Free Amazon AIP-C01 Exam Questions

Question 1

A financial services company has 15 development teams building different microservices using Amazon Bedrock. Code reviews reveal inconsistent error handling patterns, varying retry logic implementations, and different approaches to streaming responses across services. The architecture team defined standard Bedrock integration patterns, but manual enforcement through code reviews is slow and inconsistent. The company needs a solution that automatically identifies non-standard implementations and helps developers refactor code to match approved patterns across the entire multi-repository codebase.Which solution enforces consistent integration patterns MOST effectively?

A : Create an AWS CodeCommit repository with approved code templates and integration examples. Distribute the repository link to all teams with instructions to reference it when implementing Bedrock features.

B : Implement pre-commit hooks in Git that run custom Python scripts to validate Bedrock API calls against a JSON schema defining allowed patterns before allowing commits.

C : Deploy SonarQube on Amazon EC2 to perform static code analysis. Configure custom rules that flag non-standard Bedrock integration patterns and generate reports for teams to manually fix.

D : Configure Amazon Q Developer to scan the codebase, identify Bedrock integration anti-patterns, generate refactored code following company standards, and apply the consistent changes across all microservice repositories.

Answer: D

Question 2

A financial services company uses Amazon Bedrock to generate investment analysis reports. The prompt engineering team frequently updates prompts to improve output quality, but recent production incidents showed that prompt changes caused incorrect financial calculations.The company requires a solution that enables prompt versioning with the ability to rollback to previous versions, tracks which prompt version generated each report for audit compliance, sends alerts when prompt performance degrades (measured by downstream validation failures), and maintains a complete audit trail of all prompt changes for regulatory reviews.Which solution meets ALL these requirements with the LEAST operational overhead?

A : Use Bedrock Prompt Management to create and version prompts. Store each prompt version with metadata tags indicating the version number. Configure CloudWatch Logs to capture all Bedrock InvokeModel requests with prompt details. Create a Lambda function that parses CloudWatch Logs, detects validation failures, and publishes alerts to SNS. Store prompt change history in DynamoDB for audit reports.

B : Use Bedrock Prompt Management to create versioned prompts with immutable versions and aliases. Tag each report with the prompt version/alias used during generation and log this to CloudWatch Logs. Enable CloudTrail to capture all Bedrock API calls including prompt updates. Create CloudWatch metric filters on validation failure logs and configure CloudWatch Alarms to trigger SNS notifications when failure rates exceed thresholds. Use CloudTrail Event History and CloudWatch Logs Insights for audit trail queries.

C : Store prompts as text files in S3 with version control enabled. Implement a Lambda-based prompt versioning system that reads prompts from S3, tracks versions in DynamoDB, and passes prompts to Bedrock InvokeModel API. Configure S3 Event Notifications to trigger Lambda functions that log prompt changes to CloudWatch. Use CloudWatch Synthetics to test prompt quality and send alerts via SNS when tests fail.

D : Use AWS Systems Manager Parameter Store to manage prompts with versioning enabled. Create a Step Functions workflow that retrieves the prompt version from Parameter Store, invokes Bedrock, and logs the prompt version and output to CloudWatch Logs. Enable Parameter Store change notifications via EventBridge to track prompt updates. Create CloudWatch Alarms based on custom metrics published by the Step Functions workflow when validation failures occur.

Answer: B

Question 3

A financial services company has 15 development teams building different microservices using Amazon Bedrock. Code reviews reveal inconsistent error handling patterns, varying retry logic implementations, and different approaches to streaming responses across services. The architecture team defined standard Bedrock integration patterns, but manual enforcement through code reviews is slow and inconsistent. The company needs a solution that automatically identifies non-standard implementations and helps developers refactor code to match approved patterns across the entire multi-repository codebase.Which solution enforces consistent integration patterns MOST effectively?

A : Create an AWS CodeCommit repository with approved code templates and integration examples. Distribute the repository link to all teams with instructions to reference it when implementing Bedrock features.

B : Implement pre-commit hooks in Git that run custom Python scripts to validate Bedrock API calls against a JSON schema defining allowed patterns before allowing commits.

C : Deploy SonarQube on Amazon EC2 to perform static code analysis. Configure custom rules that flag non-standard Bedrock integration patterns and generate reports for teams to manually fix.

D : Configure Amazon Q Developer to scan the codebase, identify Bedrock integration anti-patterns, generate refactored code following company standards, and apply the consistent changes across all microservice repositories.

Answer: D

Question 4

A travel company is building a complex "Vacation Planner" system using Amazon Bedrock Agents multi-agent collaboration:Flight Specialist Agent: Searches flight databases using a Lambda-based action groupHotel Specialist Agent: Searches hotel inventory using an API-based action groupSupervisor Agent: Interacts with the user, breaks down the "Plan a trip to Paris" request, delegates sub-tasks to the specialist agents, and aggregates their findings into a final itineraryThe developer needs to optimize the system to reduce latency for simple, single-domain queries (like "Find me a hotel in Paris") while maintaining the ability to handle complex multi-step requests (like "Plan a complete 5-day vacation to Paris").Which collaboration mode configuration meets this requirement?

A : Configure the supervisor agent in Supervisor with routing mode. Simple queries are routed directly to the appropriate collaborator agent (Flight or Hotel Specialist), while complex queries automatically trigger full supervisor mode for orchestration across multiple agents.

B : Configure the supervisor agent in Supervisor mode. The supervisor always breaks down the request, invokes collaborator agents in parallel, and synthesizes the results regardless of query complexity.

C : Deploy all three agents as independent agents without multi-agent collaboration. Use Amazon Lex to parse user intent and route to the appropriate single agent, then use a Lambda function to aggregate results from multiple agent invocations.

D : Use AWS Step Functions to create a Choice state that evaluates query complexity. Route simple queries to individual agents via direct Lambda invocation, and complex queries to the Bedrock supervisor agent.

Answer: A

Question 5

An e-commerce company uses an Amazon Bedrock Agent to handle product returns. The agent has access to two tools: GetOrderDetails and ProcessRefund.During testing, the agent fails to process refunds for orders older than 30 days. Instead of calling the ProcessRefund tool, the agent simply apologizes to the user.The developer suspects the agent is correctly identifying the return policy date limit but needs to verify the specific logic used by the agent to reach this decision.Which troubleshooting step provides the necessary visibility?

A : Check CloudWatch Metrics. Look at the Invocations and Errors metrics for the ProcessRefund Lambda function. If the count is zero, it confirms the tool was never called.

B : Review CloudTrail Logs. Search for the InvokeModel events. Download the raw request payload to see if the system prompt includes the "30-day return policy" instruction.

C : Analyze the Agent Trace. Invoke the agent with enableTrace=true. Inspect the orchestrationTrace to view the "Observation" step where the agent received the order date from GetOrderDetails and the subsequent "Rationale" step where it reasoned that the date exceeded the 30-day policy.

D : Use Amazon Bedrock Model Evaluation. Run an evaluation job using the "Tool Usage Accuracy" metric to see a statistical percentage of how often the agent fails to call the tool.

Answer: C