Amazon Web Services AIF-C01 Dumps Questions Answers

In generative AI models, such as those built on Amazon Bedrock, inference costs are driven by the number of tokens processed. A token can be as short as one character or as long as one word, and the more tokens consumed during the inference process, the higher the cost.

Option A (Correct): "Number of tokens consumed": This is the correct answer because the inference cost is directly related to the number of tokens processed by the model.

Option B: "Temperature value" is incorrect as it affects the randomness of the model's output but not the cost directly.

Option C: "Amount of data used to train the LLM" is incorrect because training data size affects training costs, not inference costs.

Option D: "Total training time" is incorrect because it relates to the cost of training the model, not the cost of inference.

AWS AI Practitioner References:

Understanding Inference Costs on AWS: AWS documentation highlights that inference costs for generative models are largely based on the number of tokens processed.

Foundation models have a context window (max tokens), which limits the size of the input text (prompt + instructions).

If the input (e.g., a very long book) exceeds this limit, the model cannot process it, causing failure.

Temperature (A) and Top P (C) control randomness, not input size.

Fine-tuning (B) is irrelevant to input truncation failures.

???? Reference:

AWS Documentation – Amazon Bedrock Model Parameters (context size limits)

Exploratory data analysis (EDA) involves understanding the data by visualizing it, calculating statistics, and creating correlation matrices. This stage helps identify patterns, relationships, and anomalies in the data, which can guide further steps in the ML pipeline.

Option C (Correct): "Exploratory data analysis": This is the correct answer as the tasks described (correlation matrix, calculating statistics, visualizing data) are all part of the EDA process.

Option A: "Data pre-processing" is incorrect because it involves cleaning and transforming data, not initial analysis.

Option B: "Feature engineering" is incorrect because it involves creating new features from raw data, not analyzing the data's existing structure.

Option D: "Hyperparameter tuning" is incorrect because it refers to optimizing model parameters, not analyzing the data.

AWS AI Practitioner References:

Stages of the Machine Learning Pipeline: AWS outlines EDA as the initial phase of understanding and exploring data before moving to more specific preprocessing, feature engineering, and model training stages.