, size, and price as categorical features to group the products and learn the typical behavior for each group.

Therefore, the Machine Learning Specialist should apply the Amazon SageMaker DeepAR algorithm to forecast the demand for the new product, by using the sales history of the existing products as the training dataset, and the product attributes as the categorical features.

DeepAR Forecasting Algorithm - Amazon SageMaker

Now available in Amazon SageMaker: DeepAR algorithm for more accurate time series forecasting

To count the number of students who are in a class, the solution needs to detect and locate each student in the video frame. Object detection is a computer vision model that can identify and locate multiple objects in an image. To differentiate students who are performing a stretch correctly from students who are performing a stretch incorrectly, the solution needs to measure the location and angle of each student’s arms and legs. Pose estimation is a computer vision model that can estimate the pose of a person by detecting the position and orientation of key body parts. Image classification, OCR, and image GANs are not relevant for this use case. References:

Object Detection: A computer vision technique that identifies and locates objects within an image or video.

Pose Estimation: A computer vision technique that estimates the pose of a person by detecting the position and orientation of key body parts.

Amazon SageMaker: A fully managed service that provides every developer and data scientist with the ability to build, train, and deploy machine learning (ML) models quickly.

The Recordio protobuf format is a binary data format that is optimized for training on SageMaker. It allows faster data loading and lower memory usage compared to other formats such as CSV or numpy arrays. The Recordio protobuf format also supports features such as sparse input, variable-length input, and label embedding. To use the Recordio protobuf format, the data needs to be serialized and deserialized using the appropriate libraries. Some of the built-in algorithms in SageMaker support the Recordio protobuf format as a content type for training and inference. References:

Common Data Formats for Training

Using RecordIO Format

Content Types Supported by Built-in Algorithms

 Area Under the ROC Curve (AUC) is a metric that measures the performance of a binary classifier across all possible thresholds. It is also known as the probability that a randomly chosen positive example will be ranked higher than a randomly chosen negative example by the classifier. AUC is a good metric to compare different classification models because it is independent of the class distribution and the decision threshold. It also captures both the sensitivity (true positive rate) and the specificity (true negative rate) of the model. References:

AWS Machine Learning Specialty Exam Guide

AWS Machine Learning Specialty Sample Questions

 The solution A will meet the requirements with the most operational efficiency because it uses Amazon SageMaker Data Wrangler, which is a service that simplifies the process of data preparation and feature engineering for machine learning. The solution A involves the following steps:

Use Amazon SageMaker Data Wrangler preconfigured transformations to explore feature transformations. Amazon SageMaker Data Wrangler provides a visual interface that allows data scientists to apply various transformations to their tabular data, such as encoding categorical features, scaling numerical features, imputing missing values, and more. Amazon SageMaker Data Wrangler also supports custom transformations using Python code or SQL queries1.

Use SageMaker Data Wrangler templates for visualization. Amazon SageMaker Data Wrangler also provides a set of templates that can generate visualizations of the data, such as histograms, scatter plots, box plots, and more. These visualizations can help data scientists to understand the distribution and characteristics of the data, and to compare the effects of different feature transformations1.

Export the feature processing workflow to a SageMaker pipeline for automation. Amazon SageMaker Data Wrangler can export the feature processing workflow as a SageMaker pipeline, which is a service that orchestrates and automates machine learning workflows. A SageMaker pipeline can run the feature processing steps as a preprocessing step, and then feed the output to a training step or an inference step. This can reduce the operational overhead of managing the feature processing workflow and ensure its consistency and reproducibility2.

The other options are not suitable because:

Option B: Using an Amazon SageMaker notebook instance to experiment with different feature transformations, saving the transformations to Amazon S3, using Amazon QuickSight for visualization, and packaging the feature processing steps into an AWS Lambda function for automation will incur more operational overhead than using Amazon SageMaker Data Wrangler. The data scientist will have to write the code for the feature transformations, the data storage, the data visualization, and the Lambda function. Moreover, AWS Lambda has limitations on the execution time, memory size, and package size, which may not be sufficient for complex feature processing tasks3.

Option C: Using AWS Glue Studio with custom code to experiment with different feature transformations, saving the transformations to Amazon S3, using Amazon QuickSight for visualization, and packaging the feature processing steps into an AWS Lambda function for automation will incur more operational overhead than using Amazon SageMaker Data Wrangler. AWS Glue Studio is a visual interface that allows data engineers to create and run extract, transform, and load (ETL) jobs on AWS Glue. However, AWS Glue Studio does not provide preconfigured transformations or templates for feature engineering or data visualization. The data scientist will have to write custom code for these tasks, as well as for the Lambda function. Moreover, AWS Glue Studio is not integrated with SageMaker pipelines, and it may not be optimized for machine learning workflows4.

Option D: Using Amazon SageMaker Data Wrangler preconfigured transformations to experiment with different feature transformations, saving the transformations to Amazon S3, using Amazon QuickSight for visualization, packaging each feature transformation step into a separate AWS Lambda function, and using AWS Step Functions for workflow automation will incur more operational overhead than using Amazon SageMaker Data Wrangler. The data scientist will have to create and manage multiple AWS Lambda functions and AWS Step Functions, which can increase the complexity and cost of the solution. Moreover, AWS Lambda and AWS Step Functions may not be compatible with SageMaker pipelines, and they may not be optimized for machine learning workflows5.

1: Amazon SageMaker Data Wrangler

2: Amazon SageMaker Pipelines

3: AWS Lambda

4: AWS Glue Studio

5: AWS Step Functions

 The problem described in the question is a case of overfitting, where the neural network model performs well on the training data but poorly on the test data. This means that the model has learned the noise and specific patterns of the training data, but cannot generalize to new and unseen data. To solve this issue, the Specialist should consider the following changes:

Choose a lower number of layers: Reducing the number of layers can reduce the complexity and capacity of the neural network model, making it less prone to overfitting. A model with 50 hidden layers is likely too deep for the given data size and task. A simpler model with fewer layers can learn the essential features of the data without memorizing the noise.

Enable dropout: Dropout is a regularization technique that randomly drops out some units in the neural network during training. This prevents the units from co-adapting too much and forces the model to learn more robust features. Dropout can improve the generalization and test performance of the model by reducing overfitting.

Enable early stopping: Early stopping is another regularization technique that monitors the validation error during training and stops the training process when the validation error stops decreasing or starts increasing. This prevents the model from overtraining on the training data and reduces overfitting.

Deep Learning - Machine Learning Lens

How to Avoid Overfitting in Deep Learning Neural Networks

How to Identify Overfitting Machine Learning Models in Scikit-Learn

The correct solution for granting permissions for data preprocessing is to use the following steps:

Create an IAM role that has permissions to create Amazon SageMaker Processing jobs. Attach the role to the SageMaker notebook instance. This role allows the ML specialist to run Processing jobs from the notebook code1

Create an Amazon SageMaker Processing job with an IAM role that has read and write permissions to the relevant S3 bucket, and appropriate KMS and ECR permissions. This role allows the Processing job to access the data in the encrypted S3 bucket, decrypt it with the KMS CMK, and pull the container image from ECR23

The other options are incorrect because they either miss some permissions or use unnecessary steps. For example:

Option A uses a single IAM role for both the notebook instance and the Processing job. This role may have more permissions than necessary for the notebook instance, which violates the principle of least privilege4

Option C sets up both an S3 endpoint and a KMS endpoint in the default VPC. These endpoints are not required for the Processing job to access the data in the encrypted S3 bucket. They are only needed if the Processing job runs in network isolation mode, which is not specified in the question.

Option D uses the access key and secret key of the IAM user with appropriate KMS and ECR permissions. This is not a secure way to pass credentials to the Processing job. It also requires the ML specialist to manage the IAM user and the keys.

1: Create an Amazon SageMaker Notebook Instance - Amazon SageMaker

2: Create a Processing Job - Amazon SageMaker

3: Use AWS KMS–Managed Encryption Keys - Amazon Simple Storage Service

4: IAM Best Practices - AWS Identity and Access Management

Network Isolation - Amazon SageMaker

Understanding and Getting Your Security Credentials - AWS General Reference

Amazon Personalize is a fully managed machine learning service that makes it easy for developers to create personalized user experiences at scale. It uses the same recommender system technology that Amazon uses to create its own personalized recommendations. Amazon Personalize provides several pre-built recipes that can be used to train models for different use cases. The USER_PERSONALIZATION recipe is designed to provide personalized recommendations for existing users based on their past interactions with items. The PERSONALIZED_RANKING recipe is designed to re-rank a list of items for a user based on their preferences. The USER_PERSONALIZATION recipe is more suitable for this use case because it can generate recommendations for each user without requiring a list of candidate items. To filter out the items that the user previously purchased, a real-time filter can be created and applied to the campaign. A real-time filter is a dynamic filter that uses the latest interaction data to exclude items from the recommendations. By using Amazon Personalize, the development effort is minimized because it handles the data processing, model training, and deployment automatically. The web application can use the GetRecommendations API operation to get the real-time recommendations from the campaign. References:

Amazon Personalize

What is Amazon Personalize?

USER_PERSONALIZATION recipe

PERSONALIZED_RANKING recipe

Filtering recommendations

GetRecommendations API operation

Amazon Kinesis Data Analytics is a service that allows users to analyze streaming data in real time using SQL queries. Amazon Random Cut Forest (RCF) is a SQL extension that enables anomaly detection on streaming data. RCF is an unsupervised machine learning algorithm that assigns an anomaly score to each data point based on how different it is from the rest of the data. A sliding window is a type of window that moves along with the data stream, so that the anomaly detection model can adapt to changing patterns over time. A tumbling window is a type of window that has a fixed size and does not overlap with other windows, so that the anomaly detection model is based on a fixed period of time. Therefore, option D is the best approach to meet the requirements of the question, as it uses RCF to calculate anomaly scores for each web traffic entry and uses a sliding window to adapt to changing web patterns over time.

Option A is incorrect because Amazon SageMaker Random Cut Forest (RCF) is a built-in model that can be used to train and deploy anomaly detection models on batch or streaming data, but it requires more steps and resources than using the RCF SQL extension in Amazon Kinesis Data Analytics. Option B is incorrect because Amazon SageMaker XGBoost is a built-in model that can be used for supervised learning tasks such as classification and regression, but not for unsupervised learning tasks such as anomaly detection. Option C is incorrect because k-Nearest Neighbors (kNN) is a SQL extension that can be used for classification and regression tasks on streaming data, but not for anomaly detection. Moreover, using a tumbling window would not allow the anomaly detection model to adapt to changing web patterns over time.

Using CloudWatch anomaly detection

Anomaly Detection With CloudWatch

Performing Real-time Anomaly Detection using AWS

What Is AWS Anomaly Detection? (And Is There A Better Option?)

