A retail company has a generative AI (GenAI) product recommendation application that uses Amazon Bedrock. The application suggests products to customers based on browsing history and demographics. The company needs to implement fairness evaluation across multiple demographic groups to detect and measure bias in recommendations between two prompt approaches. The company wants to collect and monitor fairness metrics in real time. The company must receive an alert if the fairness metrics show a discrepancy of more than 15% between demographic groups. The company must receive weekly reports that compare the performance of the two prompt approaches.
Which solution will meet these requirements with the LEAST custom development effort?
Configure an Amazon CloudWatch dashboard to display default metrics from Amazon Bedrock API calls. Create custom metrics based on model outputs. Set up Amazon EventBridge rules to invoke AWS lambda functions that perform post-processing analysis on model responses and publish custom fairness metrics.
Create the two prompt variants in Amazon Bedrock Prompt Management. Use Amazon Bedrock Flows to deploy the prompt variants with defined traffic allocation. Configure Amazon Bedrock guardrails that have content filters to monitor demographic fairness. Set up Amazon CloudWatch alarms on the GuardrailContentSource dimension that use InvocationsIntervened metrics to detect recommendation discrepancy threshold violations.
Set up Amazon SageMaker Clarify to analyze model outputs. Publish fairness metrics to Amazon CloudWatch. Create CloudWatch composite alarms that combine SageMaker Clarify bias metrics with Amazon Bedrock latency metrics to provide a comprehensive fairness evaluation dashboard.
Create an Amazon Bedrock model evaluation job to compare fairness between the two prompt variants. Enable model invocation logging in Amazon CloudWatch. Set up CloudWatch alarms for InvocationsIntervened metrics with a dimension for each demographic group.
Show answer and explanation
Correct answer: B. Create the two prompt variants in Amazon Bedrock Prompt Management. Use Amazon Bedrock Flows to deploy the prompt variants with defined traffic allocation. Configure Amazon Bedrock guardrails that have content filters to monitor demographic fairness. Set up Amazon CloudWatch alarms on the GuardrailContentSource dimension that use InvocationsIntervened metrics to detect recommendation discrepancy threshold violations.
Option B directly addresses fairness evaluation with the least custom development by using Amazon Bedrock's built-in Prompt Management for variants, Bedrock Flows for traffic allocation, and Bedrock Guardrails specifically designed to monitor demographic fairness through content filters. The guardrails' InvocationsIntervened metrics on GuardrailContentSource dimensions can be configured with CloudWatch alarms for the 15% threshold violation detection. This approach minimizes custom development compared to building custom metrics and Lambda post-processing functions.
Why the other options are wrong
A. Requires significant custom development building post-processing analysis functions and custom fairness metrics calculation with Lambda, increasing maintenance burden.
C. SageMaker Clarify is designed for one-time model evaluation jobs, not real-time monitoring with alerts, and requires manual integration work rather than native Bedrock guardrail support.
D. Model evaluation jobs are batch-oriented for comparing variants, not suited for real-time metric collection and alerting on fairness discrepancies.
Question 2
A finance company is developing an AI assistant to help clients plan investments and manage their portfolios. The company identifies several high-risk conversation patterns such as requests for specific stock recommendations or guaranteed returns. High-risk conversation patterns could lead to regulatory violations if the company cannot implement appropriate controls.
The company must ensure that the AI assistant does not provide inappropriate financial advice, generate content about competitors, or make claims that are not factually grounded in the company's approved financial guidance. The company wants to use Amazon Bedrock Guardrails to implement a solution.
Which combination of steps will meet these requirements? (Choose three.)
Add the high-risk conversation patterns to a denied topics guardrail.
Configure a content filter guardrail to filter prompts that contain the high-risk conversation patterns.
Configure a content filter guardrail to filter prompts that contain competitor names.
Add the names of competitors as custom word filters. Set the input and output actions to block.
Set a low grounding score threshold.
Set a high grounding score threshold.
