A company has a recommendation system. The system's applications run on Amazon EC2 instances. The applications make API calls to Amazon Bedrock foundation models (FMs) to analyze customer behavior and generate personalized product recommendations.
The system is experiencing intermittent issues. Some recommendations do not match customer preferences. The company needs an observability solution to monitor operational metrics and detect patterns of operational performance degradation compared to established baselines. The solution must also generate alerts with correlation data within 10 minutes when FM behavior deviates from expected patterns.
Which solution will meet these requirements?

1. A. Configure Amazon CloudWatch Container Insights for the application infrastructur
2. B. Set up CloudWatch alarms for latency threshold
3. C. Add custom metrics for token counts by using the CloudWatch embedded metric forma
4. D. Create CloudWatch dashboards to visualize the data.
5. E. Implement AWS X-Ray to trace requests through the application component
6. F. Enable CloudWatch Logs Insights for error pattern detectio
7. G. Set up AWS CloudTrail to monitor all API calls to Amazon Bedroc
8. H. Create custom dashboards in Amazon QuickSight.
9. I. Enable Amazon CloudWatch Application Insights for the application resource
10. J. Create custom metrics for recommendation quality, token usage, and response latency by using the CloudWatch embedded metric format with dimensions for request types and user segment
11. K. Configure CloudWatch anomaly detection on the model metric
12. L. Establish log pattern analysis by using CloudWatch Logs Insights.
13. M. Use Amazon OpenSearch Service with the Observability plugi
14. N. Ingest model metrics and logs by using Amazon Kinesi
15. O. Create custom Piped Processing Language (PPL) queries to analyze model behavior pattern
16. P. Establish operational dashboards to visualizeanomalies in real time.

Correct Answer: C

A company has deployed an AI assistant as a React application that uses AWS Amplify, an AWS AppSync GraphQL API, and Amazon Bedrock Knowledge Bases. The application uses the GraphQL API to call the Amazon Bedrock RetrieveAndGenerate API for knowledge base interactions. The company configures an AWS Lambda resolver to use the RequestResponse invocation type.
Application users report frequent timeouts and slow response times. Users report these problems more frequently for complex questions that require longer processing.
The company needs a solution to fix these performance issues and enhance the user experience.
Which solution will meet these requirements?

1. A. Use AWS Amplify AI Kit to implement streaming responses from the GraphQL API and to optimize client-side rendering.
2. B. Increase the timeout value of the Lambda resolve
3. C. Implement retry logic with exponential backoff.
4. D. Update the application to send an API request to an Amazon SQS queu
5. E. Update the AWS AppSync resolver to poll and process the queue.
6. F. Change the RetrieveAndGenerate API to the InvokeModelWithResponseStream AP
7. G. Update the application to use an Amazon API Gateway WebSocket API to support the streaming response.

Correct Answer: A

An ecommerce company operates a global product recommendation system that needs to switch between multiple foundation models (FMs) in Amazon Bedrock based on regulations, cost optimization, and performance requirements. The company must apply custom controls based on proprietary business logic, including dynamic cost thresholds, AWS Region-specific compliance rules, and real-time A/B testing across multiple FMs. The system must be able to switch between FMs without deploying new code. The system must route user requests based on complex rules including user tier, transaction value, regulatory zone, and real-time cost metrics that change hourly and require immediate propagation across thousands of concurrent requests.
Which solution will meet these requirements?

1. A. Deploy an AWS Lambda function that uses environment variables to store routing rules and Amazon Bedrock FM ID
2. B. Use the Lambda console to update the environment variables when business requirements chang
3. C. Configure an Amazon API Gateway REST API to read request parameters to make routing decisions.
4. D. Deploy Amazon API Gateway REST API request transformation templates to implement routing logic based on request attribute
5. E. Store Amazon Bedrock FM endpoints as REST API stage variable
6. F. Update the variables when the system switches between models.
7. G. Configure an AWS Lambda function to fetch routing configuration from the AWS AppConfig Agent for each user reques
8. H. Run business logic in the Lambda function to select the appropriate FM for each reques
9. I. Expose the FM through a single Amazon API Gateway REST API endpoint.
10. J. Use AWS Lambda authorizers for an Amazon API Gateway REST API to evaluate routing rules that are stored in AWS AppConfi
11. K. Return authorization contexts based on business logi
12. L. Route requests to model-specific Lambda functions for each Amazon Bedrock FM.

