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?

Options :

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 analyst uses an internal GenAI tool to extract data from quarterly earnings PDFs into a standardized JSON format. The application is reporting frequent ValidationError exceptions in the backend. Upon investigation, the developer notices that for certain complex PDFs, the model either omits required fields or outputs the data as a Markdown table instead of JSON, despite the system prompt instructions.Which troubleshooting and remediation workflow should the developer implement to resolve these prompt maintenance issues and prevent recurrence?

Options :

A : Enable model invocation logging to Amazon CloudWatch Logs to capture the raw inputs and outputs. Use CloudWatch Logs Insights to filter for the failed requests and identify the specific document patterns causing the prompt confusion. Create a test suite of these edge cases, iterate on the prompt template by adding "few-shot" examples of the correct JSON structure, and implement strict schema validation to detect format inconsistencies before processing.

B : Configure AWS X-Ray to trace the latency of the model invocations. Identify the requests that take longer than 5 seconds to generate a response, as these indicate prompt confusion. Switch the Foundation Model to a lower-latency version (e.g., from Claude to Titan) to ensure the model responds before the application times out.

C : Use Amazon SageMaker Model Monitor to track the statistical bias of the generated JSON output. Configure a baseline for the expected distribution of keys in the JSON object. Set up an alert to trigger if the drift in key frequency exceeds 10%, indicating a format inconsistency.

D : Implement an Amazon EventBridge rule to trigger an automatic retry with a higher temperature setting (e.g., 1.0) whenever the backend reports a ValidationError. This encourages the model to generate a different, more creative output format that might pass validation on the second attempt.

Answer: A

Question 3

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?

Options :

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

Question 4

A company has a centralized AI team managing Bedrock models in Account A. Three development teams in Accounts B, C, and D need access to a specific Claude Sonnet model with provisioned throughput for their applications. The solution must ensure that these accounts can ONLY invoke this specific model and cannot access any other Bedrock models or features in Account A. The solution must also allow the central AI team to monitor usage and apply guardrails centrally without requiring changes in the development accounts.Which solution meets ALL these requirements?

Options :

A : Enable Bedrock model access in Accounts B, C, and D. Use AWS Organizations service control policies (SCPs) to deny access to all Bedrock models except the Claude Sonnet model. Configure each account to invoke the model directly using their own IAM permissions.

B : Create a cross-region inference profile for the Claude Sonnet model in Account A. Share the inference profile ARN with Accounts B, C, and D. Create IAM roles in each development account with permissions to bedrock:InvokeModel on the specific inference profile ARN only.

C : Create IAM roles in Account A that trust Accounts B, C, and D. Grant these roles permissions to bedrock:InvokeModel with a resource condition limiting access to the specific Claude Sonnet model ARN. Configure applications in development accounts to assume these roles.

D : Create a cross-account inference profile for the Claude Sonnet model in Account A. Use AWS RAM (Resource Access Manager) to share the inference profile with Accounts B, C, and D. Create resource-based policies on the inference profile that allow bedrock:InvokeModel action only from the specified account principals.

Answer: D

Question 5

A legal tech company uses Amazon Bedrock to extract specific liability clauses from uploaded contracts. Users have reported instances where the model "hallucinates" clauses that do not exist in the source text.Which mechanisms should the developer implement to verify accuracy and reduce these hallucinations?

Options :

A : Define a JSON Schema representing the expected liability data structure and include it in the model instructions to enforce constrained, deterministic output.

B : Implement a post-processing workflow that calculates the semantic similarity score between the generated response and the source text, rejecting responses that fall below a defined confidence threshold.

C : Configure Amazon Bedrock Guardrails with a "Sensitive Information" filter to redact names and addresses found in the contract clauses.

D : Increase the inference temperature parameter to 0.9 and enable Top-P sampling to broaden the diversity of the generated clauses.

E : Use Amazon CloudWatch to monitor the InvocationLatency and ModelTokenCount metrics to identify anomalies in the model's processing speed.

Answer: A,B

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