Show answer and explanation
Correct answer: A, D, F. Add the high-risk conversation patterns to a denied topics guardrail; Add the names of competitors as custom word filters. Set the input and output actions to block; Set a high grounding score threshold.
Option A addresses high-risk conversation patterns by adding them to denied topics guardrails. Option D blocks competitor names using custom word filters with input/output block actions. Option F sets a high grounding score threshold to ensure responses are factually grounded in approved financial guidance. Together, these three options comprehensively address preventing inappropriate financial advice, blocking competitor content, and ensuring factual accuracy without allowing unreliable content.
Why the other options are wrong
B. Content filters are for filtering TYPES of content (violence, hate speech), not specific conversation patterns; denied topics guardrails are the correct mechanism.
C. Custom word filters (option D) are more appropriate and direct for blocking competitor names than content filters designed for content categories.
E. A low grounding score threshold would allow poorly grounded claims, directly opposing the requirement to ensure factually grounded guidance.
Question 3
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?
Use AWS Amplify AI Kit to implement streaming responses from the GraphQL API and to optimize client-side rendering.
Increase the timeout value of the Lambda resolver. Implement retry logic with exponential backoff.
Update the application to send an API request to an Amazon SQS queue. Update the AWS AppSync resolver to poll and process the queue.
Change the RetrieveAndGenerate API to the InvokeModelWithResponseStream API. Update the application to use an Amazon API Gateway WebSocket API to support the streaming response.
Show answer and explanation
Correct answer: A. Use AWS Amplify AI Kit to implement streaming responses from the GraphQL API and to optimize client-side rendering.
AWS Amplify AI Kit provides native support for streaming responses from GraphQL APIs and optimizes client-side rendering for long-running AI operations. This directly addresses the timeout and latency issues caused by complex questions requiring longer processing. The streaming approach allows responses to begin flowing to the client immediately rather than waiting for complete computation, significantly improving perceived performance and user experience without architectural changes.
Why the other options are wrong
B. Simply increasing timeout values treats the symptom but doesn't solve the underlying slow response problem; exponential backoff on timeouts won't improve performance for intrinsically slow operations.
C. SQS-based asynchronous polling adds significant architectural complexity and latency (polling overhead) compared to native streaming, which is inefficient for real-time user interactions.
D. Switching APIs and adding WebSocket complexity is unnecessarily complicated when Amplify AI Kit provides streaming support natively integrated with existing GraphQL infrastructure.
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ABOUT THIS PACK
What’s in the AWS AIP-C01 pack
136 questions across all five AIP-C01 domains
Answers and explanations for every question, including the wrong options
A questions-only PDF for timed practice runs
Instant delivery by email the moment you check out
Free monthly updates for as long as the exam is live
Pass or your money back
A failed AIP-C01 attempt costs another US$300, plus the time it takes to restudy. This pack is US$39, paid once, and refunded if you fail.
What makes the AIP-C01 hard
Early sitters call this the hardest AWS exam since Advanced Networking Specialty. It is new, and the difficulty is not exotic services, it is compounding requirements: every scenario stacks data residency, privacy, cost ceilings, latency and scale on top of each other, and all four answers technically work. You need Bedrock in depth (knowledge bases, agents, guardrails, evaluations, fine-tuning versus RAG) plus vector store selection, prompt and cost optimisation, and the operational layer of monitoring, CI/CD for GenAI and incident handling.
65 scored questions plus 10 unscored, 750 to pass. This pack has 136 practice questions for the AIP-C01, and with a bank this new, seeing the real scenarios in advance is the whole edge.
About the exam
AIP-C01 certifies building and operating production generative AI solutions on AWS: integrating foundation models into applications and business workflows with Bedrock, RAG architectures, agents and GenAI operations. AWS recommends two or more years building on AWS plus hands-on GenAI experience. Valid for three years.
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Damon Reddy –
AIP-C01 passed! Happy with the result.