Correct Answer: C

A company is building a video analysis platform on AWS. The platform will analyze a large video archive by using Amazon Rekognition and Amazon Bedrock. The platform must comply with predefined privacy standards. The platform must also use secure model I/O, control foundation model (FM) access patterns, and provide an audit of who accessed what and when.
Which solution will meet these requirements?

1. A. Configure VPC endpoints for Amazon Bedrock model API call
2. B. Implement Amazon Bedrock guardrails to filter harmful or unauthorized content in prompts and response
3. C. Use Amazon Bedrock trace events to track all agent and model invocations for auditing purpose
4. D. Export the traces to Amazon CloudWatch Logs as an audit record of model usag
5. E. Store all prompts and outputs in Amazon S3 with server-side encryption with AWS KMS keys (SSE-KMS).
6. F. Define access control by using IAM with attribute-based access control (ABAC) to map departments to specific permission
7. G. Configure VPC endpoints for Amazon Bedrock model API call
8. H. Use IAM condition keys to enforce specific GuardrailIdentifier and ModelId value
9. I. Configure AWS CloudTrail to capture management and data events for S3 objects and KMS key usage activitie
10. J. Enable S3 server access logging to record detailed file-level interactions with the video archive
11. K. Send all CloudTrail logs to AWS CloudTrail Lak
12. L. Set up Amazon CloudWatch alarms to detect and alert on unexpected activity from Amazon Bedrock, Amazon Rekognition, and AWS KMS.
13. M. Restrict access to services by using VPC endpoint policie
14. N. Use AWS Config to track resource changes and compliance with security rule
15. O. Use server-side encryption with AWS KMS keys (SSE-KMS) to encrypt data at res
16. P. Store the model??s I/O in separate Amazon S3 bucket
17. Q. Enable S3 server access logging to track file-level interactions.
18. R. Configure AWS CloudTrail Insights to analyze API call patterns across accounts and detect anomalous activity in Amazon Bedrock, Amazon Rekognition, Amazon S3, and AWS KM
19. S. Deploy Amazon Macie to scan and classify the video archiv
20. T. Use server-side encryption with AWS KMS keys (SSE-KMS) to encrypt all stored dat
21. . Configure CloudTrail to capture KMS API usage events for audit purpose
22. . Configure Amazon EventBridge rules to process CloudTrail Insights anomalies and Macie finding
23. . Use CloudWatch alarms to trigger automated notifications and security responses when potential security issues are detected.

Correct Answer: B

A financial services company uses an AI application to process financial documents by using Amazon Bedrock. During business hours, the application handles approximately 10,000 requests each hour, which requires consistent throughput.
The company uses the CreateProvisionedModelThroughput API to purchase provisioned throughput. Amazon CloudWatch metrics show that the provisioned capacity is unused while on-demand requests are being throttled. The company finds the following code in the application:
python
response = bedrock_runtime.invoke_model(modelId="anthropic.claude-v2", body=json.dumps(payload))
The company needs the application to use the provisioned throughput and to resolve the throttling issues.
Which solution will meet these requirements?

1. A. Increase the number of model units (MUs) in the provisioned throughput configuration.
2. B. Replace the model ID parameter with the ARN of the provisioned model that the CreateProvisionedModelThroughput API returns.
3. C. Add exponential backoff retry logic to handle throttling exceptions during peak hours.
4. D. Modify the application to use the InvokeModelWithResponseStream API instead of the InvokeModel API.

Correct Answer: B