AWS Certified AI Practitioner AIF-C01 Updated Exam

The correct answer is C because Amazon Bedrock with On-Demand Throughput is designed for variable or unpredictable workloads. It allows you to pay only for what you use, which is ideal for early-stage or pilot-phase generative AI applications.

From AWS documentation:

" On-Demand Throughput in Amazon Bedrock provides flexibility and cost-efficiency for unpredictable or low-volume generative AI applications. You are charged based on the number of input/output tokens processed, with no need to provision dedicated capacity. "

Explanation of other options:

A. Using GPU-powered EC2 instances requires managing infrastructure and is generally more expensive, especially for inconsistent usage.

B. Provisioned Throughput is best suited for predictable, high-volume production workloads where guaranteed throughput is needed.

D. SageMaker JumpStart is for training and deploying ML models, not for efficient inference of foundation models.

Referenced AWS AI/ML Documents and Study Guides:

Amazon Bedrock Pricing and Throughput Models

AWS Generative AI Best Practices – Cost Optimization

AWS ML Specialty Guide – Bedrock Deployment Models

Comprehensive and Detailed Explanation from AWS AI Documents:

Classification requires predefined labels (categories). The company explicitly does not want to define categories.

Regression (linear or logistic) predicts numerical values or probabilities, not groups.

Clustering is an unsupervised learning technique that groups similar data points together based on their features without needing labeled categories.

AWS defines clustering as:

“Clustering is an unsupervised machine learning algorithm that automatically groups data points into clusters based on their similarities.”

This makes Clustering the correct choice for segmenting customers into groups without predefined labels.

???? Reference:

AWS ML Glossary – Clustering

Amazon Bedrock provides an option to enable invocation logging to capture and store the input and output data of the models used. This is essential for monitoring and auditing purposes, particularly when handling customer data.

Option B (Correct): " Enable invocation logging in Amazon Bedrock " : This is the correct answer as it directly enables the logging of all model invocations, ensuring transparency and traceability.

Option A: " Configure AWS CloudTrail " is incorrect because CloudTrail logs API calls but does not provide specific logging for model inputs and outputs.

Option C: " Configure AWS Audit Manager " is incorrect as Audit Manager is used for compliance reporting, not specific invocation logging for AI models.

Option D: " Configure model invocation logging in Amazon EventBridge " is incorrect as EventBridge is for event-driven architectures, not specifically designed for logging AI model inputs and outputs.

AWS AI Practitioner References:

Amazon Bedrock Logging Capabilities: AWS emphasizes using built-in logging features in Bedrock to maintain data integrity and transparency in model operations.

The verified answer is B. Recall. In a Retrieval Augmented Generation system, the retrieval component must find the documents or passages that contain the information needed to answer a user query. The question specifically says the company wants to retrieve all relevant documents and avoid missing important information. That wording points directly to recall. Recall measures how many of the relevant items were successfully retrieved out of all relevant items that should have been retrieved. In practical RAG evaluation, high recall means the retriever is less likely to omit important source documents from the context sent to the language model.

Faithfulness is incorrect because it evaluates whether the generated answer is grounded in, and consistent with, the retrieved context. It is a generation-quality metric, not the primary retrieval-coverage metric. A response can be faithful to retrieved context even if the retriever failed to include some important documents. Mean reciprocal rank is incorrect because it measures how highly the first relevant result appears in the ranked list. It is useful for ranking quality, but it does not directly confirm that all relevant documents were retrieved. Precision is incorrect because precision measures how many retrieved documents are actually relevant. High precision reduces irrelevant results, but it can still miss relevant documents if the retriever returns only a narrow subset.

AWS documentation separates retrieval-focused evaluation metrics from response-generation metrics. For knowledge base retrieval-only evaluations, AWS lists built-in retrieval metrics such as context relevance and context coverage, while response generation evaluations include metrics such as faithfulness, correctness, completeness, and helpfulness. This distinction supports the principle that retrieval coverage and not missing relevant information is a retrieval-side concern. In standard information retrieval terminology, the metric that best captures “do not miss relevant documents” is recall.