Professional-Data-Engineer Google Professional Data Engineer Exam Questions and Answers

The data is first comes to buffer and then written to Storage. If we are running queries in buffer we will face above mentioned issues. If we wait for the bigquery to write the data to storage then we won’t face the issue. So We need to wait till it’s written tio storage

To architect a data transformation solution for BigQuery that aligns with the ELT development technique and provides an intuitive coding environment for SQL-proficient developers, Dataform is an optimal choice. Here’s why:

ELT Development Technique:

ELT (Extract, Load, Transform) is a process where data is first extracted and loaded into a data warehouse, and then transformed using SQL queries. This is different from ETL, where data is transformed before being loaded into the data warehouse.

BigQuery supports ELT, allowing developers to write SQL transformations directly in the data warehouse.

Dataform:

Dataformis a development environment designed specifically for data transformations in BigQuery and other SQL-based warehouses.

It provides tools for managing SQL as code, including version control and collaborative development.

Dataform integrates well with existing development workflows and supports scheduling and managing SQL-based data pipelines.

Intuitive Coding Environment:

Dataform offers an intuitive and user-friendly interface for writing and managing SQL queries.

It includes features like SQLX, a SQL dialect that extends standard SQL with features for modularity and reusability, which simplifies the development of complex transformation logic.

Managing SQL as Code:

Dataform supports version control systems like Git, enabling developers to manage their SQL transformations as code.

This allows for better collaboration, code reviews, and version tracking.

Reference Links:

Dataform Documentation

BigQuery Documentation

Managing ELT Pipelines with Dataform

To ensure that important legal hold documents in a Cloud Storage bucket are not deleted or modified, the most effective method is to set and lock a retention policy. Here’s why this is the best choice:

Retention Policy:

A retention policy defines a retention period during which objects in the bucket cannot be deleted or modified. This ensures data immutability.

Once a retention policy is set and locked, it cannot be removed or reduced, providing strong protection against accidental or malicious deletions.

Locking the Retention Policy:

Locking a retention policy ensures that the retention period cannot be changed. This action is permanent and guarantees that the specified retention period will be enforced.

Steps to Implement:

Set the Retention Policy:

Define a retention period for the bucket to ensure that all objects are protected for the required duration.

Lock the Retention Policy:

Lock the retention policy to prevent any modifications, ensuring the immutability of the documents.

Reference Links:

Cloud Storage Retention Policy Documentation

How to Set a Retention Policy

To create a robust and resilient SQL pipeline in BigQuery that handles retries and failure notifications, consider the following:

BigQuery Scheduled Queries: This feature allows you to schedule recurring queries in BigQuery. It is a straightforward way to run SQL transformations on a regular basis without requiring extensive setup.

Error Handling and Retries: While BigQuery Scheduled Queries can run at specified intervals, they don't natively support complex retry logic or failure notifications directly. This is where additional Google Cloud services like Pub/Sub and Cloud Functions come into play.

Pub/Sub for Notifications: By configuring a BigQuery scheduled query to publish messages to a Pub/Sub topic upon failure, you can create a decoupled and scalable notification system.

Cloud Functions: Cloud Functions can subscribe to the Pub/Sub topic and implement logic to count consecutive failures. After detecting three consecutive failures, the Cloud Function can then send an email notification using a service like SendGrid or Gmail API.

Implementation Steps:

Set up a BigQuery Scheduled Query:

Create a scheduled query in BigQuery to run your SQL transformation every two hours.

Configure the scheduled query to publish a notification to a Pub/Sub topic in case of a failure.

Create a Pub/Sub Topic:

Create a Pub/Sub topic that will receive messages from the scheduled query.

Develop a Cloud Function:

Write a Cloud Function that subscribes to the Pub/Sub topic.

Implement logic in the Cloud Function to track failure messages. If three consecutive failure messages are detected, the function sends an email notification.

