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AIP-C01 Practice Exam: AWS Certified Generative AI Developer - Professional

136 questions · 180 min timed mode · AWS · Updated 2026

Professional-level GenAI on AWS: Bedrock, agents, RAG pipelines, guardrails, evaluation — build production AI, not demos.

What you'll be tested on

Sample AIP-C01 questions

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?
  1. 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.
  2. 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.
  3. 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.
  4. 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 answerC — 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.
SageMaker Clarify provides built-in bias and fairness metrics (such as demographic disparity measures) that can be computed on model outputs and published to CloudWatch, giving real-time metric collection, threshold alarms for a 15% discrepancy, and dashboards for weekly prompt comparison reports with minimal custom code. Option A requires heavy custom Lambda post-processing to derive fairness metrics. Option B misuses guardrail content filters, which detect harmful content rather than measure demographic fairness, and InvocationsIntervened is not a per-group fairness metric. Option D uses point-in-time evaluation jobs rather than continuous monitoring, and relies on the wrong metric dimensions.
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.
  1. Use AWS Amplify AI Kit to implement streaming responses from the GraphQL API and to optimize client- side rendering.
  2. Increase the timeout value of the Lambda resolver. Implement retry logic with exponential backoff.
  3. Update the application to send an API request to an Amazon SQS queue. Update the AWS AppSync resolver to poll and process the queue.
  4. 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 answerA — Use AWS Amplify AI Kit to implement streaming responses from the GraphQL API and to optimize client- side rendering.
The RequestResponse invocation waits for the full Bedrock response, so long generations exceed Lambda and AppSync timeouts. AWS Amplify AI Kit provides a managed way to stream model tokens from the GraphQL API to the React client, cutting time-to-first-byte, eliminating timeouts, and improving perceived performance with minimal rework. Option B only raises the timeout ceiling without fixing slow responses. Option C introduces queue polling complexity that harms interactivity. Option D can work but forces the company to build and manage custom WebSocket infrastructure through API Gateway instead of using the purpose-built Amplify AI Kit integration.
An ecommerce company operates a global product recommendation system that needs to switch between multiple foundation models (FM) 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.
  1. Deploy an AWS Lambda function that uses environment variables to store routing rules and Amazon Bedrock FM IDs. Use the Lambda console to update the environment variables when business requirements change. Configure an Amazon API Gateway REST API to read request parameters to make routing decisions.
  2. Deploy Amazon API Gateway REST API request transformation templates to implement routing logic based on request attributes. Store Amazon Bedrock FM endpoints as REST API stage variables. Update the variables when the system switches between models.
  3. Configure an AWS Lambda function to fetch routing configurations from the AWS AppConfig Agent for each user request. Run business logic in the Lambda function to select the appropriate FM for each request. Expose the FM through a single Amazon API Gateway REST API endpoint.
  4. Use AWS Lambda authorizers for an Amazon API Gateway REST API to evaluate routing rules that are stored in AWS AppConfig. Return authorization contexts based on business logic. Route requests to model- specific Lambda functions for each Amazon Bedrock FM.
Show answerC — Configure an AWS Lambda function to fetch routing configurations from the AWS AppConfig Agent for each user request. Run business logic in the Lambda function to select the appropriate FM for each request. Expose the FM through a single Amazon API Gateway REST API endpoint.
AWS AppConfig centrally stores routing rules and propagates changes immediately to thousands of concurrent Lambda executions via the AppConfig Agent, so cost thresholds, compliance rules, and A/B allocations update hourly without redeployment. Lambda executes the complex per-request business logic, and a single API Gateway endpoint means foundation model switching needs no code changes. Option A stores rules in environment variables, requiring manual console edits and lacking rapid propagation. Option B mapping templates cannot express complex, data-driven routing logic. Option D misuses Lambda authorizers for routing and multiplies model-specific endpoints, adding complexity and deployment coupling.

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FAQ

How many practice questions are in this AIP-C01 bank?
136 questions covering the current AIP-C01 AWS Certified Generative AI Developer - Professional syllabus, every one with the correct answer and an explanation.
How long is the real AIP-C01 exam?
The official AIP-C01 exam gives you 180 minutes. Our timed exam mode uses the same limit so the pace feels familiar.
What does AIP-C01 access cost?
Plans start at $3.99 for 3 months. One payment, no subscription — and far cheaper than retaking the real exam.