 Based on the figure provided, a decision tree would have the highest recall with respect to the fraudulent class. Recall is a model evaluation metric that measures the proportion of actual positive instances that are correctly classified by the model. Recall is calculated as follows:

Recall = True Positives / (True Positives + False Negatives)

A decision tree is a type of machine learning model that can perform classification tasks by splitting the data into smaller and purer subsets based on a series of rules or conditions. A decision tree can handle both linear and non-linear data, and can capture complex patterns and interactions among the features. A decision tree can also be easily visualized and interpreted1

In this case, the data is not linearly separable, and has a clear pattern of seasonality. The fraudulent class forms a large circle in the center of the plot, while the normal class is scattered around the edges. A decision tree can use the transaction month and the age of account as the splitting criteria, and create a circular boundary that separates the fraudulent class from the normal class. A decision tree can achieve a high recall for the fraudulent class, as it can correctly identify most of the black dots as positive instances, and minimize the number of false negatives. A decision tree can also adjust the depth and complexity of the tree to balance the trade-off between recall and precision23

The other options are not valid or suitable for achieving a high recall for the fraudulent class. A linear support vector machine (SVM) is a type of machine learning model that can perform classification tasks by finding a linear hyperplane that maximizes the margin between the classes. A linear SVM can handle linearly separable data, but not non-linear data. A linear SVM cannot capture the circular pattern of the fraudulent class, and may misclassify many of the black dots as negative instances, resulting in a low recall4 A naive Bayesian classifier is a type of machine learning model that can perform classification tasks by applying the Bayes’ theorem and assuming conditional independence among the features. A naive Bayesian classifier can handle both linear and non-linear data, and can incorporate prior knowledge and probabilities into the model. However, a naive Bayesian classifier may not perform well when the features are correlated or dependent, as in this case. A naive Bayesian classifier may not capture the circular pattern of the fraudulent class, and may misclassify many of the black dots as negative instances, resulting in a low recall5 A single perceptron with sigmoidal activation function is a type of machine learning model that can perform classification tasks by applying a weighted linear combination of the features and a non-linear activation function. A single perceptron with sigmoidal activation function can handle linearly separable data, but not non-linear data. A single perceptron with sigmoidal activation function cannot capture the circular pattern of the fraudulent class, and may misclassify many of the black dots as negative instances, resulting in a low recall.

The best way to maintain the privacy and integrity of the data stored in Amazon S3 is to use a combination of VPC endpoints and S3 bucket policies. A VPC endpoint allows the SageMaker notebook instance to access the S3 bucket without going through the public internet. A bucket policy allows the S3 bucket owner to specify which VPCs or VPC endpoints can access the bucket. This way, the data is protected from unauthorized access and tampering. The other options are either insecure (A and D) or inefficient (B). References: Using Amazon S3 VPC Endpoints, Using Bucket Policies and User Policies

 The best way to split the dataset into a training and test set for this use case is to randomly select 10% of the users and split off all interaction data from these users for the test set. This is because the company relies on a steady stream of new customers, so the test set should reflect the behavior of new customers who have not been seen by the model before. The other options are not suitable because they either mix old and new customers in the test set (A and B), or they bias the test set towards users with less interaction data ©. References:

Amazon SageMaker Developer Guide: Train and Test Datasets

Amazon Personalize Developer Guide: Preparing and Importing Data

The SageMaker Spark library is a library that enables Apache Spark applications to integrate with Amazon SageMaker for training and hosting machine learning models. The library provides several features, such as:

Estimators: Classes that allow Spark users to train Amazon SageMaker models and host them on Amazon SageMaker endpoints using the Spark MLlib Pipelines API. The library supports various built-in algorithms, such as linear learner, XGBoost, K-means, etc., as well as custom algorithms using Docker containers.

Model classes: Classes that wrap Amazon SageMaker models in a Spark MLlib Model abstraction. This allows Spark users to use Amazon SageMaker endpoints for inference within Spark applications.

Data sources: Classes that allow Spark users to read data from Amazon S3 using the Spark Data Sources API. The library supports various data formats, such as CSV, LibSVM, RecordIO, etc.

To integrate the Spark application with SageMaker, the Machine Learning Specialist should do the following:

Install the SageMaker Spark library in the Spark environment. This can be done by using Maven, pip, or downloading the JAR file from GitHub.

Use the appropriate estimator from the SageMaker Spark Library to train a model. For example, to train a linear learner model, the Specialist can use the following code:

Use the sageMakerModel. transform method to get inferences from the model hosted in SageMaker. For example, to get predictions for a test DataFrame, the Specialist can use the following code:

[SageMaker Spark]: A documentation page that introduces the SageMaker Spark library and its features.

[SageMaker Spark GitHub Repository]: A GitHub repository that contains the source code, examples, and installation instructions for the SageMaker Spark library.

 The IP Insights algorithm is designed to capture associations between entities and IP addresses, and can be used to identify anomalous IP usage patterns. The algorithm can learn from historical data that contains pairs of entities and IP addresses, and can return a score that indicates how likely the pair is to occur. The company can use this algorithm to train a model that can detect when a customer is accessing the site from a different location than usual, and request additional information accordingly. The company can also schedule updates and retraining of the model using new log data nightly to keep the model up to date with the latest IP usage patterns.

The other options are not suitable for this use case because:

Option A: The factorization machines (FM) algorithm is a general-purpose supervised learning algorithm that can be used for both classification and regression tasks. However, it is not optimized for capturing associations between entities and IP addresses, and would require labeling each record as either a successful or failed access attempt, which is a costly and time-consuming process.

Option C: The IP Insights algorithm is a good choice for this use case, but it does not require labeling each record as either a successful or failed access attempt. The algorithm is unsupervised and can learn from the historical data without labels. Labeling the data would be unnecessary and wasteful.

Option D: The Object2Vec algorithm is a general-purpose neural embedding algorithm that can learn low-dimensional dense embeddings of high-dimensional objects. However, it is not designed to capture associations between entities and IP addresses, and would require a different input format than the one provided by the company. The Object2Vec algorithm expects pairs of objects and their relationship labels or scores as inputs, while the company has data containing the source IP addresses and the login names of the requesting users.

IP Insights - Amazon SageMaker

Factorization Machines Algorithm - Amazon SageMaker

Object2Vec Algorithm - Amazon SageMaker

Amazon SageMaker supports encryption at rest for the ML storage volumes, the model artifacts, and the data in Amazon S3 using AWS Key Management Service (AWS KMS). AWS KMS is a service that allows customers to create and manage encryption keys that can be used to encrypt data. AWS KMS also provides an audit trail of key usage by logging key events to AWS CloudTrail. Customers can use either AWS managed keys or customer managed keys to encrypt their data. AWS managed keys are created and managed by AWS on behalf of the customer, while customer managed keys are created and managed by the customer. Customer managed keys offer more control and flexibility over the key policies, permissions, and rotation. Therefore, to meet the requirements of the company, the best option is to use customer managed keys in AWS KMS to encrypt the ML data volumes, and to encrypt the model artifacts and data in Amazon S3.

The other options are not correct because:

Option A: AWS Cloud HSM is a service that provides hardware security modules (HSMs) to store and use encryption keys. AWS Cloud HSM is not integrated with Amazon SageMaker, and cannot be used to encrypt the ML data volumes, the model artifacts, or the data in Amazon S3. AWS Cloud HSM is more suitable for customers who need to meet strict compliance requirements or who need direct control over the HSMs.

Option B: SageMaker built-in transient keys are temporary keys that are used to encrypt the ML data volumes and are discarded immediately after encryption. These keys do not provide persistent encryption or logging of key usage. Enabling default encryption for new Amazon Elastic Block Store (Amazon EBS) volumes does not affect the ML data volumes, which are encrypted separately by SageMaker. Moreover, this option does not address the encryption of the model artifacts and data in Amazon S3.

Option D: AWS Security Token Service (AWS STS) is a service that provides temporary credentials to access AWS resources. AWS STS does not provide encryption keys or encryption services. AWS STS cannot be used to encrypt the ML storage volumes, the model artifacts, or the data in Amazon S3.

Protect Data at Rest Using Encryption - Amazon SageMaker

What is AWS Key Management Service? - AWS Key Management Service

What is AWS CloudHSM? - AWS CloudHSM

What is AWS Security Token Service? - AWS Security Token Service

 Pushing the data from Microsoft SQL Server to Amazon S3 using an AWS Data Pipeline and providing the S3 location within the notebook is the best approach for training a model using the data stored in Amazon RDS. This is because Amazon SageMaker can directly access data from Amazon S3 and train models on it. AWS Data Pipeline is a service that can automate the movement and transformation of data between different AWS services. It can also use Amazon RDS as a data source and Amazon S3 as a data destination. This way, the data can be transferred efficiently and securely without writing any code within the notebook. References:

Amazon SageMaker

AWS Data Pipeline

The best option is to use AWS Database Migration Service (AWS DMS) with table mapping to select PostgreSQL tables with no sensitive data through an SSL connection. Replicate data directly into Amazon S3. This option meets the following requirements:

It ensures that only nonsensitive data is transferred to the cloud by using table mapping to filter out the tables that contain sensitive data1.

It uses IPsec to secure the data transfer by enabling SSL encryption for the AWS DMS endpoint2.

It uploads the data to Amazon S3 each day for model retraining by using the ongoing replication feature of AWS DMS3.

The other options are not as effective or feasible as the option above. Creating an AWS Glue job to connect to the PostgreSQL DB instance and ingest data through an AWS Site-to-Site VPN connection directly into Amazon S3 is possible, but it requires more steps and resources than using AWS DMS. Also, it does not specify how to filter out the sensitive data from the tables. Creating an AWS Glue job to connect to the PostgreSQL DB instance and ingest all data through an AWS Site-to-Site VPN connection into Amazon S3 while removing sensitive data using a PySpark job is also possible, but it is more complex and error-prone than using AWS DMS. Also, it does not use IPsec as required. Using PostgreSQL logical replication to replicate all data to PostgreSQL in Amazon EC2 through AWS Direct Connect with a VPN connection, and then using AWS Glue to move data from Amazon EC2 to Amazon S3 is not feasible, because PostgreSQL logical replication does not support replicating only a subset of data4. Also, it involves unnecessary data movement and additional costs.

Table mapping - AWS Database Migration Service

Using SSL to encrypt a connection to a DB instance - AWS Database Migration Service

Ongoing replication - AWS Database Migration Service

Logical replication - PostgreSQL

The solution that uses Amazon Comprehend and Amazon CloudWatch is the most suitable for the given scenario. Amazon Comprehend is a natural language processing (NLP) service that can analyze text and extract insights such as sentiment, entities, topics, and syntax. Amazon CloudWatch is a monitoring and observability service that can collect and track metrics, create dashboards, and set alarms based on various thresholds. By using these services, the data science team can quickly and easily implement a solution to monitor the sentiment of social media posts without requiring much infrastructure or data science resources. The solution also meets the requirements of storing the sentiment in both S3 and CloudWatch, and using CloudWatch alarms to notify analysts of trends.

Amazon Comprehend

Amazon CloudWatch

The problem that the Machine Learning Specialist is facing is likely due to concept drift, which is a phenomenon where the statistical properties of the target variable change over time, making the model less accurate and relevant. Concept drift can occur due to various reasons, such as changes in customer preferences, market trends, product inventory, seasonality, etc. In this case, the product recommendations model may have become outdated as the product inventory changed over time, making the recommendations less appealing to the customers. To address this issue, the model should be periodically retrained using the original training data plus new data as product inventory changes. This way, the model can learn from the latest data and adapt to the changing customer behavior and preferences. Retraining the model from scratch using the original data while adding a regularization term may not be sufficient, as it does not account for the new data. Updating the model’s hyperparameters may not help either, as it does not address the underlying data distribution change. Re-engineering the model completely may not be necessary, as the model may still be valid and useful with periodic retraining.

Concept Drift - Amazon SageMaker

Detecting and Handling Concept Drift - Amazon SageMaker

Machine Learning Concepts - Amazon Machine Learning

The FindMatches transform is a machine learning transform that can identify and match similar records from different datasets, even when the records do not have a common unique identifier or exact field values. The FindMatches transform can also remove duplicate records from a single dataset. The FindMatches transform can be used with AWS Glue crawlers and jobs to process the data from various sources and store it in a data lake. The FindMatches transform can be created and managed using the AWS Glue console, API, or AWS Glue Studio.

