Free Amazon Web Services AIP-C01 Practice Exam with Questions & Answers | Set: 2

Option C is the correct solution because it provides a secure, scalable, and standards-compliant way to expose an MCP server to an AI agent while enforcing strong user authorization. The Model Context Protocol supports HTTP-based transports for remote MCP servers, making Streamable HTTP the appropriate choice when the server is hosted as a managed service rather than a local process.

Hosting the MCP server in AWS Lambda enables automatic scaling and cost-efficient execution. By placing Amazon API Gateway in front of the Lambda function, the company creates a secure, managed HTTP endpoint that the AI agent can invoke reliably. This architecture cleanly separates transport, authentication, and business logic, which aligns with AWS serverless best practices.

Using Amazon Cognito to enforce OAuth 2.1 ensures that only authenticated and authorized users can access the MCP server. This satisfies security and compliance requirements when the MCP server handles sensitive user information. Cognito integrates natively with API Gateway, removing the need for custom authentication logic and reducing operational overhead.

Option A lacks user-level authorization controls. Option B and Option D rely on STDIO transport, which is intended for local or tightly coupled processes and is not suitable for distributed, serverless architectures. Option D also introduces security risks by handling credentials through environment variables.

Therefore, Option C best meets the requirements for secure access control, scalability, and correct MCP integration in an AWS-based AI agent architecture.

Option B is the correct evaluation configuration because it enables end-to-end assessment of both retrieval and generation quality while supporting direct comparison of chunking strategies and foundation models. Amazon Bedrock evaluation jobs are designed to support RAG workflows by evaluating how well retrieved context supports accurate and high-quality model outputs.

A retrieve-and-generate evaluation job evaluates the complete RAG pipeline, not just retrieval. This is essential for medical information use cases, where both the relevance of retrieved content and the correctness of generated responses directly impact user safety and trust. Including multiple chunking strategies in the evaluation dataset allows side-by-side comparison under identical prompts and conditions.

Custom precision-at-k metrics measure how effectively the retrieval component surfaces relevant chunks, while an LLM-as-a-judge metric provides qualitative scoring of generated responses. Using a numeric scale enables consistent, repeatable evaluation and supports automated quality gates. Amazon Bedrock supports LLM-based evaluators to score dimensions such as accuracy, completeness, and relevance.

Using the same evaluator model to assess outputs from both FMs ensures consistent scoring and eliminates evaluator bias. This configuration allows the company to define quantitative thresholds that must be met before deployment, enabling automated promotion through CI/CD pipelines.

Option A evaluates retrieval only and cannot assess generation quality. Option C introduces manual review, which does not scale and delays deployment. Option D separates retrieval and generation evaluation, making it harder to correlate chunking strategies with final output quality.

Therefore, Option B best meets the requirements for systematic evaluation, comparison, and quality enforcement in an Amazon Bedrock–based RAG system.

This complex set of requirements is best addressed by Amazon Bedrock Prompt Management . It allows the creation of parameterized prompts where variables (like demographics) can be injected at runtime, ensuring consistent medical terminology while adapting recommendations to the specific patient. Prompt Management natively supports versioning and approval workflows , which is a requirement for clinical safety and compliance. For audit and retention, Bedrock model invocation logging can be configured to send detailed prompt and response data to Amazon S3 . Storing these logs in S3 supports the 2-year retention requirement and facilitates local audits. S3 is more cost-effective for long-term storage than CloudWatch Logs alone. CloudTrail (Option A) only logs management events, not the actual prompt/response content required for medical auditing.

Option C best satisfies the requirement for rapid, correlated detection of model-related performance degradation. Amazon CloudWatch Application Insights provides automated observability across application components running on Amazon EC2, identifying abnormal behavior patterns without requiring extensive manual configuration.

Using custom metrics for recommendation quality, token usage, and response latency allows the company to directly monitor FM behavior, not just infrastructure health. Applying dimensions such as request type and user segment enables fine-grained correlation between performance issues and specific customer interactions or workloads.

CloudWatch anomaly detection is critical because it establishes dynamic baselines from historical data and detects deviations automatically. This enables alerts to be generated within minutes when FM behavior changes unexpectedly, satisfying the 10-minute alerting requirement without static thresholds that can miss subtle degradations.

