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AWS Certified Generative AI Developer - Professional AIP-C01

Here you have the best Amazon AWS Certified Generative AI Developer - Professional AIP-C01 practice exam questions

You have 85 total questions across 17 pages (5 per page)
These questions were last updated on March 12, 2026
This site is not affiliated with or endorsed by Amazon.

Question 1 of 85

A media company must use Amazon Bedrock to implement a robust governance process for AI-generated content. The company needs to manage hundreds of prompt templates. Multiple teams use the templates across multiple AWS Regions to generate content. The solution must provide version control with approval workflows that include notifications for pending reviews. The solution must also provide detailed audit trails that document prompt activities and consistent prompt parameterization to enforce quality standards.
Which solution will meet these requirements?

Configure Amazon Bedrock Studio prompt templates. Use Amazon CloudWatch to create dashboards that display prompt usage metrics. Store the approval status of content in Amazon DynamoDB. Use AWS Lambda functions to enforce approvals.

Use Amazon Bedrock Prompt Management to implement version control. Configure AWS CloudTrail for audit logging. Use IAM policies to control approval permissions. Create parameterized prompt templates by specifying variables.

Use AWS Step Functions to create an approval workflow. Store prompts as documents in Amazon S3. Use tags to implement version control. Use Amazon EventBridge to send notifications.

Deploy Amazon SageMaker Canvas with prompt templates that are stored in Amazon S3. Use AWS CloudFormation to implement version control. Use AWS Config to enforce approval policies.

Question 2 of 85

A medical company is building a generative AI (GenAI) application that uses RAG to provide evidence-based medical information. The application uses Amazon OpenSearch Service to retrieve vector embeddings. Users report that searches frequently miss results that contain exact medical terms and acronyms and return too many semantically similar but irrelevant documents. The company needs to improve retrieval quality and maintain low end user latency, even as the document collection grows to millions of documents.
Which solution will meet these requirements with the LEAST operational overhead?

Configure hybrid search by combining vector similarity with keyword matching to improve semantic understanding and exact term and acronym matching.

Increase the dimensions of the vector embeddings from 384 to 1536. Use a post-processing AWS Lambda function to filter out irrelevant results after retrieval.

Replace OpenSearch Service with Amazon Kendra. Use query expansion to handle medical acronyms and terminology variants during pre-processing.

Implement a two-stage retrieval architecture in which initial vector search results are re-ranked by an ML model that is hosted on Amazon SageMaker AI.

Question 3 of 85

A healthcare company is using Amazon Bedrock to build a system to help practitioners make clinical decisions. The system must provide treatment recommendations to physicians based only on approved medical documentation and must cite specific sources. The system must not hallucinate or produce factually incorrect information.
Which solution will meet these requirements with the LEAST operational overhead?

Integrate Amazon Bedrock with Amazon Kendra to retrieve approved documents. Implement custom post-processing to compare generated responses against source documents and to include citations.

Deploy an Amazon Bedrock knowledge base and connect it to approved clinical source documents. Use the Amazon Bedrock RetrieveAndGenerate API to return citations from the knowledge base.

Use Amazon Bedrock and Amazon Comprehend Medical to extract medical entities. Implement verification logic against a medical terminology database.

Use an Amazon Bedrock knowledge base with Retrieve API calls and InvokeModel API calls to retrieve approved clinical source documents. Implement verification logic to compare against retrieved sources and to cite sources.

Question 4 of 85

A company uses an organization in AWS Organizations with all features enabled to manage multiple AWS accounts. Employees use Amazon Bedrock across multiple accounts. The company must prevent specific topics and proprietary information from being included in prompts to Amazon Bedrock models. The company must ensure that employees can use only approved Amazon Bedrock models. The company centrally manages IAM roles for employees.
Which combination of solutions will meet these requirements? (Choose two.)

Create an IAM permissions boundary for each employee's IAM role. Configure the permissions boundary to require an approved Amazon Bedrock guardrail identifier to invoke Amazon Bedrock models. Create an SCP that allows employees to use only approved models.

Create an SCP that allows employees to use only approved models. Configure the SCP to require employees to specify a guardrail identifier in calls to invoke an approved model.

Create an SCP that prevents an employee from invoking a model if a centrally deployed guardrail identifier is not specified in a call to the model. Create a permissions boundary on each employee's IAM role that allows each employee to invoke only approved models.

Use AWS CloudFormation to create a custom Amazon Bedrock guardrail that has a block filtering policy. Use stack sets to deploy the guardrail to each account in the organization.

Use AWS CloudFormation to create a custom Amazon Bedrock guardrail that has a mask filtering policy. Use stack sets to deploy the guardrail to each account in the organization.

Question 5 of 85

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?

Configure VPC endpoints for Amazon Bedrock model API calls. Implement Amazon Bedrock Guardrails to filter harmful or unauthorized content in prompts and responses. Use Amazon Bedrock trace events to track all agent and model invocations for auditing purposes. Export the traces to Amazon CloudWatch Logs as an audit record of model usage. Store all prompts and outputs in Amazon S3 with server-side encryption with AWS KMS keys (SSE-KMS).

Define access control by using IAM with attribute-based controls to map departments to specific permissions. Configure VPC endpoints for Amazon Bedrock model API calls. Use IAM condition keys to enforce specific GuardrailIdentifier and ModelId values. Configure AWS CloudTrail to capture management and data events for S3 objects and KMS key usage activities. Enable S3 server access logging to record detailed file-level interactions with the video archives. Send all CloudTrail logs to AWS CloudTrail Lake. Set up Amazon CloudWatch alarms to detect and alert on unexpected activity from Amazon Bedrock, Amazon Rekognition, and AWS KMS.

Restrict access to services by using VPC endpoint policies. Use AWS Config to track resource changes and compliance with security rules. Use server-side encryption with AWS KMS keys (SSE-KMS) to encrypt data at rest. Store the model's I/O in separate Amazon S3 buckets. Enable S3 server access logging to track file-level interactions.

Configure AWS CloudTrail Insights to analyze API call patterns across accounts and detect anomalous activity in Amazon Bedrock, Amazon Rekognition, Amazon S3, and AWS KMS. Deploy Amazon Macie to scan and classify the video archive. Use server-side encryption with AWS KMS keys (SSE-KMS) to encrypt all stored data. Configure CloudTrail to capture KMS API usage events for audit purposes. Configure Amazon EventBridge rules to process CloudTrail Insights anomalies and Macie findings. Use CloudWatch alarms to trigger automated notifications and security responses when potential security issues are detected.

About the Amazon AWS Certified Generative AI Developer - Professional AIP-C01 Certification Exam

About the Exam

The Amazon AWS Certified Generative AI Developer - Professional AIP-C01 (AWS Certified Generative AI Developer - Professional AIP-C01) validates your knowledge and skills. Passing demonstrates proficiency and can boost your career prospects in the field.

How to Prepare

Work through all 85 practice questions across 17 pages. Focus on understanding the reasoning behind each answer rather than memorizing responses to be ready for any variation on the real exam.

Why Practice Exams?

Practice exams help you familiarize yourself with the question format, manage your time, and reduce anxiety on the test day. Our AWS Certified Generative AI Developer - Professional AIP-C01 questions are regularly updated to reflect the latest exam objectives.