Reference Links:

BigQuery Scheduled Queries

Pub/Sub Documentation

Cloud Functions Documentation

SendGrid Email API

Gmail API

Comprehensive and Detailed Explanation:

The core requirement is to make specific data values permanently unreadable, a concept often referred to as cryptographic erasure or "crypto-shredding."

Option C is the correct answer because it achieves this at a granular, field-level. BigQuery's AEAD (Authenticated Encryption with Associated Data) functions allow you to encrypt individual field values within your tables using a keyset from Cloud KMS. You can encrypt the sensitive data, store the encrypted value in BigQuery, and when an employee leaves, you simply destroy the specific KMS key version used for their data. Once the key is destroyed, the encrypted data is permanently unrecoverable, thus rendering it unreadable.

Option A and B are incorrect because data masking and column-level access controls are about controlling access to the data, not about making the underlying data itself unreadable. If an administrator with sufficient privileges were to query the table, the data would still be there in its original form. Revoking permissions prevents a specific user from seeing the data, but it doesn't erase it.

Option D is incorrect because Customer-Managed Encryption Keys (CMEK) operate at the entire table level. Deleting a CMEK key would make the entire table unreadable, which is far too broad and disruptive. The goal is to target specific sensitive values, not destroy whole tables.

Reference (Google Cloud Documentation Concepts):

This solution leverages field-level encryption within BigQuery. The official documentation for "Protecting data with Cloud KMS keys" states that BigQuery's AEAD functions (AEAD.ENCRYPT and AEAD.DECRYPT) are the intended mechanism for this purpose. This approach provides fine-grained control, allowing you to manage the lifecycle of encrypted data by managing the lifecycle of the encryption keys in Cloud Key Management Service (Cloud KMS). The act of destroying a key version is the mechanism for crypto-shredding.

The BigQuery Data Viewer role allows users to read data and metadata from tables and views, but not to modify or delete them. By giving analysts this role on the shared dataset, you canensure that they can access the data for analysis, but not change it. The BigQuery Data Editor role allows users to create, update, and delete tables and views, as well as read and write data. By giving analysts this role at the dataset level for their assigned dataset, you can provide them with individual workspaces where they can store their own tables and views, without affecting the shared dataset or other analysts’ datasets. This way, you can achieve both data protection and data isolation for your team. References:

BigQuery IAM roles and permissions

Basic roles and permissions

To simulate a Redis instance failover in a production-like environment without impacting production data, the best approach is to use a development environment. Here’s why option D is the best choice:

Standard Tier Memorystore for Redis:

The Standard Tier provides high availability and automatic failover capabilities. It’s suitable for testing failover scenarios in a controlled environment.

Development Environment:

Using a development environment ensures that any potential data loss or impact from the failover simulation does not affect production data, maintaining the integrity and availability of the production system.

Limited-Data-Loss Mode:

The limited-data-loss mode for manual failover ensures that data loss is minimized during the failover process, making it a realistic simulation of a production failover scenario.

Steps to Implement:

Create a Development Environment:

Set up a development environment with a Standard Tier Memorystore for Redis instance that mirrors the configuration of your production instance.

Initiate Manual Failover:

Initiate a manual failover using the limited-data-loss data protection mode to simulate a failover scenario:

gcloud redis instances failover INSTANCE_ID --data-protection-mode=limited-data-loss

Verify Failover:

Monitor and verify the failover process to ensure it behaves as expected, simulating the disaster recovery scenario accurately.