The other options are not suitable for this use case because:

Option A: Using an AWS Lambda function to process the data and compare equal strings in the fields from the two datasets is not an efficient or scalable solution. It would require writing custom code and handling the data loading and cleansing logic. It would also not account for variations or inconsistencies in the field values, such as spelling errors, abbreviations, or missing data.

Option B: The AWS Glue SearchTables API operation is used to search for tables in the AWS Glue Data Catalog based on a set of criteria. It is not a machine learning transform that can match records across different datasets or remove duplicates. It would also require writing custom code to invoke the API and process the results.

Option D: AWS Lake Formation does not provide a custom transform feature. It provides predefined blueprints for common data ingestion scenarios, such as database snapshot, incremental database, and log file. These blueprints do not support matching records across different datasets or removing duplicates.

To create a serverless ingestion and analytics solution for high-velocity, real-time streaming data, the Data Scientist should use the following AWS services:

AWS Glue Data Catalog: This is a managed service that acts as a central metadata repository for data assets across AWS and on-premises data sources. The Data Scientist can use AWS Glue Data Catalog to create a schema of the incoming data format, which defines the structure, format, and data types of the JSON records. The schema can be used by other AWS services to understand and process the data1.

Amazon Kinesis Data Firehose: This is a fully managed service that delivers real-time streaming data to destinations such as Amazon S3, Amazon Redshift, Amazon Elasticsearch Service, and Splunk. The Data Scientist can use Amazon Kinesis Data Firehose to stream the data from the source and transform the data to a query-optimized, columnar format such as Apache Parquet or ORC using the AWS Glue Data Catalog before delivering to Amazon S3. This enables efficient compression, partitioning, and fast analytics on the data2.

Amazon S3: This is an object storage service that offers high durability, availability, and scalability. The Data Scientist can use Amazon S3 as the output datastore for the transformed data, which can be organized into buckets and prefixes according to the desired partitioning scheme. Amazon S3 also integrates with other AWS services such as Amazon Athena, Amazon EMR, and Amazon Redshift Spectrum for analytics3.

Amazon Athena: This is a serverless interactive query service that allows users to analyze data in Amazon S3 using standard SQL. The Data Scientist can use Amazon Athena to run SQL queries against the data in Amazon S3 and connect to existing business intelligence dashboards using the Athena Java Database Connectivity (JDBC) connector. Amazon Athena leverages the AWS Glue Data Catalog to access the schema information and supports formats such as Parquet and ORC for fast and cost-effective queries4.

1: What Is the AWS Glue Data Catalog? - AWS Glue

2: What Is Amazon Kinesis Data Firehose? - Amazon Kinesis Data Firehose

3: What Is Amazon S3? - Amazon Simple Storage Service

4: What Is Amazon Athena? - Amazon Athena

The solution C meets the requirements because it minimizes costs for compute infrastructure, maximizes the scalability of resources for training, and facilitates the use of an ML model in low-connectivity environments. The solution C involves the following steps:

Move the training data to an Amazon S3 bucket. This will enable the company to store the large amount of data in a durable, scalable, and cost-effective way. It will also allow the company to access the data from the cloud for training and evaluation purposes1.

Train and evaluate the model by using Amazon SageMaker. This will enable the company to use a fully managed service that provides various features and tools for building, training, tuning, and deploying ML models. Amazon SageMaker can handle large-scale data processing and distributed training, and it can leverage the power of AWS compute resources such as Amazon EC2, Amazon EKS, and AWS Fargate2.

Optimize the model by using SageMaker Neo. This will enable the company to reduce the size of the model and improve its performance and efficiency. SageMaker Neo can compile the model into an executable that can run on various hardware platforms, such as CPUs, GPUs, and edge devices3.

Set up an edge device in the manufacturing facilities with AWS IoT Greengrass. This will enable the company to deploy the model on a local device that can run inference in real time, even in low-connectivity environments. AWS IoT Greengrass can extend AWS cloud capabilities to the edge, and it can securely communicate with the cloud for updates and synchronization4.

Deploy the model on the edge device. This will enable the company to automate quality control in its facilities by using the model to detect defects in new parts as they move on a conveyor belt. The model can run inference locally on the edge device without requiring internet connectivity, and it can send the results to the cloud when the connection is available4.

The other options are not suitable because:

Option A: Deploying the model on a SageMaker hosting services endpoint will not facilitate the use of the model in low-connectivity environments, as it will require internet access to perform inference. Moreover, it may incur higher costs for hosting and data transfer than deploying the model on an edge device.

Option B: Training and evaluating the model on premises will not minimize costs for compute infrastructure, as it will require the company to maintain and upgrade its own hardware and software. Moreover, it will not maximize the scalability of resources for training, as it will limit the company’s ability to leverage the cloud’s elasticity and flexibility.

Option D: Training the model on premises will not minimize costs for compute infrastructure, nor maximize the scalability of resources for training, for the same reasons as option B.

1: Amazon S3

2: Amazon SageMaker

3: SageMaker Neo

4: AWS IoT Greengrass

 Amazon Rekognition, Amazon Comprehend, and Amazon Transcribe are AWS machine learning services that can analyze and extract metadata from images, text, audio, and video content. These services are easy to use, scalable, and do not require any machine learning expertise. They can help the media company to quickly index its assets and enable rapid identification of relevant content by the research team. Using these services is the fastest route to index the assets, compared to the other options that involve human intervention, custom model development, or additional steps. References:

AWS Media Intelligence Solutions

AWS Machine Learning Services

The Best Services For Running Machine Learning Models On AWS

The combination of steps that will meet the requirements are to create an IAM role in the development account that the integration account and production account can assume, attach IAM policies to the role that allow access to the feature repository and the S3 buckets, and share the feature repository that is associated with the S3 buckets from the development account to the integration account and the production account by using AWS Resource Access Manager (AWS RAM). This approach will enable cross-account access and sharing of the features stored in Amazon SageMaker Feature Store and Amazon S3.

Amazon SageMaker Feature Store is a fully managed, purpose-built repository to store, update, search, and share curated data used in training and prediction workflows. The service provides feature management capabilities such as enabling easy feature reuse, low latency serving, time travel, and ensuring consistency between features used in training and inference workflows. A feature group is a logical grouping of ML features whose organization and structure is defined by a feature group schema. A feature group schema consists of a list of feature definitions, each of which specifies the name, type, and metadata of a feature. Amazon SageMaker Feature Store stores the features in both an online store and an offline store. The online store is a low-latency, high-throughput store that is optimized for real-time inference. The offline store is a historical store that is backed by an Amazon S3 bucket and is optimized for batch processing and model training1.

AWS Identity and Access Management (IAM) is a web service that helps you securely control access to AWS resources for your users. You use IAM to control who can use your AWS resources (authentication) and what resources they can use and in what ways (authorization). An IAM role is an IAM identity that you can create in your account that has specific permissions. You can use an IAM role to delegate access to users, applications, or services that don’t normally have access to your AWS resources. For example, you can create an IAM role in your development account that allows the integration account and the production account to assume the role and access the resources in the development account. You can attach IAM policies to the role that specify the permissions for the feature repository and the S3 buckets. You can also use IAM conditions to restrict the access based on the source account, IP address, or other factors2.

AWS Resource Access Manager (AWS RAM) is a service that enables you to easily and securely share AWS resources with any AWS account or within your AWS Organization. You can share AWS resources that you own with other accounts using resource shares. A resource share is an entity that defines the resources that you want to share, and the principals that you want to share with. For example, you can share the feature repository that is associated with the S3 buckets from the development account to the integration account and the production account by creating a resource share in AWS RAM. You can specify the feature group ARN and the S3 bucket ARN as the resources, and the integration account ID and the production account ID as the principals. You can also use IAM policies to further control the access to the shared resources3.

The other options are either incorrect or unnecessary. Using AWS Security Token Service (AWS STS) from the integration account and the production account to retrieve credentials for the development account is not required, as the IAM role in the development account can provide temporary security credentials for the cross-account access. Setting up S3 replication between the development S3 buckets and the integration and production S3 buckets would introduce redundancy and inconsistency, as the S3 buckets are already shared through AWS RAM. Creating an AWS PrivateLink endpoint in the development account for SageMaker is not relevant, as it is used to securely connect to SageMaker services from a VPC, not from another account.

1: Amazon SageMaker Feature Store – Amazon Web Services

2: What Is IAM? - AWS Identity and Access Management

3: What Is AWS Resource Access Manager? - AWS Resource Access Manager

DeepAR is a supervised algorithm that can leverage historical data from related time series to predict demand for new items, unlike ARIMA which requires sufficient historical data for the new item.

From AWS documentation:

“The DeepAR algorithm is a supervised learning algorithm for forecasting scalar (one-dimensional) time series. It can also forecast demand for new items by learning patterns from similar time series.”

— AWS SageMaker DeepAR documentation

The true class frequency for Romance is the percentage of movies that are actually Romance out of all the movies. This can be calculated by dividing the sum of the true values for Romance by the total number of movies. The predicted class frequency for Adventure is the percentage of movies that are predicted to be Adventure out of all the movies. This can be calculated by dividing the sum of the predicted values for Adventure by the total number of movies. Based on the confusion matrix, the true class frequency for Romance is 57.92% and the predicted class frequency for Adventure is 13.12%. References: Confusion Matrix, Classification Metrics

Amazon Kinesis Data Analytics is a service that enables you to analyze streaming data in real time using SQL or Apache Flink applications. You can use Kinesis Data Analytics to process and gain insights from data streams such as web logs, clickstreams, IoT data, and more.

To use SQL to query a data stream of GZIP files, you need to first transform the data into a format that Kinesis Data Analytics can understand, such as JSON, CSV, or Apache Parquet. You can use an AWS Lambda function to perform this transformation and send the output to a Kinesis data stream that is connected to your Kinesis Data Analytics application. This way, you can use SQL to query the stream with the least latency, as Lambda functions are triggered in near real time by the incoming data and Kinesis Data Analytics can process the data as soon as it arrives.

The other options are not optimal for this scenario, as they introduce more latency or complexity. AWS Glue is a serverless data integration service that can perform ETL (extract, transform, and load) tasks on data sources, but it is not designed for real-time streaming data analysis. An Amazon Kinesis Client Library is a Java library that enables you to build custom applications that process data from Kinesis data streams, but it requires more coding and configuration than using a Lambda function. Amazon Kinesis Data Firehose is a service that can deliver streaming data to destinations such as Amazon S3, Amazon Redshift, Amazon OpenSearch Service, and Splunk, but it does not support SQL queries on the data.

What Is Amazon Kinesis Data Analytics for SQL Applications?

Using AWS Lambda with Amazon Kinesis Data Streams

Using AWS Lambda with Amazon Kinesis Data Firehose

Image classification is a machine learning task that involves assigning labels to images based on their content. Image classification can be performed using various algorithms, such as convolutional neural networks (CNNs), which are a type of deep learning model that can learn to extract high-level features from images. To train a customized image classification model, the e-commerce company needs a compute choice that can support the high computational demands of deep learning and provide good accuracy on the model quickly. A GPU accelerated computing instance, such as p3.2xlarge, is a suitable choice for this task, as it can leverage the parallel processing power of GPUs to speed up the training process and reduce the training time. A p3.2xlarge instance has one NVIDIA Tesla V100 GPU, which can provide up to 125 teraflops of mixed-precision performance and 16 GB of GPU memory. A p3.2xlarge instance can also use various deep learning frameworks, such as TensorFlow, PyTorch, MXNet, etc., to build and train the image classification model. A p3.2xlarge instance is also more cost-effective than a p3.8xlarge instance, which has four NVIDIA Tesla V100 GPUs, as the latter may not be necessary for a proof of concept with a small dataset. Therefore, the Machine Learning Specialist should select p3.2xlarge as the compute choice to train and achieve good accuracy on the model quickly.