CloudWatch Logs Insights complements metrics by enabling rapid analysis of log patterns, error messages, or unusual request flows associated with degraded recommendations. Because all data remains within CloudWatch, correlation between metrics, logs, and alerts is straightforward and operationally efficient.

Option A focuses on infrastructure metrics and lacks behavioral baselining. Option B provides tracing but not automated anomaly detection. Option D adds significant operational overhead and ingestion complexity for a use case already well supported by CloudWatch-native features.

Therefore, Option C delivers the most effective, scalable, and low-overhead observability solution for detecting FM-related performance deviations.

Option D is the correct solution because Amazon Q Developer customizations are designed to incorporate organization-approved knowledge and coding guidance without requiring per-project changes. A customization can point Amazon Q Developer to curated internal sources such as approved libraries, coding standards, architectural patterns, and proprietary techniques. This allows the assistant’s suggestions to align with company policies and preferred implementations consistently across teams and repositories.

The key requirement is that the company does not want to make project-level modifications. Options A, B, and C all require adding files or repositories into the project workspace, which directly violates this constraint. They also rely on developer behavior to “use workspace context,” which is harder to enforce and can lead to inconsistent adherence to standards.

With a customization, the organization centrally manages and updates approved resources. This reduces operational overhead because updates to libraries, patterns, or guidelines propagate automatically to developers using the customization, without requiring changes to each project. This is especially valuable for a new team, where consistent enforcement of approved practices is important to reduce compliance risk, security issues, and inconsistent code style.

Additionally, customizations support governance by allowing the company to standardize how Amazon Q Developer responds, ensuring that suggestions reflect approved internal content rather than generic public patterns.

Therefore, Option D best satisfies the requirement for centralized enforcement of approved resources with minimal ongoing management and no project-level modifications.

Option A is the most complete solution because it provides a fully automated canary strategy with staged traffic shifts, metric-based decisioning, and automatic rollback, all using managed AWS services. The requirement emphasizes automation, health-based traffic progression, and zero manual intervention to revert if performance degrades.

AWS Step Functions is well suited for orchestrating controlled deployment workflows with deterministic stages, waits, and conditional branches. By shifting traffic in stages and pausing for observation windows, the system can evaluate real-time inference latency and error rates before promoting more traffic to the new model version. Amazon CloudWatch provides the necessary real-time metrics and alarms for latency and error monitoring.

Invoking a Lambda function to evaluate CloudWatch metrics enables dynamic logic: increase traffic if thresholds remain healthy, reduce traffic or roll back if error rates rise or latency exceeds limits. Step Functions can halt the deployment by stopping progression or triggering rollback steps immediately, meeting the requirement for automated revert without human action.

Amazon EventBridge provides reliable automation triggers when a new model version is released, ensuring the deployment process is event-driven and repeatable.

Option B depends on “external logic,” which introduces operational risk and does not guarantee automatic rollback without custom systems. Option C incorrectly uses SageMaker endpoint variants to represent Bedrock model versions, which is not the intended integration model. Option D is overly indirect and operationally complex, using log pipelines and automation runbooks instead of direct metric-based traffic control.

Therefore, Option A best meets the requirements for automated gradual traffic shifting, real-time monitoring, and automatic rollback for Amazon Bedrock model deployments in a canary strategy.

Option B is the correct solution because Amazon Bedrock Prompt Management is purpose-built to manage, govern, and standardize prompt usage at scale across teams and Regions. It provides native version control, allowing teams to track prompt changes over time and ensure that only approved versions are used in production workflows.

Prompt Management supports approval workflows that align with enterprise governance requirements. Approval permissions can be enforced through IAM policies, ensuring that only authorized reviewers can approve or publish prompt versions. This removes the need for custom workflow engines or external storage systems, significantly reducing operational overhead.

Parameterized prompt templates enable consistent prompt structure while allowing controlled variation through defined variables. This ensures consistent quality standards and reduces prompt drift, which is critical when hundreds of prompts are reused across multiple applications and teams.

AWS CloudTrail integrates natively with Amazon Bedrock to provide immutable audit logs for prompt creation, updates, approvals, and usage. These detailed audit trails satisfy compliance requirements and allow security and governance teams to trace prompt activity across Regions and users.

