Amazon Web Services AIP-C01 Based on Real Exam Environment

Option A is the best solution because it satisfies streaming, token control, and retry requirements while keeping operational overhead low by using fully managed, serverless AWS services. Amazon API Gateway HTTP APIs provide a lightweight, cost-effective front door for APIs and integrate cleanly with AWS Lambda for request processing and security controls.

AWS Lambda response streaming allows the API to begin returning content to the client as soon as partial model output is available, reducing perceived latency and improving user experience for long responses. Using Lambda as the integration layer also provides a centralized place to enforce token-aware request handling, such as rejecting oversized requests, truncating optional context, or applying consistent limits across users and tenants to manage compute usage.

Retry logic is best handled in the client or integration layer for transient failures such as timeouts and throttling. Lambda can implement controlled retries with exponential backoff and jitter, while API Gateway timeouts help bound request lifetimes and prevent hung connections from consuming resources indefinitely. Because the model service is managed, the company avoids infrastructure management and focuses only on request shaping, safety, and resiliency behavior.

Option B is not suitable because client-side polling is not true streaming, front-end token enforcement is insecure and inconsistent, and API Gateway does not provide model-aware retry behavior on its own. Option C introduces container hosting and scaling complexity, which increases operational overhead compared to serverless. Option D can work, but REST APIs are generally heavier than HTTP APIs for this pattern and do not reduce overhead compared to Option A.

Therefore, Option A provides the required streaming and resiliency capabilities with the least infrastructure management effort.

Option B is correct because it uses the managed evaluation capability in Amazon Bedrock that is intended specifically for comparing foundation models using a consistent prompt set and producing structured results with minimal custom tooling. In a Bedrock evaluation workflow, you provide an input dataset of prompts, typically in JSON Lines format so each line represents one evaluation record. Storing the JSONL file in Amazon S3 allows Bedrock to read the dataset at scale and write standardized evaluation outputs back to S3 for downstream analysis, sharing, and retention.

The key requirement is to assess both quality and safety across multiple models. A Bedrock evaluation job can use a judge model to score the generated outputs against defined criteria. This approach supports repeatable, apples-to-apples comparisons because the same judge model and scoring rubric can be applied to every candidate foundation model. The candidate models are configured as generators, meaning each evaluation job run uses one selected FM to produce answers for the same prompt set, and the judge model evaluates those answers. That matches the requirement to generate evaluation reports that help a data scientist select the best FM.

Option A does not use Bedrock evaluation jobs, and a knowledge base plus RetrieveAndGenerate is a RAG pattern, not an evaluation framework. It would produce responses but not standardized scoring and reporting suitable for model selection. Option C is incorrect because Bedrock evaluation outputs are delivered to S3, not directly to a BI destination, and selecting the candidate FM as the evaluator conflicts with the intended pattern of using a stable judge model. Option D misuses knowledge bases and retrieval evaluation types when the requirement is prompt-based model assessment rather than evaluating retrieval quality.