Amazon EC2 P3 Instances - Amazon Web Services

Image Classification - Amazon SageMaker

Convolutional Neural Networks - Amazon SageMaker

Deep Learning AMIs - Amazon Web Services

The correct combination of actions to enable the data scientist’s IAM user to invoke the SageMaker endpoint is B, C, and E, because they ensure that the IAM user has the necessary permissions, access, and syntax to query the ML model from Athena. These actions have the following benefits:

B: Including a policy statement for the IAM user that allows the sagemaker:InvokeEndpoint action grants the IAM user the permission to call the SageMaker Runtime InvokeEndpoint API, which is used to get inferences from the model hosted at the endpoint1.

C: Including an inline policy for the IAM user that allows SageMaker to read S3 objects enables the IAM user to access the data stored in S3, which is the source of the Athena queries2.

E: Including the SQL statement “USING EXTERNAL FUNCTION ml_function_name” in the Athena SQL query allows the IAM user to invoke the ML model as an external function from Athena, which is a feature that enables querying ML models from SQL statements3.

The other options are not correct or necessary, because they have the following drawbacks:

A: Attaching the AmazonAthenaFullAccess AWS managed policy to the user identity is not sufficient, because it does not grant the IAM user the permission to invoke the SageMaker endpoint, which is required to query the ML model4.

D: Including a policy statement for the IAM user that allows the IAM user to perform the sagemaker:GetRecord action is not relevant, because this action is used to retrieve a single record from a feature group, which is not the case in this scenario5.

F: Performing a user remapping in SageMaker to map the IAM user to another IAM user that is on the hosted endpoint is not applicable, because this feature is only available for multi-model endpoints, which are not used in this scenario.

1: InvokeEndpoint - Amazon SageMaker

2: Querying Data in Amazon S3 from Amazon Athena - Amazon Athena

3: Querying machine learning models from Amazon Athena using Amazon SageMaker | AWS Machine Learning Blog

4: AmazonAthenaFullAccess - AWS Identity and Access Management

5: GetRecord - Amazon SageMaker Feature Store Runtime

[Invoke a Multi-Model Endpoint - Amazon SageMaker]

In Amazon SageMaker, hyperparameter tuning jobs optimize model performance by adjusting hyperparameters. Amazon SageMaker’s hyperparameter tuning supports completion criteria settings that enable efficient management of tuning resources. In this scenario, the ML specialist aims to set a completion criterion that will terminate the tuning job as soon as SageMaker detects that further improvements in the objective metric are unlikely to exceed 1%.

The CompleteOnConvergence setting is designed for such requirements. This criterion enables the tuning job to automatically stop when SageMaker determines that additional hyperparameter evaluations are unlikely to improve the objective metric beyond a certain threshold, allowing for efficient tuning completion. The convergence process relies on an internal optimization algorithm that continuously evaluates the objective metric during tuning and stops when performance stabilizes without further improvement.

This is supported by AWS documentation, which explains that CompleteOnConvergence is an efficient way to manage tuning by stopping unnecessary evaluations once the model performance stabilizes within the specified threshold.

 The XGBoost model is a supervised machine learning algorithm, which means it requires labeled data to learn from. The customers’ current segmentation is unknown, so there is no label to train the XGBoost model on. Moreover, the XGBoost model is designed for classification or regression tasks, not for clustering. Clustering is a type of unsupervised machine learning, which means it does not require labeled data. Clustering algorithms try to find natural groups or clusters in the data based on their similarity or distance. A common clustering algorithm is k-means, which partitions the data into K clusters, where each data point belongs to the cluster with the nearest mean. To meet the current requirements, the data scientist should train a k-means model with K = 5 on the same dataset to predict a segment for each customer. This way, the data scientist can identify five customer segments based on age, income, and location, without needing any labels. References:

What is XGBoost? - Amazon SageMaker

What is Clustering? - Amazon SageMaker

K-Means Algorithm - Amazon SageMaker

SageMaker Automatic Model Tuning (AMT) is a fully managed hyperparameter optimization feature that finds the best values for model parameters like eta, alpha, min_child_weight, and max_depth.

“Use SageMaker automatic model tuning to find the best version of a model by running many training jobs on your dataset using the ranges of hyperparameters that you specify.”

It supports built-in algorithms like XGBoost, and can optimize for evaluation metrics like AUC, making it the least operational overhead solution for this task.

The solution C will meet the requirements because it uses Amazon SageMaker Spot Instances, which are unused EC2 instances that are available at up to 90% discount compared to On-Demand prices. Amazon SageMaker Spot Instances can speed up training and lower costs by taking advantage of the spare EC2 capacity. The company does not need to make any code changes to use Spot Instances, as it can simply enable the managed spot training option in the SageMaker training job configuration. The company also does not need to implement model checkpoints, as it is using only one epoch for training, which means the model will not resume from a previous state1.

The other options are not suitable because:

Option A: Configuring the SageMaker training job to use Pipe mode instead of File mode will not speed up training or lower costs significantly. Pipe mode is a data ingestion mode that streams data directly from S3 to the training algorithm, without copying the data to the local storage of the training instance. Pipe mode can reduce the startup time of the training job and the disk space usage, but it does not affect the computation time or the instance price. Moreover, Pipe mode may require some code changes to handle the streaming data, depending on the training algorithm2.

Option B: Configuring the SageMaker training job to use FastFile mode instead of File mode will not speed up training or lower costs significantly. FastFile mode is a data ingestion mode that copies data from S3 to the local storage of the training instance in parallel with the training process. FastFile mode can reduce the startup time of the training job and the disk space usage, but it does not affect the computation time or the instance price. Moreover, FastFile mode is only available for distributed training jobs that use multiple instances, which is not the case for the company3.

Option D: Configuring the SageMaker training job to use Spot Instances and implementing model checkpoints will not meet the requirements without the need to make any code changes. Model checkpoints are a feature that allows the training job to save the model state periodically to S3, and resume from the latest checkpoint if the training job is interrupted. Model checkpoints can help to avoid losing the training progress and ensure the model convergence, but they require some code changes to implement the checkpointing logic and the resuming logic4.

1: Managed Spot Training - Amazon SageMaker

2: Pipe Mode - Amazon SageMaker

3: FastFile Mode - Amazon SageMaker

4: Checkpoints - Amazon SageMaker

Amazon SageMaker Studio Data Wrangler is a visual data preparation tool that enables users to clean and normalize data without writing any code. Using Data Wrangler, the data scientist can easily import the time-series data from various sources, such as Amazon S3, Amazon Athena, or Amazon Redshift. Data Wrangler can automatically generate data insights and quality reports, which can help identify and fix missing values, outliers, and anomalies in the data. Data Wrangler also provides over 250 built-in transformations, such as resampling, interpolation, aggregation, and filtering, which can be applied to the data with a point-and-click interface. Data Wrangler can also export the prepared data to different destinations, such as Amazon S3, Amazon SageMaker Feature Store, or Amazon SageMaker Pipelines, for further modeling and analysis. Data Wrangler is integrated with Amazon SageMaker Studio, a web-based IDE for machine learning, which makes it easy to access and use the tool. Data Wrangler is a serverless and fully managed service, which means the data scientist does not need to provision, configure, or manage any infrastructure or clusters.

Option A is incorrect because Amazon EMR Serverless is a serverless option for running big data analytics applications using open-source frameworks, such as Apache Spark. However, using Amazon EMR Serverless would require the data scientist to write PySpark code to perform the data preparation tasks, such as resampling, imputation, and aggregation. This would require more implementation effort than using Data Wrangler, which provides a visual and code-free interface for data preparation.

Option B is incorrect because AWS Glue DataBrew is another visual data preparation tool that can be used to clean and normalize data without writing code. However, DataBrew does not support time-series data as a data type, and does not provide built-in transformations for resampling, interpolation, or aggregation of time-series data. Therefore, using DataBrew would not meet the requirements of the use case.

Option D is incorrect because using Amazon SageMaker Studio Notebook with Pandas would also require the data scientist to write Python code to perform the data preparation tasks. Pandas is a popular Python library for data analysis and manipulation, which supports time-series data and provides various methods for resampling, interpolation, and aggregation. However, using Pandas would require more implementation effort than using Data Wrangler, which provides a visual and code-free interface for data preparation.

1: Amazon SageMaker Data Wrangler documentation

2: Amazon EMR Serverless documentation

3: AWS Glue DataBrew documentation

4: Pandas documentation

Principal component analysis (PCA) is an unsupervised machine learning algorithm that attempts to reduce the dimensionality (number of features) within a dataset while still retaining as much information as possible. This is done by finding a new set of features called components, which are composites of the original features that are uncorrelated with one another. They are also constrained so that the first component accounts for the largest possible variability in the data, the second component the second most variability, and so on. By using PCA, the impact of having a large number of features that are highly correlated with each other can be reduced, as the new feature space will have fewer dimensions and less redundancy. This can make the linear models more stable and less prone to overfitting. References:

Principal Component Analysis (PCA) Algorithm - Amazon SageMaker

Perform a large-scale principal component analysis faster using Amazon SageMaker | AWS Machine Learning Blog

Machine Learning- Prinicipal Component Analysis | i2tutorials

Data augmentation is a technique to increase the size and diversity of the training data by applying random transformations such as rotation, translation, scaling, flipping, etc. This can help reduce overfitting and improve the generalization of the model. Data augmentation can be done using the Amazon SageMaker image classification algorithm, which supports various augmentation options such as horizontal_flip, vertical_flip, rotate, brightness, contrast, etc1

The Amazon SageMaker k-nearest neighbors (k-NN) algorithm is a supervised learning algorithm that can be used to label unlabeled data based on the similarity to the labeled data. The k-NN algorithm assigns a label to an unlabeled instance by finding the k closest labeled instances in the feature space and taking a majority vote among their labels. This can help increase the size and diversity of the training data and reduce overfitting. The k-NN algorithm can be used with the Amazon SageMaker image classification algorithm by extracting features from the images using a pre-trained model and then applying the k-NN algorithm on the feature vectors2

Using Amazon SageMaker Ground Truth to label the unlabeled images is not a good option because it is a manual and costly process that requires human annotators. Moreover, it does not address the issue of overfitting on the existing labeled data.

Using image preprocessing to transform the images into grayscale images is not a good option because it reduces the amount of information and variation in the images, which can degrade the performance of the model. Moreover, it does not address the issue of overfitting on the existing labeled data.

Replacing the activation of the last layer with a sigmoid is not a good option because it is not suitable for a multi-class classification problem. A sigmoid activation function outputs a value between 0 and 1, which can be interpreted as a probability of belonging to a single class. However, for a multi-class classification problem, the output should be a vector of probabilities that sum up to 1, which can be achieved by using a softmax activation function.

Amazon Athena is an interactive query service that allows you to analyze data stored in Amazon S3 using standard SQL. Athena is serverless, so you only pay for the queries that you run and there is no infrastructure to manage.

To optimize the query performance of Athena, one of the best practices is to convert the data into a columnar format, such as Apache Parquet or Apache ORC. Columnar formats store data by columns rather than by rows, which allows Athena to scan only the columns that are relevant to the query, reducing the amount of data read and improving the query speed. Columnar formats also support compression and encoding schemes that can reduce the storage space and the data scanned per query, further enhancing the performance and reducing the cost.

In contrast, plaintext CSV files store data by rows, which means that Athena has to scan the entire row even if only a few columns are needed for the query. This increases the amount of data read and the query latency. Moreover, plaintext CSV files do not support compression or encoding, which means that they take up more storage space and incur higher query costs.