Option A requires significant custom development to coordinate approvals and maintain state. Option C relies on general-purpose workflow services and manual versioning mechanisms that are error-prone and difficult to scale. Option D uses services not designed for large-scale GenAI prompt governance and introduces unnecessary complexity.

Therefore, Option B best meets the requirements for scalable, auditable, and low-overhead governance of AI-generated content using Amazon Bedrock.

Option C best addresses both core problems: hallucinated recommendations that do not exist in the catalog and slow response times, while keeping operational overhead low. The most direct way to prevent the model from recommending unavailable products is to ground generation on authoritative product catalog data at inference time. An Amazon Bedrock knowledge base is designed for this pattern by ingesting domain data, chunking content, creating embeddings, and retrieving the most relevant catalog entries when a user asks for recommendations. Implementing Retrieval Augmented Generation ensures the foundation model receives only approved, catalog-backed context and can cite or base its output on those retrieved items. This sharply reduces the likelihood of inventing products, because the response is conditioned on retrieved catalog records rather than relying on the model’s parametric memory.

The requirement also notes that most interactions are unique. That makes response caching far less effective, because there are fewer repeated prompts to benefit from cached outputs. Instead, improving the retrieval and model invocation path is the better optimization. Using the PerformanceConfigLatency parameter set to optimized prioritizes lower latency behavior for model inference, helping meet faster recommendation generation without requiring the company to build and operate additional infrastructure.

The other options do not solve the root cause as reliably. Prompt engineering and streaming can improve perceived latency, but they do not guarantee catalog-only recommendations because the model can still hallucinate items. Guardrails can help detect or block certain undesired outputs, but without consistent catalog grounding they do not ensure every recommendation is derived from the company’s product data. Building a custom OpenSearch validation and caching layer increases operational complexity, and caching is misaligned with predominantly unique interactions.

Alright, after comparing List B (txt file) against List A (Word file) , I have identified the unique questions. These questions cover scenarios or architectural configurations that were not present in the existing list.

Here are the unique questions from List B, formatted as requested:

AWS Step Functions provides native support for human-in-the-loop workflows, making it the best fit for regulatory oversight requirements. The waitForTaskToken integration pattern is explicitly designed to pause a workflow until an external actor—such as a human reviewer—completes a task.

In this architecture, AI-generated recommendations are sent to a human technician for review. The workflow pauses execution using a task token. Once the technician approves or rejects the recommendation, an AWS Lambda function calls SendTaskSuccess or SendTaskFailure, allowing the workflow to continue deterministically.

This approach ensures full auditability, as Step Functions records every state transition, timestamp, and execution path. Storing review outcomes in Amazon DynamoDB provides durable, queryable audit records required for regulatory compliance.

Option A requires custom orchestration and lacks native workflow state management. Option C incorrectly uses AWS Glue, which is not designed for approval workflows. Option D uses caching instead of durable audit storage and introduces unnecessary complexity.

Therefore, Option B is the AWS-recommended, lowest-risk, and most auditable solution for mandatory human review of AI outputs.

Option C directly addresses the root cause of truncated and inconsistent responses by using AWS-recommended semantic chunking and dynamic retrieval rather than static or sequential chunk processing. Amazon Bedrock documentation emphasizes that foundation models have fixed context windows and that sending oversized or poorly structured input can lead to truncation, loss of context, and degraded output quality.

Semantic chunking breaks documents based on meaning instead of fixed token counts. By using a breakpoint percentile threshold and sentence buffers, the content remains coherent and semantically complete. This approach reduces the likelihood that important concepts are split across chunks, which is a common cause of inconsistent summarization results.

The RetrieveAndGenerate API is designed specifically to handle large documents that exceed a model’s context window. Instead of forcing all content into a single inference call, the API generates embeddings for chunks and dynamically selects only the most relevant chunks based on similarity to the user query. This ensures that the FM receives only high-value context while staying within its context window limits.

Option A is ineffective because chaining chunks sequentially does not align with how FMs process context and risks exceeding context limits or introducing irrelevant information. Option B improves structure but still relies on larger parent chunks, which can lead to inefficiencies when processing very large documents. Option D processes segments independently, which often causes loss of global context and inconsistent summaries.

Therefore, Option C is the most robust, AWS-aligned solution for resolving truncation and consistency issues when processing large technical documents with Amazon Bedrock.