Reference Links:

Memorystore for Redis Documentation

Manual Failover in Memorystore

Dataprep is a low code, serverless, and fully managed service that allows users to visually explore, cleanse, and validate data in Cloud Storage. It also provides features such as data profiling, data quality, data transformation, and data lineage. Dataprep is integrated with BigQuery, so users can easily export the prepared data to BigQuery for further analysis or modeling. Dataprep is a suitable solution for the data science team to quickly and easily work with the data in Cloud Storage, without having to write code or manage infrastructure. The other options are not as suitable as Dataprep for this use case, because they either require more coding, more infrastructure management, or more data movement. Loading the data into BigQuery, either directly or through Dataflow, would incur additional costs and latency, and may not provide the same level of data exploration and validation as Dataprep. Creating an external table in BigQuery would allow users to query the data in Cloud Storage, but would not provide the same level of data cleansing and transformation as Dataprep. References:

Dataprep overview

Dataprep features

Dataprep and BigQuery integration

To identify performance bottlenecks within a specific operation like a JOIN, you need to see the "Execution Details" or the Execution Graph. This tool provides a visual representation of the query plan, showing each stage of the query (Input, Compute, Output) and where the most time or resources were consumed.

Why C is correct: The Query History (or Job History) in the BigQuery console allows you to select a completed job and view its Execution Graph. This graph explicitly breaks down performance by stage, showing wait times, read times, and compute times. It is specifically designed to identify "bottlenecks" such as Data Skew or Shuffle issues during JOIN operations.

Correcting other options:

A (Audit Logs): Cloud Logging provides high-level metadata (who ran the query, when, and how much it cost), but it does not provide the granular execution plan details required to diagnose a JOIN bottleneck.

B (INFORMATION_SCHEMA): While this view provides great historical data for overall job performance (like total bytes or duration), it is tabular and less efficient than the visual Execution Graph for quickly spotting which specific stage of a complex JOIN is slow.

D (Dry Run): A dry run only estimates the number of bytes that will be read and checks syntax. It does not actually execute the query or generate a performance plan based on real data distribution, so it cannot identify execution bottlenecks.

This option will allow you to store the data in a low-cost storage option, as the archive storage class has the lowest price per GB among the Cloud Storage classes. It will also ensure that the data is immutable for 3 years, as the locked retention policy prevents the deletion or overwriting of the data until the retention period expires. You can still query the data using SQL by creating a BigQuery external table that references the exported files in the Cloud Storage bucket. Option A is incorrect because creating a BigQuery table clone will not reduce the storage costs, as the clone will have the same size and storage class as the original table. Option B is incorrect because creating a BigQuery table snapshot will also not reduce the storage costs, as the snapshot will have the same size and storage class as the original table. Option C is incorrect because enabling versioning on the bucket will not make the data immutable, as the versions can still be deleted or overwritten by anyone with the appropriate permissions. It will also increase the storage costs, as each version of the file will be charged separately. References:

Exporting table data | BigQuery | Google Cloud

Storage classes | Cloud Storage | Google Cloud

Retention policies and retention periods | Cloud Storage | Google Cloud

Federated queries | BigQuery | Google Cloud

- Autoclass automatically moves objects between storage classes without impacting performance or availability, nor incurring retrieval costs. - It continuously optimizes storage costs based on access patterns without the need to set specific lifecycle management policies.

The key requirements for sharing the sales dataset are:

Self-serving for other business units.

Low-maintenance.

Cost-effective.

Sharing with other business units (implying potentially different projects).

Analytics Hub (Option C) is designed precisely for this purpose of sharing data assets (like datasets) in a governed, discoverable, and self-service manner across an organization and even externally.

Self-Serving: Consumers (other business units) can browse available datasets in an exchange and subscribe to them. This makes it easy for them to discover and access the data they need without manual intervention from the data provider for each request.

Low-Maintenance for Provider: Once a dataset is published as a "listing" in Analytics Hub, the provider doesn't need to manage individual access requests or data copying for each new consumer project that subscribes. Updates to the source dataset are reflected for subscribers.

Cost-Effective:No Data Duplication for Sharing: When a dataset is shared via Analytics Hub, subscribers query the data directly from the provider's project (unless the provider explicitly opts for a replicated dataset model, which is less common for internal sharing where live access is preferred). This avoids storage costs associated with duplicating large datasets in multiple projects.

Query Costs: Query costs are typically borne by the subscriber's project.

Governed Sharing: Analytics Hub provides a centralized way to manage and audit data sharing.