Therefore, the Machine Learning Specialist should transform the dataset to Apache Parquet format to minimize query runtime.

Top 10 Performance Tuning Tips for Amazon Athena

Columnar Storage Formats

Using compressions will reduce the amount of data scanned by Amazon Athena, and also reduce your S3 bucket storage. It’s a Win-Win for your AWS bill. Supported formats: GZIP, LZO, SNAPPY (Parquet) and ZLIB.

Overfitting occurs when a model performs well on the training data but poorly on the test data. This is often because the model has learned the training data too well and is not able to generalize to new data. To avoid overfitting, the Machine Learning Specialist should lower the max_depth parameter value. This will reduce the complexity of the model and make it less likely to overfit. According to the XGBoost documentation1, the max_depth parameter controls the maximum depth of a tree and lower values can help prevent overfitting. The documentation also suggests other ways to control overfitting, such as adding randomness, using regularization, and using early stopping1. References:

XGBoost Parameters

The best solution to meet the requirements of the company is to use AWS DataSync to make an initial copy of the entire dataset, and schedule subsequent incremental transfers of changing data until the final cutover from on premises to AWS. This is because:

AWS DataSync is an online data movement and discovery service that simplifies data migration and helps you quickly, easily, and securely transfer your file or object data to, from, and between AWS storage services 1. AWS DataSync can copy data between on-premises object storage and Amazon S3, and also supports encryption, scheduling, monitoring, and data integrity validation 1.

AWS DataSync can make an initial copy of the entire dataset by using a DataSync agent, which is a software appliance that connects to your on-premises storage and manages the data transfer to AWS 2. The DataSync agent can be deployed as a virtual machine (VM) on your existing hypervisor, or as an Amazon EC2 instance in your AWS account 2.

AWS DataSync can schedule subsequent incremental transfers of changing data by using a task, which is a configuration that specifies the source and destination locations, the options for the transfer, and the schedule for the transfer 3. You can create a task to run once or on a recurring schedule, and you can also use filters to include or exclude specific files or objects based on their names or prefixes 3.

AWS DataSync can perform the final cutover from on premises to AWS by using a sync task, which is a type of task that synchronizes the data in the source and destination locations 4. A sync task transfers only the data that has changed or that doesn’t exist in the destination, and also deletes any files or objects from the destination that were deleted from the source since the last sync 4.

Therefore, by using AWS DataSync, the company can create a data repository in the AWS Cloud for machine learning projects, and use Amazon S3 for the data storage, while meeting the requirements of encryption, scheduling, monitoring, and data integrity validation.

Data Transfer Service - AWS DataSync

Deploying a DataSync Agent

Creating a Task

Syncing Data with AWS DataSync

In linear least squares regression, the design matrix (often denoted as XXX) must have full rank to ensure a unique solution. When two or more features are perfectly linearly dependent, it leads to multicollinearity, causing the matrix XTXX^TXXTX to become singular (non-invertible). This singularity prevents the computation of a unique solution for the regression coefficients.​

From the documentation:​

Therefore, perfect linear dependence among features is problematic for linear regression models as it undermines the mathematical foundation required for deriving unique solutions.

Feature selection is the process of selecting a subset of extracted features that are relevant and contribute to minimizing the error rate of a trained model. Some techniques for feature selection are:

Correlation plot with heat maps: This technique visualizes the correlation between features using a color-coded matrix. Features that are highly correlated with each other or with the target variable can be identified and removed to reduce redundancy and noise.

Univariate selection: This technique evaluates each feature individually based on a statistical test, such as chi-square, ANOVA, or mutual information, and selects the features that have the highest scores or p-values. This technique is simple and fast, but it does not consider the interactions between features.

Feature importance with a tree-based classifier: This technique uses a tree-based classifier, such as random forest or gradient boosting, to rank the features based on their importance in splitting the nodes. Features that have low importance scores can be dropped from the model. This technique can capture the non-linear relationships and interactions between features.

The other options are not techniques for feature selection, but rather for feature engineering, which is the process of creating, transforming, or extracting features from the original data. Feature engineering can improve the performance and interpretability of the model, but it does not reduce the number of features.

Data scaling with standardization and normalization: This technique transforms the features to have a common scale, such as zero mean and unit variance, or a range between 0 and 1. This technique can help some algorithms, such as k-means or logistic regression, to converge faster and avoid numerical instability, but it does not change the number of features.

Data binning: This technique groups the continuous features into discrete bins or categories based on some criteria, such as equal width, equal frequency, or clustering. This technique can reduce the noise and outliers in the data, and also create ordinal or nominal features that can be used for some algorithms, such as decision trees or naive Bayes, but it does not reduce the number of features.

Data augmentation: This technique generates new data from the existing data by applying some transformations, such as rotation, flipping, cropping, or noise addition. This technique can increase the size and diversity of the data, and help prevent overfitting, but it does not reduce the number of features.

Feature engineering - Machine Learning Lens

Amazon SageMaker Autopilot now provides feature selection and the ability to change data types while creating an AutoML experiment

Feature Selection in Machine Learning | Baeldung on Computer Science

Feature Selection in Machine Learning: An easy Introduction

 To improve the test error for the image classifier, the Machine Learning Specialist should use the technique of increasing the training data by adding variation in rotation for training images. This technique is called data augmentation, which is a way of artificially expanding the size and diversity of the training dataset by applying various transformations to the original images, such as rotation, flipping, cropping, scaling, etc. Data augmentation can help the model learn more robust features that are invariant to the orientation, position, and size of the objects in the images. This can improve the generalization ability of the model and reduce the test error, especially for cases where the images are not well-aligned or have different perspectives1.

1: Image Augmentation - Amazon SageMaker

 The issue described in the question is a sign of overfitting, which is a common problem in machine learning when the model learns the noise and details of the training data too well and fails to generalize to new and unseen data. Overfitting can result in a low training error rate but a high test error rate, which indicates poor performance and validity of the model. There are several techniques that can be used to prevent or reduce overfitting, such as data augmentation and regularization.

Data augmentation is a technique that applies various transformations to the original training data, such as rotation, scaling, cropping, flipping, adding noise, changing brightness, etc., to create new and diverse data samples. Data augmentation can increase the size and diversity of the training data, which can help the model learn more features and patterns and reduce the variance of the model. Data augmentation is especially useful for image data, as it can simulate different scenarios and perspectives that the model may encounter in real life. For example, in the question, the device uses a camera to observe drivers’ behavior, so data augmentation can help the model deal with different lighting conditions, angles, distances, etc. Data augmentation can be done using various libraries and frameworks, such as TensorFlow, PyTorch, Keras, OpenCV, etc12

Regularization is a technique that adds a penalty term to the model’s objective function, which is typically based on the model’s parameters. Regularization can reduce the complexity and flexibility of the model, which can prevent overfitting by avoiding learning the noise and details of the training data. Regularization can also improve the stability and robustness of the model, as it can reduce the sensitivity of the model to small fluctuations in the data. There are different types of regularization, such as L1, L2, dropout, etc., but they all have the same goal of reducing overfitting. L2 regularization, also known as weight decay or ridge regression, is one of the most common and effective regularization techniques. L2 regularization adds the squared norm of the model’s parameters multiplied by a regularization parameter (lambda) to the model’s objective function. L2 regularization can shrink the model’s parameters towards zero, which can reduce the variance of the model and improve the generalization ability of the model. L2 regularization can be implemented using various libraries and frameworks, such as TensorFlow, PyTorch, Keras, Scikit-learn, etc34

The other options are not valid or relevant for resolving the issue of overfitting. Adding vanishing gradient to the model is not a technique, but a problem that occurs when the gradient of the model’s objective function becomes very small and the model stops learning. Making the neural network architecture complex is not a solution, but a possible cause of overfitting, as a complex model can have more parameters and more flexibility to fit the training data too well. Using gradient checking in the model is not a technique, but a debugging method that verifies the correctness of the gradient computation in the model. Gradient checking is not related to overfitting, but to the implementation of the model.

Data normalization is a data preprocessing technique that scales the features to a common range, such as [0, 1] or [-1, 1]. This helps reduce the impact of features with high magnitude on the cost function and improves the convergence during backpropagation. Data normalization can be done using different methods, such as min-max scaling, z-score standardization, or unit vector normalization. Data normalization is different from dimensionality reduction, which reduces the number of features; model regularization, which adds a penalty term to the cost function to prevent overfitting; and data augmentation, which increases the amount of data by creating synthetic samples. References:

Data processing options for AI/ML | AWS Machine Learning Blog

Data preprocessing - Machine Learning Lens

How to Normalize Data Using scikit-learn in Python

Normalization | Machine Learning | Google for Developers

The solution that will accomplish the necessary transformation to train the Amazon SageMaker model with the least amount of administrative overhead is to crawl the data using AWS Glue crawlers, write an AWS Glue ETL job that merges the two tables and writes the output in CSV format to Amazon S3. This solution leverages the serverless capabilities of AWS Glue to automatically discover the schema of the data sources, and to perform the data integration and transformation without requiring any cluster management or configuration. The output in CSV format is compatible with Amazon SageMaker and can be easily loaded into a training job. References: AWS Glue, Amazon SageMaker

Updating the existing SageMaker endpoint to use a new configuration that is weighted to send 5% of the traffic to the new variant is the simplest approach with the least risk to deploy the model and roll it back, if needed. This is because SageMaker supports A/B testing, which allows the Specialist to compare the performance of different model variants by sending a portion of the traffic to each variant. The Specialist can monitor the metrics of each variant and adjust the weights accordingly. If the new variant does not perform as expected, the Specialist can revert traffic to the last version by resetting the weights to 100% for the old variant and 0% for the new variant. This way, the Specialist can deploy the model without affecting the customer experience and roll it back easily if needed. References:

Amazon SageMaker

Deploying models to Amazon SageMaker hosting services

Amazon Kinesis Data Firehose is the most operationally simple and fully managed service for real-time data ingestion and transformation. Firehose supports:

Direct ingestion from edge locations or public endpoints

Integration with AWS Lambda for transformation

Backup of raw source records

Error handling with separate S3 prefixes for failed transformations

“You can configure your delivery stream to use a Lambda function to transform incoming records, and to back up all or failed records to Amazon S3.”

This meets all requirements with minimal infrastructure management:

Real-time ingestion

Inline transformation

Raw data and failed transformation storage in S3

To comply with the company’s security requirement that SageMaker notebook instances must not have internet connectivity, the most secure and recommended approach is to use VPC interface endpoints (AWS PrivateLink) for connecting SageMaker to other AWS services such as Amazon S3, without requiring internet access or a NAT Gateway.

Exact Extract from Amazon SageMaker documentation:

“You can connect to your notebook instance from your VPC through an interface endpoint in your Virtual Private Cloud (VPC) instead of connecting over the public internet. When you use a VPC interface endpoint, communication between your VPC and the notebook instance is conducted entirely and securely within the AWS network.”

Further security can be enforced using IAM and security group restrictions:

“To restrict access to only connections made from within your VPC, create an AWS Identity and Access Management (IAM) policy that restricts access to only calls that come from within your VPC. Then add that policy to every AWS Identity and Access Management user, group, or role used to access the notebook instance.”

Also:

“Use VPC endpoints to keep traffic between your VPC and SageMaker in the AWS network. This is especially useful when your notebook instances or endpoints don’t have internet access.”

By configuring interface VPC endpoints and controlling security groups and IAM policies appropriately, the SageMaker notebook instances can operate without any internet connectivity, meeting the company’s policy securely and effectively.

