Question 1

A marketing analytics company generates monthly reports that include AI-generated summaries of customer feedback. They have 10,000 customer feedback entries to summarize each month. The summaries are not time-sensitive and can be processed overnight. The team wants to optimize costs compared to processing requests one-by-one in real-time.Which processing approach should the developer use to reduce costs?

Options :

A : Invoke the foundation model in real-time for each customer feedback entry as soon as it arrives

B : Use provisioned throughput to guarantee capacity for all 10,000 requests

C : Use batch processing to send multiple requests together and process them during off-peak hours

D : Enable auto-scaling to handle the large burst of requests

Answer: C

Question 2

A biotechnology company has fine-tuned an open-source LLaMA model on their proprietary research dataset to generate scientific hypotheses. They need to deploy this custom model to a production endpoint where it can be invoked by their research application. The team requires control over the instance type (GPU-optimized) and auto-scaling policies.Which AWS service should they use to deploy and host their custom fine-tuned model?

Options :

A : AWS Lambda with container images

B : Amazon Bedrock with custom model import

C : Amazon SageMaker AI with real-time endpoints

D : Amazon EC2 with manual model deployment

Answer: C

Question 3

An insurance company has deployed a Generative AI chatbot using Amazon Bedrock to explain policy details to customers. To comply with strict financial regulations, the solution requires a continuous monitoring and automated remediation system for two specific governance issues:Misuse Remediation: If a specific user triggers the "Hate Speech" content filter in Amazon Bedrock Guardrails more than 5 times in a single minute, their access must be automatically and immediately suspended.Bias Monitoring: The system must strictly monitor the model's responses over time to detect if the terminology becomes statistically biased against specific demographics compared to a pre-established "Golden Set" baseline.Which combination of mechanisms satisfies these requirements?

Options :

A : Implement a scheduled AWS Step Functions workflow that uses Amazon SageMaker Clarify to analyze the aggregated inference logs stored in Amazon S3. Configure the job to calculate bias metrics against the baseline and publish findings to CloudWatch.

B : Deploy Amazon SageMaker Model Monitor on the Amazon Bedrock endpoint. Enable the "Real-time Concept Drift" feature to block any inference response that deviates more than 5% from the training data distribution.

C : Configure an Amazon EventBridge Rule to match the aws.bedrock.GuardrailIntervention event pattern. Set the target as an AWS Systems Manager Run Command to disable the user's account in the identity provider.

D : Enable Amazon Macie on the bucket containing the inference logs. configure a custom data identifier for "Biased Terminology" and set up an automated remediation job to delete non-compliant logs.

E : Create an Amazon CloudWatch Alarm that monitors the GuardrailInterventionCount metric with a distinct dimension for the user ID. Configure the alarm action to trigger an AWS Lambda function that revokes the user's IAM permissions or API keys.

Answer: A,E

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

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