Let's analyze why other options are less suitable:

A (Enable access to authorized views): Authorized views are a good way to share specific slices or aggregations of data without exposing the underlying tables. However, managing authorizations for potentially many views across many business units/projects can become less "self-serving" and more "low-maintenance" than a dedicated data exchange platform. Discoverability is also less centralized.

B (BigQuery Data Transfer Service to copy): This creates data copies, which increases storage costs and can lead to data staleness if the copy schedule isn't frequent enough. It's not "low-maintenance" as it requires managing DTS jobs and storage for copies. It's generally not the most cost-effective way to share for querying.

D (Create and share views with users): Similar to authorized views, but sharing directly with individual users can be a permissions management challenge at scale compared to project-level or group-level subscriptions facilitated by Analytics Hub. It lacks the "exchange" concept for discovery and self-service subscription by business units/projects.

Object Lifecycle Management (OLM) is the correct and fully managed feature in Cloud Storage designed to automate transitions between storage classes and object deletion based on rules like object age, which directly addresses the scenario's requirements for cost control and retention policy.

Cost Control and Rarely Accessed Data: The Archive storage class offers the lowest storage cost, which is ideal for data accessed less than once a year (data older than two years is "rarely accessed"). OLM can automate the move to this class.

Retention Policy: OLM rules can include a Delete action based on a condition like object age, ensuring the "no longer than seven years" retention policy is enforced automatically.

Correcting other options:

A (Custom Script): This is a custom, unmanaged, and potentially complex solution that would require maintenance and incur Compute Engine costs. OLM is the native, fully managed feature.

C (Autoclass): Autoclass automatically manages storage classes to optimize costs, but it generally defaults to Standard for new uploads and transitions data based on access patterns. While it is a managed service, explicitly defining a rule with a deletion policy (after seven years) and the specific transition at two years is best achieved and guaranteed with an OLM rule.

D (Replication): Replicating to a different region increases complexity and typically incurs data transfer and duplicate storage costs, which works against the goal of controlling rising storage costs.

Selecting and crafting the right set of feature columns is key to learning an effective model.

Bucketization is a process of dividing the entire range of a continuous feature into a set of consecutive bins/buckets, and then converting the original numerical feature into a bucket ID (as a categorical feature) depending on which bucket that value falls into.

Using each base feature column separately may not be enough to explain the data. To learn the differences between different feature combinations, we can add crossed feature columns to the model.

When collecting and grouping data into windows, Beam uses triggers to determine when to emit the aggregated results of each window.

Processing time triggers. These triggers operate on the processing time – the time when the data element is processed at any given stage in the pipeline.

Event time triggers. These triggers operate on the event time, as indicated by the timestamp on each data element. Beam’s default trigger is event time-based.

A general guide is to, keep your row keys reasonably short. Long row keys take up additional memory and storage and increase the time it takes to get responses from the Cloud Bigtable server.

Denormalization increases query speed for tables with billions of rows because BigQuery's performance degrades when doing JOINs on large tables, but with a denormalized data

structure, you don't have to use JOINs, since all of the data has been combined into one table. Denormalization also makes queries simpler because you do not have to use JOIN clauses.

Denormalization increases the amount of data processed and the amount of storage required because it creates redundant data.

The flaw with evaluating a predictive model on training data is that it does not inform you on how well the model has generalized to new unseen data. A model that is selected for its accuracy on the training dataset rather than its accuracy on an unseen test dataset is very likely to have lower accuracy on an unseen test dataset. The reason is that the model is not as generalized. It has specialized to the structure in the training dataset. This is called overfitting.

Cloud Bigtable is a sparsely populated table that can scale to billions of rows and thousands of columns, allowing you to store terabytes or even petabytes of data. A single value in each row is indexed; this value is known as the row key.

In Google Cloud dataflow SDK, you can use the ParDo to extract only a customer name of each

element in your PCollection.