 The best way to securely access data stored in a specific Amazon S3 bucket from an Amazon SageMaker notebook instance is to attach a policy to the IAM role associated with the notebook that allows GetObject, PutObject, and ListBucket operations to the specific S3 bucket. This way, the notebook can use the AWS SDK or CLI to access the S3 bucket without exposing any credentials or requiring any additional configuration. This is also the recommended approach by AWS for granting access to S3 from SageMaker. References:

Amazon SageMaker Roles

Accessing Amazon S3 from a SageMaker Notebook Instance

 Amazon S3 and Amazon Athena are technologies that meet the company’s requirements for a temporary ad hoc solution for machine learning data storage and query. Amazon S3 and Amazon Athena have the following features and benefits:

Amazon S3 is a service that provides scalable, durable, and secure object storage for any type of data. Amazon S3 can store csv and JSON files, as well as other formats, and can handle large volumes of data with high availability and performance. Amazon S3 also integrates with other AWS services, such as Amazon Athena, for further processing and analysis of the data.

Amazon Athena is a service that allows querying data stored in Amazon S3 using standard SQL. Amazon Athena can query over semi-structured data, such as JSON, as well as structured data, such as csv, without requiring any loading or transformation. Amazon Athena is serverless, meaning that there is no infrastructure to manage and users only pay for the queries they run. Amazon Athena also supports the use of AWS Glue Data Catalog, which is a centralized metadata repository that can store and manage the schema and partition information of the data in Amazon S3.

Using Amazon S3 and Amazon Athena, the company can achieve the following high priorities:

Solution simplicity: Amazon S3 and Amazon Athena are easy to use and require minimal configuration and maintenance. The company can simply upload the csv and JSON files to Amazon S3 and use Amazon Athena to query them using SQL. The company does not need to worry about provisioning, scaling, or managing any servers or clusters.

Fast development time: Amazon S3 and Amazon Athena can enable the company to quickly access and analyze the data without any data preparation or loading. The company can use the existing workforce of SQL experts to write and run queries on Amazon Athena and get results in seconds or minutes.

Low cost: Amazon S3 and Amazon Athena are cost-effective and offer pay-as-you-go pricing models. Amazon S3 charges based on the amount of storage used and the number of requests made. Amazon Athena charges based on the amount of data scanned by the queries. The company can also reduce the costs by using compression, encryption, and partitioning techniques to optimize the data storage and query performance.

High flexibility: Amazon S3 and Amazon Athena are flexible and can support various data types, formats, and sources. The company can store and query any type of data in Amazon S3, such as csv, JSON, Parquet, ORC, etc. The company can also query data from multiple sources in Amazon S3, such as data lakes, data warehouses, log files, etc.

The other options are not as suitable as option A for the company’s requirements for the following reasons:

Option B: Amazon Redshift and AWS Glue are technologies that can be used for data warehousing and data integration, but they are not ideal for a temporary ad hoc solution. Amazon Redshift is a service that provides a fully managed, petabyte-scale data warehouse that can run complex analytical queries using SQL. AWS Glue is a service that provides a fully managed extract, transform, and load (ETL) service that can prepare and load data for analytics. However, using Amazon Redshift and AWS Glue would require more effort and cost than using Amazon S3 and Amazon Athena. The company would need to load the data from Amazon S3 to Amazon Redshift using AWS Glue, which can take time and incur additional charges. The company would also need to manage the capacity and performance of the Amazon Redshift cluster, which can be complex and expensive.

Option C: Amazon DynamoDB and DynamoDB Accelerator (DAX) are technologies that can be used for fast and scalable NoSQL database and caching, but they are not suitable for the company’s data storage and query needs. Amazon DynamoDB is a service that provides a fully managed, key-value and document database that can deliver single-digit millisecond performance at any scale. DynamoDB Accelerator (DAX) is a service that provides a fully managed, in-memory cache for DynamoDB that can improve the read performance by up to 10 times. However, using Amazon DynamoDB and DAX would not allow the company to continue to use SQL as a query language, as Amazon DynamoDB does not support SQL. The company would need to use the DynamoDB API or the AWS SDKs to access and query the data, which can require more coding and learning effort. The company would also need to transform the csv and JSON files into DynamoDB items, which can involve additional processing and complexity.

Option D: Amazon RDS and Amazon ES are technologies that can be used for relational database and search and analytics, but they are not optimal for the company’s data storage and query scenario. Amazon RDS is a service that provides a fully managed, relational database that supports various database engines, such as MySQL, PostgreSQL, Oracle, etc. Amazon ES is a service that provides a fully managed, Elasticsearch cluster, which is mainly used for search and analytics purposes. However, using Amazon RDS and Amazon ES would not be as simple and cost-effective as using Amazon S3 and Amazon Athena. The company would need to load the data from Amazon S3 to Amazon RDS, which can take time and incur additional charges. The company would also need to manage the capacity and performance of the Amazon RDS and Amazon ES clusters, which can be complex and expensive. Moreover, Amazon RDS and Amazon ES are not designed to handle semi-structured data, such as JSON, as well as Amazon S3 and Amazon Athena.

Amazon S3

Amazon Athena

Amazon Redshift

AWS Glue

Amazon DynamoDB

[DynamoDB Accelerator (DAX)]

[Amazon RDS]

[Amazon ES]

Regularization is a technique that can be used to prevent overfitting and improve model performance on unseen data. Overfitting occurs when the model learns the training data too well and fails to generalize to new and unseen data. This can be seen in the question, where the validation accuracy is lower than the training accuracy, and both are lower than the human-level performance. Regularization is a way of adding some constraints or penalties to the model to reduce its complexity and prevent it from memorizing the training data. Some common forms of regularization for image classification are:

Weight decay: Adding a term to the loss function that penalizes large weights in the model. This can help reduce the variance and noise in the model and make it more robust to small changes in the input.

Dropout: Randomly dropping out some units or connections in the model during training. This can help reduce the co-dependency among the units and make the model more resilient to missing or corrupted features.

Data augmentation: Artificially increasing the size and diversity of the training data by applying random transformations, such as cropping, flipping, rotating, scaling, etc. This can help the model learn more invariant and generalizable features and reduce the risk of overfitting to specific patterns in the training data.

The other options are not likely to fix the issue of overfitting, and may even worsen it:

A longer training time: This can lead to more overfitting, as the model will have more chances to fit the noise and details in the training data that are not relevant for the validation data.

Making the network larger: This can increase the model capacity and complexity, which can also lead to more overfitting, as the model will have more parameters to learn and adjust to the training data.

Using a different optimizer: This can affect the speed and stability of the training process, but not necessarily the generalization ability of the model. The choice of optimizer depends on the characteristics of the data and the model, and there is no guarantee that a different optimizer will prevent overfitting.

Regularization (machine learning)

Image Classification: Regularization

How to Reduce Overfitting With Dropout Regularization in Keras

Amazon Transcribe, Amazon Translate, and Amazon Comprehend are the most efficient combination of services to accomplish the task of sentiment analysis on a video clip with audio in Spanish. Amazon Transcribe is a service that can convert speech to text using deep learning. Amazon Transcribe can transcribe audio from various sources, such as video files, audio files, or streaming audio. Amazon Transcribe can also recognize multiple speakers, different languages, accents, dialects, and custom vocabularies. In this case, Amazon Transcribe can transcribe the audio from the video clip in Spanish to text in Spanish1 Amazon Translate is a service that can translate text from one language to another using neural machine translation. Amazon Translate can translate text from various sources, such as documents, web pages, chat messages, etc. Amazon Translate can also support multiple languages, domains, and styles. In this case, Amazon Translate can translate the text from Spanish to English2 Amazon Comprehend is a service that can analyze and derive insights from text using natural language processing. Amazon Comprehend can perform various tasks, such as sentiment analysis, entity recognition, key phrase extraction, topic modeling, etc. Amazon Comprehend can also support multiple languages and domains. In this case, Amazon Comprehend can perform sentiment analysis on the text in English and determine whether the feedback is positive, negative, neutral, or mixed3

The other options are not valid or efficient for accomplishing the task of sentiment analysis on a video clip with audio in Spanish. Amazon Comprehend, Amazon SageMaker seq2seq, and Amazon SageMaker Neural Topic Model (NTM) are not a good combination, as they do not include a service that can transcribe speech to text, which is a necessary step for processing the audio from the video clip. Amazon Comprehend, Amazon Translate, and Amazon SageMaker BlazingText are not a good combination, as they do not include a service that can perform sentiment analysis, which is the main goal of the task. Amazon SageMaker BlazingText is a service that can train and deploy text classification and word embedding models using deep learning. Amazon SageMaker BlazingText can perform tasks such as text classification, named entity recognition, part-of-speech tagging, etc., but not sentiment analysis4

To build a scalable, serverless, and cost-effective data ingestion pipeline, this solution uses a Kinesis data stream to handle fluctuations in data flow, buffering and distributing incoming data in real time. By connecting two Amazon Kinesis Data Firehose delivery streams to the Kinesis data stream, the company can simultaneously route data to Amazon S3 for backup and Amazon OpenSearch Service for analysis.

This approach meets all requirements by providing automatic scaling, reducing operational overhead, and ensuring data storage and analysis without duplicating efforts or needing additional infrastructure.

In cases where survey questions allow multiple choices per question, one-hot encoding is an effective way to represent responses as binary features. Each possible option for each question is transformed into a separate binary column (1 if selected, 0 if not), providing a comprehensive and machine-readable format that logistic regression models can interpret effectively.

This approach ensures that each respondent’s selections are accurately captured in a format suitable for training, offering a straightforward representation for multi-choice responses.

The metrics that will indicate an ML solution that will provide the greatest probability of detecting an abnormality are precision and recall. Precision is the ratio of true positives (TP) to the total number of predicted positives (TP + FP), where FP is false positives. Recall is the ratio of true positives (TP) to the total number of actual positives (TP + FN), where FN is false negatives. A high precision means that the ML solution has a low rate of false alarms, while a high recall means that the ML solution has a high rate of true detections. For the chemical company, the goal is to avoid process abnormalities, which are marked as 1 in the labels. Therefore, the company needs an ML solution that has a high recall for the positive class, meaning that it can detect most of the abnormalities and minimize the false negatives. Among the four options, option B has the highest recall for the positive class, which is 0.98. This means that the ML solution can detect 98% of the abnormalities and miss only 2%. Option B also has a reasonable precision for the positive class, which is 0.61. This means that the ML solution has a false alarm rate of 39%, which may be acceptable for the company, depending on the cost and benefit analysis. The other options have lower recall for the positive class, which means that they have higher false negative rates, which can be more detrimental for the company than false positive rates.

1: AWS Certified Machine Learning - Specialty Exam Guide

2: AWS Training - Machine Learning on AWS

3: AWS Whitepaper - An Overview of Machine Learning on AWS

4: Precision and recall

To develop a daily ETL workflow containing multiple ETL jobs that can start as soon as data is uploaded to Amazon S3, the best configuration is to use AWS Lambda to trigger an AWS Step Functions workflow to wait for dataset uploads to complete in Amazon S3. Use AWS Glue to join the datasets. Use an Amazon CloudWatch alarm to send an SNS notification to the Administrator in the case of a failure.

AWS Lambda is a serverless compute service that lets you run code without provisioning or managing servers. You can use Lambda to create functions that respond to events such as data uploads to Amazon S3. You can also use Lambda to invoke other AWS services such as AWS Step Functions and AWS Glue.

AWS Step Functions is a service that lets you coordinate multiple AWS services into serverless workflows. You can use Step Functions to create a state machine that defines the sequence and logic of your ETL workflow. You can also use Step Functions to handle errors and retries, and to monitor the execution status of your workflow.

