Question 1

A fintech company is launching a GenAI-powered "Spending Analyzer" feature. The application uses a Large Language Model (LLM) on Amazon Bedrock to explain transaction history to users. The project has two critical governance requirements:Compliance: The model must strictly refuse to answer any questions related to "Tax Advice" or "Legal Liability," as the company is not licensed to provide these services.Agility: The non-technical Product Team needs the ability to update the "Persona" and "Output Format" (e.g., switching from paragraphs to bullet points) of the analyzer weekly without requiring the engineering team to deploy new application code.Which combination of AWS GenAI features should the developer implement to meet these requirements? (Choose two)

Options :

A : Fine-tune the Foundation Model using Amazon SageMaker JumpStart on a dataset of "Refusal" examples. Train the model to recognize tax-related questions and output a standard disclaimer.

B : Implement Amazon Bedrock Prompt Management. Create a parameterized prompt template that defines the System Prompt (Persona) and output structure. Grant the Product Team permissions to iterate, test, and version the prompt within the Bedrock console.

C : Implement a post-processing AWS Lambda function. Use a keyword list (e.g., "tax", "audit", "lawyer") to scan the generated text. If a restricted keyword is found, overwrite the response with a hardcoded error message.

D : Use AWS Systems Manager Parameter Store to hold the System Prompt string. Update the backend Lambda function to fetch the latest prompt version on every invocation. Give the Product Team access to the Systems Manager console to edit the string.

E : Create an Amazon Bedrock Guardrail. Configure a "Denied Topics" policy that explicitly defines "Tax Advice" and "Legal Advice" with description examples. Attach this guardrail to the model invocation to intercept and block restricted queries.

Answer: B,E

Question 2

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 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 development team is building a serverless application where AWS Lambda functions need to invoke Amazon Bedrock foundation models to generate product descriptions. The security team requires that the Lambda functions follow the principle of least privilege when accessing AWS services.Which approach should the developer implement to grant the Lambda function access to Amazon Bedrock?

Options :

A : Embed AWS access keys and secret keys as environment variables in the Lambda function configuration

B : Grant the Lambda function's IAM role full Administrator access to ensure it can call all AWS services

C : Attach an IAM role to the Lambda function with the bedrock:InvokeModel permission for the specific models being used

D : Store IAM credentials in the Lambda function code and rotate them manually every 90 days

Answer: C

Question 5

A bank uses an AWS CodePipeline to deploy a GenAI loan approval application. The application consists of an Amazon Bedrock Knowledge Base and a set of Lambda functions. The compliance team requires that before any new Knowledge Base configuration (e.g., chunking strategy change) is promoted to Production, it must pass a "Golden Set" evaluation.If the evaluation score (F1 score) is below 0.85, the pipeline must automatically rollback.Which solution meets these requirements?

Options :

A : Use Amazon Inspector within the pipeline to scan the Knowledge Base configuration for misconfigurations and vulnerabilities.

B : Add a Manual Approval action to the pipeline. Configure an Amazon SNS notification to the compliance team with a link to the Bedrock Console so they can manually test the agent and click "Approve."

C : Add a Test Stage to the pipeline. Use AWS CodeBuild to run a script that invokes the new Knowledge Base Retrieve API against the golden questions. Calculate the score and return a non-zero exit code if it fails, causing the pipeline to stop.

D : Use AWS CloudFormation Guard. Define a policy that checks the VectorIndexConfiguration parameter in the template. Fail the stack deployment if the chunking strategy has changed.

Answer: C

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