Question 1

A large ecommerce company has deployed a foundation model (FM) to generate product descriptions. The company's engineering team monitors technical metrics such as token usage, latency, and error rates by using Amazon CloudWatch. The company's marketing team tracks business metrics such as conversion rates and revenue impact in its own systems. The company needs a unified observability solution that correlates technical performance with business outcomes. The solution must provide automatic alerts to stakeholders when operational metrics indicate degradation. The solution must provide comprehensive visibility across both technical and business metrics. Which solution will meet these requirements? 

A. Create CloudWatch dashboards that include technical metrics and imported business metrics. Configure CloudWatch composite alarms that combine technical data and business data. Use Amazon SNS to set up notifications to stakeholders. 

B. Use Amazon Managed Grafana to visualize technical metrics from CloudWatch with business metrics from external sources. Configure Amazon Managed Grafana alerts to invoke AWS Lambda functions. Configure the Lambda functions to remediate issues automatically when metrics exceed predefined thresholds. 

C. Stream CloudWatch metrics to Amazon S3 by using CloudWatch metric streams. Create Amazon QuickSight dashboards to visualize the combined technical metrics and business metrics. Set up Amazon EventBridge rules to send notifications to stakeholders when metrics exceed predefined thresholds. 

D. Configure CloudWatch custom dashboards that integrate operational metrics with imported business metrics. Set up CloudWatch composite alarms with anomaly detection. Use Amazon SNS to create alarm actions to notify stakeholders when correlated metrics indicate performance issues. 

Answer: D

Question 2

A university is building an AI-powered application that includes several sub-applications. The sub-applications include AI assistants, assignment graders, and internal analytics applications. The university is defining and testing multiple prompts by using various foundation models (FMs). The university wants to compare variants of each prompt and choose the variant that yield outputs that are best-suited for specified use cases. The university requires a version control solution for the prompts. The university must be able to test prompt variations and collect audit trails for prompt changes and usage. The solution must also maintain consistency while allowing the prompts to integrate into the main application. Which combination of solutions will meet these requirements with the LEAST operational overhead? (Select TWO.)

A. Use Amazon Bedrock Prompt Management to create versioned prompts. Include parameterized variables for each use case. 

B. Store prompts in Amazon S3. Use AWS Step Functions to orchestrate the model interactions and service integrations. 

C. Use Amazon Bedrock Flows to create workflows that combine FMs and AWS services. 

D. Configure AWS Config to record prompt changes. Use AWS CloudTrail to track prompt usage. 

E. Configure Amazon Bedrock intelligent prompt routing. 

Answer: A,C

Question 3

A company purchases Amazon Q Developer Pro subscriptions for 500 developers to improve code quality and productivity. The company needs to create an observability system that tracks adoption metrics across the company. The observability system must be able to identify active subscription users compared to underused subscriptions. The system must give the company the ability to recognize power users every quarter and to identify teams that require additional training. The system must provide visibility into usage patterns such as the number of lines of Amazon Q generated code that each user has accepted. Which solution will meet these requirements?

A. Create a usage dashboard for Amazon Q Developer. Use the usage dashboard to track aggregated usage adoption metrics. 

B. Use the Amazon Q Developer built-in administrator dashboard to track user adoption metrics across the company’s organization in AWS Organizations. 

C. Collect user-level metrics in Amazon Q Developer. Store the metrics in an Amazon S3 bucket. Use Amazon QuickSight to visualize the usage data. Create dashboards to show adoption metrics for users and teams. 

D. Configure AWS CloudTrail to track all Amazon Q Developer API calls in the company’s organization in AWS Organizations. Use an AWS Lambda function to process the logs. Store the processed logs in Amazon DynamoDB. Create custom dashboards in Amazon Managed Grafana to visualize the data. 

Answer: B

Question 4

A company is building a real-time voice assistant system to assist customer service representatives during customer calls. The system must convert audio calls to text with end-to-end latency of less than 500 ms. The system must use generative AI (GenAI) to produce response suggestions. Human supervisors must be able to rate the system's suggestions during a live customer call. The company must store all customer interactions to comply with auditing policies. Which solution will meet these requirements?

A. Use the Amazon Transcribe streaming API with standard settings to convert speech to text. Use Amazon Bedrock batch processing to perform inference. Store call recordings and metadata in Amazon S3. Use S3 Lifecycle policies to manage the storage. 

B. Use the Amazon Transcribe streaming API with 100-ms audio chunks to optimize latency for the voice assistant. Call the Amazon Bedrock InvokeModelWithResponseStream operation to process client inquiries in real time. Store supervisor ratings in an Amazon DynamoDB table. 

C. Use Amazon Transcribe batch processing to perform post-call analysis. Configure AWS Lambda functions to generate responses by using the Amazon Bedrock InvokeModel operation. Use Amazon CloudWatch to log supervisor feedback. 

D. Use Amazon Transcribe to convert speech to text and to perform real-time analytics. Use Amazon Comprehend to perform sentiment analysis. Use Amazon SQS to queue processing tasks. Run the Amazon Bedrock InvokeModel operation to generate responses. 

Answer: B

Question 5

A retail company runs an application that makes product recommendations to customers on the company’s website. The application uses Amazon Bedrock to generate recommendations by dynamically constructing prompts and sending them to foundation models (FMs). A GenAI developer has deployed an update to the application that instructs the FM to include a specific promotional message when the FM generates a response to prompts. When the developer tests the application, the promotional message does not always appear in the responses. When the promotional message does appear in the responses, it does not always flow with the rest of the text. The GenAI developer must ensure that the promotional message always appears in the FM responses. Which solution will meet this requirement? 

A. Use an Amazon Bedrock Guardrails filter on the prompt. Set the input filter strength to HIGH. 

B. Generate multiple response variants that include the promotional message in different ways. Use a reranker model to select the most coherent version based on relevance to the original prompt. 

C. Run the prompt through Amazon Bedrock. Process the response through Amazon Bedrock AgentCore to add the promotional message. Rerank the results by using the original prompt and the desired message as context. 

D. Reinforce the requirement to include the new promotional message within product recommendations by using an output indicator in prompts to the FM. 

Answer: D