AWS Glue is a serverless data integration service that makes it easy to discover, prepare, and combine data for analytics. You can use Glue to create and run ETL jobs that can join data from multiple sources in Amazon S3. You can also use Glue to catalog your data and make it searchable and queryable.

Amazon CloudWatch is a service that monitors your AWS resources and applications. You can use CloudWatch to create alarms that trigger actions when a metric or a log event meets a specified threshold. You can also use CloudWatch to send notifications to Amazon Simple Notification Service (SNS) topics, which can then deliver the notifications to subscribers such as email addresses or phone numbers.

Therefore, by using these services together, you can achieve the following benefits:

You can start the ETL workflow as soon as data is uploaded to Amazon S3 by using Lambda functions to trigger Step Functions workflows.

You can wait for all the datasets to be available in Amazon S3 by using Step Functions to poll the S3 buckets and check the data completeness.

You can join the datasets with terabyte-sized datasets in Amazon S3 by using Glue ETL jobs that can scale and parallelize the data processing.

You can store the results of joining datasets in Amazon S3 by using Glue ETL jobs to write the output to S3 buckets.

You can send a notification to the Administrator if one of the jobs fails by using CloudWatch alarms to monitor the Step Functions or Glue metrics and send SNS notifications in case of a failure.

In this scenario, Kinesis Data Streams efficiently ingests real-time event data, while Amazon Managed Service for Apache Flink (formerly Amazon Kinesis Data Analytics) is ideal for transforming and analyzing data in a continuous stream. Apache Flink allows processing of time-based windows, such as the 10-minute sliding window required here, with low operational overhead.

This combination provides an effective solution for low-latency data processing and transformation, meeting the requirements for preparing data for inference with minimal setup and serverless scalability.

Amazon Comprehend is a natural language processing (NLP) service that uses machine learning to find insights and relationships in text. It can analyze text documents and identify the key topics, entities, sentiments, languages, and more. In this case, Amazon Comprehend can be used with the transcribed files from Amazon Transcribe to extract the main topics that are being discussed by the call operators. This can help to understand the common issues and concerns of the customers, and provide insights for further analysis and improvement. References:

Amazon Comprehend - Amazon Web Services

AWS Certified Machine Learning - Specialty Sample Questions

Amazon Kinesis Data Streams is a service that can capture, store, and process streaming data in real time. Amazon Kinesis Data Firehose is a service that can deliver streaming data to various destinations, such as Amazon S3, Amazon Redshift, or Amazon Elasticsearch Service. Amazon Kinesis Data Firehose can also transform the data before delivering it, such as converting the data format, compressing the data, or encrypting the data. One of the supported data formats that Amazon Kinesis Data Firehose can convert to is Apache Parquet, which is a columnar storage format that can improve the performance and cost-efficiency of analytics queries. By using Amazon Kinesis Data Streams and Amazon Kinesis Data Firehose, the Mobile Network Operator can ingest the .CSV data from the source systems and use Amazon Kinesis Data Firehose to convert the data into Parquet before storing it on Amazon S3. This solution takes the least effort to implement, as it does not require any additional resources, such as Amazon EC2 instances, Amazon EMR clusters, or Amazon Glue jobs. The solution can also leverage the built-in features of Amazon Kinesis Data Firehose, such as data buffering, batching, retry, and error handling.

Amazon Kinesis Data Streams - Amazon Web Services

Amazon Kinesis Data Firehose - Amazon Web Services

Data Transformation - Amazon Kinesis Data Firehose

Apache Parquet - Amazon Athena

CNN-QR is a proprietary machine learning algorithm for forecasting time series using causal convolutional neural networks (CNNs). CNN-QR works best with large datasets containing hundreds of time series. It accepts item metadata, and is the only Forecast algorithm that accepts related time series data without future values. In this case, the power company has historical power consumption data for the last 10 years, which is a large dataset with multiple time series. The data also includes related data such as weather, number of individuals on the property, and public holidays, which can be used as item metadata or related time series data. Therefore, CNN-QR is the most suitable algorithm for this scenario. References: Amazon Forecast Algorithms, Amazon Forecast CNN-QR

A scatter plot allows the first two dimensions to be represented by coordinates, while color and size represent the third and fourth dimensions, respectively.

From AWS documentation:

“Scatter plots in Amazon SageMaker Canvas and Data Wrangler can visualize relationships between two numeric variables. Additional dimensions can be represented using point size and color.”

— AWS SageMaker Canvas documentation

A naive Bayesian model assumes that the features are conditionally independent given the class label. This means that the joint probability of the features and the class can be factorized as the product of the class prior and the feature likelihoods. A full Bayesian network, on the other hand, does not make this assumption and allows for modeling arbitrary dependencies between the features and the class using a directed acyclic graph. In this case, the joint probability of the features and the class is given by the product of the conditional probabilities of each node given its parents in the graph. If the features are statistically dependent, meaning that their correlation coefficients are not close to zero, then a naive Bayesian model would not capture these dependencies and would likely perform worse than a full Bayesian network that can account for them. Therefore, a full Bayesian network describes the underlying data better in this situation. References:

Naive Bayes and Text Classification I

Bayesian Networks

The best indicators that an ML-based solution is suitable for this scenario are D and E, because they imply that the historical sensor data is sufficient and relevant for building a predictive maintenance model. This model can use machine learning techniques such as regression, classification, or anomaly detection to learn from the past data and forecast future failures or issues12. Having high granularity and diversity of data can improve the accuracy and generalization of the model, as well as enable the detection of complex patterns and relationships that are not captured by simple rule-based thresholds3.

The other options are not good indicators that an ML-based solution is suitable, because they suggest that the historical sensor data is incomplete, inconsistent, or inadequate for building a predictive maintenance model. These options would require additional data collection, preprocessing, or augmentation to overcome the data quality issues and ensure that the model can handle different scenarios and types of cranes4 .

1: Machine Learning Techniques for Predictive Maintenance

2: A Guide to Predictive Maintenance & Machine Learning

3: Machine Learning for Predictive Maintenance: Reinventing Asset Upkeep

4: Predictive Maintenance with Machine Learning: A Complete Guide

[Machine Learning for Predictive Maintenance - AWS Online Tech Talks]

To fix the incorrectly skewed data, the Data Scientist can apply two feature transformations: numerical value binning and logarithmic transformation. Numerical value binning is a technique that groups continuous values into discrete bins or categories. This can help reduce the skewness of the data by creating more balanced frequency distributions. Logarithmic transformation is a technique that applies the natural logarithm function to each value in the data. This can help reduce the right skewness of the data by compressing the large values and expanding the small values. Both of these transformations can make the data more suitable for machine learning algorithms that assume normality of the data. References:

Data Transformation - Amazon SageMaker

Transforming Skewed Data for Machine Learning

Amazon SageMaker Experiments is a service that helps track, compare, and evaluate different iterations of ML models. It can be used to capture key parameters such as the number of features and the sample count from a PySpark script that runs as a SageMaker processing job. A SageMaker processing job is a flexible and scalable way to run data processing workloads on AWS, such as feature engineering, data validation, model evaluation, and model interpretation.

Amazon SageMaker Experiments

Process Data and Evaluate Models

The algorithms that are best suited to this scenario are latent Dirichlet allocation (LDA) and neural topic modeling (NTM), as they are both unsupervised learning methods that can discover abstract topics from a collection of text documents. LDA and NTM can provide a list of the top words for each topic, as well as the topic distribution for each document, which can help the auditors assess the relevance and priority of the topic12.

The other options are not suitable because:

Option B: A random forest classifier is a supervised learning method that can perform classification or regression tasks by using an ensemble of decision trees. A random forest classifier is not suitable for discovering abstract topics from text documents, as it requires labeled data and predefined classes3.

Option D: A linear support vector machine is a supervised learning method that can perform classification or regression tasks by using a linear function that separates the data into different classes. A linear support vector machine is not suitable for discovering abstract topics from text documents, as it requires labeled data and predefined classes4.

Option E: A linear regression is a supervised learning method that can perform regression tasks by using a linear function that models the relationship between a dependent variable and one or more independent variables. A linear regression is not suitable for discovering abstract topics from text documents, as it requires labeled data and a continuous output variable5.

1: Latent Dirichlet Allocation

2: Neural Topic Modeling

3: Random Forest Classifier

4: Linear Support Vector Machine

5: Linear Regression

 In this scenario, the specialist should use one-hot encoding and RGB encoding to allow the regression model to learn from the Wall_Color data. One-hot encoding is a technique used to convert categorical data into numerical data. It creates new columns that store one-hot representation of colors. For example, a variable named color has three categories: red, green, and blue. After one-hot encoding, the new variables should be like this:

One-hot encoding can capture the presence or absence of a color, but it cannot capture the intensity or hue of a color. RGB encoding is a technique used to represent colors in a digital image. It creates three columns to encode the color in RGB format. For example, a variable named color has three categories: red, green, and blue. After RGB encoding, the new variables should be like this:

RGB encoding can capture the intensity and hue of a color, but it may also introduce correlation among the three columns. Therefore, using both one-hot encoding and RGB encoding can provide more information to the regression model than using either one alone.

Feature Engineering for Categorical Data

How to Perform Feature Selection with Categorical Data

Using log-scaled hyperparameters is a guideline that can improve the automatic model tuning in Amazon SageMaker. Log-scaled hyperparameters are hyperparameters that have values that span several orders of magnitude, such as learning rate, regularization parameter, or number of hidden units. Log-scaled hyperparameters can be specified by using a log-uniform distribution, which assigns equal probability to each order of magnitude within a range. For example, a log-uniform distribution between 0.001 and 1000 can sample values such as 0.001, 0.01, 0.1, 1, 10, 100, or 1000 with equal probability. Using log-scaled hyperparameters can allow the hyperparameter optimization feature to search the hyperparameter space more efficiently and effectively, as it can explore different scales of values and avoid sampling values that are too small or too large. Using log-scaled hyperparameters can also help avoid numerical issues, such as underflow or overflow, that may occur when using linear-scaled hyperparameters. Using log-scaled hyperparameters can be done by setting the ScalingType parameter to Logarithmic when defining the hyperparameter ranges in Amazon SageMaker12

The other options are not valid or relevant guidelines for improving the automatic model tuning in Amazon SageMaker. Choosing the maximum number of hyperparameters supported by Amazon SageMaker to search the largest number of combinations possible is not a good practice, as it can increase the time and cost of the tuning job and make it harder to find the optimal values. Amazon SageMaker supports up to 20 hyperparameters for tuning, but it is recommended to choose only the most important and influential hyperparameters for the model and algorithm, and use default or fixed values for the rest3 Specifying a very large hyperparameter range to allow Amazon SageMaker to cover every possible value is not a good practice, as it can result in sampling values that are irrelevant or impractical for the model and algorithm, and waste the tuning budget. It is recommended to specify a reasonable and realistic hyperparameter range based on the prior knowledge and experience of the model and algorithm, and use the results of the tuning job to refine the range if needed4 Executing only one hyperparameter tuning job at a time and improving tuning through successive rounds of experiments is not a good practice, as it can limit the exploration and exploitation of the hyperparameter space and make the tuning process slower and less efficient. It is recommended to use parallelism and concurrency to run multiple training jobs simultaneously and leverage the Bayesian optimization algorithm that Amazon SageMaker uses to guide the search for the best hyperparameter values5

 Amazon SageMaker is a service that allows users to build, train, and deploy ML models on AWS. Amazon SageMaker endpoints are scalable and secure web services that can be used to perform real-time inference on ML models. An endpoint configuration defines the models that are deployed and the resources that are used by the endpoint. An endpoint configuration can have multiple production variants, each representing a different version or variant of a model. Users can specify the portion of the inferences served by each production variant using the initialVariantWeight parameter. Users can also programmatically update the endpoint configuration to change the portion of the inferences served by each production variant using the UpdateEndpointWeightsAndCapacities API. Therefore, option B is the best solution to satisfy the requirements with minimal effort.