Partitioned tables include a pseudo column named _PARTITIONTIME that contains a date-based timestamp for data loaded into the table. To limit a query to particular partitions (such as Jan 1st and 2nd of 2017), use a clause similar to this:

WHERE _PARTITIONTIME BETWEEN TIMESTAMP('2017-01-01') AND TIMESTAMP('2017-01-02')

The following rules will apply when you use preemptible workers with a Cloud Dataproc cluster:

Processing only—Since preemptibles can be reclaimed at any time, preemptible workers do not store data. Preemptibles added to a Cloud Dataproc cluster only function as processing nodes.

No preemptible-only clusters—To ensure clusters do not lose all workers, Cloud Dataproc cannot create preemptible-only clusters.

Persistent disk size—As a default, all preemptible workers are created with the smaller of 100GB or the primary worker boot disk size. This disk space is used for local caching of data and is not available through HDFS.

The managed group automatically re-adds workers lost due to reclamation as capacity permits.

One of the advantages of Cloud Dataproc is its low cost. Dataproc charges for what you really use with minute-by-minute billing and a low, ten-minute-minimum billing period.

A Cloud Dataproc Viewer is limited in its actions based on its role. A viewer can only list clusters, get cluster details, list jobs, get job details, list operations, and get operation details.

You can load data with nested and repeated fields using the Web UI.

You cannot use the Web UI to:

- Upload a file greater than 10 MB in size

- Upload multiple files at the same time

- Upload a file in SQL format

All three of the above operations can be performed using the "bq" command.

Cloud Bigtable is Google's NoSQL Big Data database service. It is the same database that Google uses for services, such as Search, Analytics, Maps, and Gmail.

It is used for requirements that are low latency and high throughput including Internet of Things (IoT), user analytics, and financial data analysis.

Triggers control when the elements for a specific key and window are output. As elements arrive, they are put into one or more windows by a Window transform and its associated WindowFn, and then passed to the associated Trigger to determine if the Windows contents should be output.

If model parameters are variables that get adjusted by training with existing data, your hyperparameters are the variables about the training process itself. For example, part of setting up a deep neural network is deciding how many "hidden" layers of nodes to use between the input layer and the output layer, as well as how many nodes each layer should use. These variables are not directly related to the training data at all. They are configuration variables. Another difference is that parameters change during a training job, while the hyperparameters are usually constant during a job.

Weights and biases are variables that get adjusted during the training process, so they are not hyperparameters.

When you apply a BigQueryIO.Write transform in batch mode to write to a single table, Dataflow invokes a BigQuery load job. When you apply a BigQueryIO.Write transform in streaming mode or in batch mode using a function to specify the destination table, Dataflow uses BigQuery's streaming inserts

If you split a table into multiple tables (such as one table for each day), then you can limit your query to the data in specific tables (such as for particular days). A better method is to use a partitioned table, as long as your data can be separated by the day.

If you use the LIMIT clause, BigQuery will still process the entire table.

You do not set a query language for each dataset. It is set each time you run a query and the default query language is Legacy SQL.

Standard SQL has been the preferred query language since BigQuery 2.0 was released.

In legacy SQL, to query a table with a project-qualified name, you use a colon, :, as a separator. In standard SQL, you use a period, ., instead.

Due to the differences in syntax between the two query languages (such as with project-qualified table names), if you write a query in Legacy SQL, it might generate an error if you try to run it with Standard SQL.

There are three major kinds of triggers that Dataflow supports: 1. Time-based triggers 2. Data-driven triggers. You can set a trigger to emit results from a window when that window has received a certain number of data elements. 3. Composite triggers. These triggers combine multiple time-based or data-driven triggers in some logical way

For example, if you plan to store extensive historical data for a large number of remote-sensing devices and then use the data to generate daily reports, the cost savings for HDD storage may justify the performance tradeoff. On the other hand, if you plan to use the data to display a real-time dashboard, it probably would not make sense to use HDD storage—reads would be much more frequent in this case, and reads are much slower with HDD storage.