Option A is incorrect because creating multiple endpoints for each model would incur more cost and complexity than using a single endpoint with multiple production variants. Moreover, controlling the invocation of different models at the application layer would require more custom logic and coordination than using the UpdateEndpointWeightsAndCapacities API. Option C is incorrect because Amazon SageMaker Neo is a service that allows users to optimize ML models for different hardware platforms, such as edge devices. It is not relevant to the problem of running multiple versions of a model in parallel for long periods of time. Option D is incorrect because Amazon SageMaker batch transform is a service that allows users to perform asynchronous inference on large datasets. It is not suitable for the problem of performing real-time inference on streaming data from device users.

Deploying models to Amazon SageMaker hosting services - Amazon SageMaker

Update an Amazon SageMaker endpoint to accommodate new models - Amazon SageMaker

UpdateEndpointWeightsAndCapacities - Amazon SageMaker

 The best way to address the overfitting problem in image classification is to increase the dropout rate at the flatten layer because the model is not generalized enough. Dropout is a regularization technique that randomly drops out some units from the neural network during training, reducing the co-adaptation of features and preventing overfitting. The flatten layer is the layer that converts the output of the convolutional layers into a one-dimensional vector that can be fed into the dense layers. Increasing the dropout rate at the flatten layer means that more features from the convolutional layers will be ignored, forcing the model to learn more robust and generalizable representations from the remaining features.

The other options are not correct for this scenario because:

Increasing the learning rate would not help with the overfitting problem, as it would make the optimization process more unstable and prone to overshooting the global minimum. A high learning rate can also cause the model to diverge or oscillate around the optimal solution, resulting in poor performance and accuracy.

Increasing the dimensionality of the dense layer next to the flatten layer would not help with the overfitting problem, as it would make the model more complex and increase the number of parameters to be learned. A more complex model can fit the training data better, but it can also memorize the noise and irrelevant details in the data, leading to overfitting and poor generalization.

Increasing the epoch number would not help with the overfitting problem, as it would make the model train longer and more likely to overfit the training data. A high epoch number can cause the model to converge to the global minimum, but it can also cause the model to over-optimize the training data and lose the ability to generalize to new data.

Dropout: A Simple Way to Prevent Neural Networks from Overfitting

How to Reduce Overfitting With Dropout Regularization in Keras

How to Control the Stability of Training Neural Networks With the Learning Rate

How to Choose the Number of Hidden Layers and Nodes in a Feedforward Neural Network?

How to decide the optimal number of epochs to train a neural network?

The model in Experiment 1 had a high variance error because it performed well on the training data (train error = 5%) but poorly on the test data (test error = 8%). This indicates that the model was overfitting the training data and not generalizing well to new data. The model in Experiment 3 had a lower variance error because it performed similarly on the training data (train error = 5.1%) and the test data (test error = 5.4%). This indicates that the model was more robust and less sensitive to the fluctuations in the training data. The model in Experiment 3 achieved this improvement by implementing regularization, which is a technique that reduces the complexity of the model and prevents overfitting by adding a penalty term to the loss function. The model in Experiment 2 had a minimal bias error because it performed similarly on the training data (train error = 5.2%) and the test data (test error = 5.7%) as the model in Experiment 1. This indicates that the model was not underfitting the data and capturing the true relationship between the input and output variables. The model in Experiment 2 increased the number of layers and neurons in the model, which is a way to increase the complexity and flexibility of the model. However, this did not improve the performance of the model, as the variance error remained high. This shows that increasing the complexity of the model is not always the best way to reduce the bias error, and may even increase the variance error if the model becomes too complex for the data. References:

Bias Variance Tradeoff - Clearly Explained - Machine Learning Plus

The Bias-Variance Trade-off in Machine Learning - Stack Abuse

Multiple imputation is a technique that uses machine learning to generate multiple plausible values for each missing value in a dataset, based on the observed data and the relationships among the variables. Multiple imputation preserves the integrity of the dataset by accounting for the uncertainty and variability of the missing data, and avoids the bias and loss of information that may result from other methods, such as listwise deletion, last observation carried forward, or mean substitution. Multiple imputation can improve the accuracy and validity of statistical analysis and machine learning models that use the imputed dataset. References:

Managing missing values in your target and related datasets with automated imputation support in Amazon Forecast

Imputation by feature importance (IBFI): A methodology to impute missing data in large datasets

Multiple Imputation by Chained Equations (MICE) Explained

AWS Glue is a serverless data integration service that can catalogue, clean, enrich, and move data between various data stores. Amazon Athena is an interactive query service that can run SQL queries on data stored in Amazon S3. By using AWS Glue to catalogue the data and Amazon Athena to run queries, the Machine Learning Specialist can leverage the existing data in Amazon S3 without any additional data transformation or loading. This solution requires the least effort compared to the other options, which involve more complex and costly data processing and storage services. References: AWS Glue, Amazon Athena

 A convolutional neural network (CNN) is a type of machine learning algorithm that is suitable for image classification tasks. A CNN consists of multiple layers that can extract features from images and learn to recognize patterns and objects. A CNN can also use transfer learning to leverage pre-trained models that have been trained on large-scale image datasets, such as ImageNet, and fine-tune them for specific tasks, such as detecting the company’s retail brand. A CNN can achieve high accuracy and performance for image classification problems, as it can handle complex and diverse images and reduce the dimensionality and noise of the input data. A CNN can be implemented using various frameworks and libraries, such as TensorFlow, PyTorch, Keras, MXNet, etc12

The other options are not valid or relevant for the image classification task. Latent Dirichlet Allocation (LDA) is a type of machine learning algorithm that is suitable for topic modeling tasks. LDA can discover the hidden topics and their proportions in a collection of text documents, such as news articles, tweets, reviews, etc. LDA is not applicable for image data, as it requires textual input and output. LDA can be implemented using various frameworks and libraries, such as Gensim, Scikit-learn, Mallet, etc34

Recurrent neural network (RNN) is a type of machine learning algorithm that is suitable for sequential data tasks. RNN can process and generate data that has temporal or sequential dependencies, such as natural language, speech, audio, video, etc. RNN is not optimal for image data, as it does not capture the spatial features and relationships of the pixels. RNN can be implemented using various frameworks and libraries, such as TensorFlow, PyTorch, Keras, MXNet, etc.

K-means is a type of machine learning algorithm that is suitable for clustering tasks. K-means can partition a set of data points into a predefined number of clusters, based on the similarity and distance between the data points. K-means is not suitable for image classification tasks, as it does not learn to label the images or detect the objects of interest. K-means can be implemented using various frameworks and libraries, such as Scikit-learn, TensorFlow, PyTorch, etc.

 The best feature engineering technique to speed up the model training time without losing a lot of information from the original dataset is to use an autoencoder or principal component analysis (PCA) to replace original features with new features. An autoencoder is a type of neural network that learns a compressed representation of the input data, called the latent space, by minimizing the reconstruction error between the input and the output. PCA is a statistical technique that reduces the dimensionality of the data by finding a set of orthogonal axes, called the principal components, that capture the maximum variance of the data. Both techniques can help reduce the number of features and remove the noise and redundancy in the data, which can improve the model performance and speed up the training process. References:

AWS Machine Learning Specialty Exam Guide

AWS Machine Learning Training - Dimensionality Reduction for Machine Learning

AWS Machine Learning Training - Deep Learning with Amazon SageMaker

 Amazon Comprehend is a natural language processing (NLP) service that can analyze text and extract insights such as sentiment, entities, topics, and language. Amazon Comprehend also provides custom classification and custom entity recognition features that allow users to train their own models using their own data and labels. For the scenario of routing customer claims to different queues based on categories, Amazon Comprehend custom classification is a suitable solution. The custom classifier can be trained using a .csv file that contains the claim text and the claim label as columns. The custom classifier can then be used to process incoming claims and predict the labels using the Amazon Comprehend API. The predicted labels can be used to route the claims to the appropriate queue. This solution does not require any machine learning expertise or model deployment, and it can be easily integrated with the existing application.

The other options are not suitable because:

Option A: Amazon SageMaker Object2Vec is an algorithm that can learn embeddings of objects such as words, sentences, or documents. It can be used for tasks such as text classification, sentiment analysis, or recommendation systems. However, using this algorithm requires machine learning expertise and model deployment using SageMaker, which are not available for the company.

Option B: Amazon SageMaker Latent Dirichlet Allocation (LDA) is an algorithm that can discover the topics or themes in a collection of documents. It can be used for tasks such as topic modeling, document clustering, or text summarization. However, using this algorithm requires machine learning expertise and model deployment using SageMaker, which are not available for the company. Moreover, LDA does not provide labels for the topics, but rather a distribution of words for each topic, which may not match the existing categories of the claims.

Option C: Amazon Textract is a service that can extract text and data from scanned documents or images. It can be used for tasks such as document analysis, data extraction, or form processing. However, using this service is unnecessary and inefficient for the scenario, since the company already has the claim text and label in a database. Moreover, Amazon Textract does not provide custom classification features, so it cannot be used to train a custom classifier using the existing data and labels.

Amazon Comprehend Custom Classification

Amazon SageMaker Object2Vec

Amazon SageMaker Latent Dirichlet Allocation

Amazon Textract

 To improve the training speed of a time-series forecasting model using TensorFlow, the Machine Learning Specialist should change the TensorFlow code to implement a Horovod distributed framework supported by Amazon SageMaker. Horovod is a free and open-source software framework for distributed deep learning training using TensorFlow, Keras, PyTorch, and Apache MXNet1. Horovod can scale up to hundreds of GPUs with upwards of 90% scaling efficiency2. Horovod is easy to use, as it requires only a few lines of Python code to modify an existing training script2. Horovod is also portable, as it runs the same for TensorFlow, Keras, PyTorch, and MXNet; on premise, in the cloud, and on Apache Spark2.

Amazon SageMaker is a fully managed service that provides every developer and data scientist with the ability to build, train, and deploy machine learning models quickly3. Amazon SageMaker supports Horovod as a built-in distributed training framework, which means that the Machine Learning Specialist does not need to install or configure Horovod separately4. Amazon SageMaker also provides a number of features and tools to simplify and optimize the distributed training process, such as automatic scaling, debugging, profiling, and monitoring4. By using Amazon SageMaker, the Machine Learning Specialist can parallelize the training to as many machines as needed to achieve the business goals, while minimizing coding effort and infrastructure changes.

1: Horovod (machine learning) - Wikipedia

2: Home - Horovod

3: Amazon SageMaker – Machine Learning Service – AWS

4: Use Horovod with Amazon SageMaker - Amazon SageMaker

 A receiver operating characteristic (ROC) curve is a model evaluation technique that can be used to understand how different classification thresholds will impact the model’s performance. A ROC curve plots the true positive rate (TPR) against the false positive rate (FPR) for various values of the classification threshold. The TPR, also known as sensitivity or recall, is the proportion of positive instances that are correctly classified as positive. The FPR, also known as the fall-out, is the proportion of negative instances that are incorrectly classified as positive. A ROC curve can show the trade-off between the TPR and the FPR for different thresholds, and help the Machine Learning Specialist to select the optimal threshold that maximizes the TPR and minimizes the FPR. A ROC curve can also be used to compare the performance of different models by calculating the area under the curve (AUC), which is a measure of how well the model can distinguish between the positive and negative classes. A higher AUC indicates a better model