AWS Certified Generative AI Developer - Professional AIP-C01

By Amazon · Browse Mode
About the Exam

This professional-level exam is intended for GenAI developers and AI practitioners with hands-on experience building production-grade applications. It validates the ability to integrate foundation models into applications and business workflows and to design, implement, and deploy generative AI solutions on AWS. Passing demonstrates practical knowledge of topics such as RAG, prompt engineering, agentic AI, security and governance, and GenAI application optimization.

Exam Topics

  • Foundation Model Integration, Data Management, and Compliance31%
  • Implementation and Integration26%
  • AI Safety, Security, and Governance20%
  • Operational Efficiency and Optimization for GenAI Applications12%
  • Testing, Validation, and Troubleshooting11%

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Last updated September 3, 2026 at 5:45 PM

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QuestionQ1

Operational Efficiency and Optimization for GenAI Applications

A company plans to establish an annual customer rewards program. The rewards customers earn differ according to various parameters, including the categories of items they order and the customers' purchase history.

The company requires a generative AI (GenAI) solution that uses three Amazon Bedrock agents to assist customers while they browse an online catalog. The agents must use knowledge bases and action groups to manage the search, recommendation, and order modules. The modules must run sequentially. An AWS Lambda function must calculate estimated rewards for every recommended item. The solution must offer graceful degradation during service disruptions.

Which solution meets these requirements with the MOST operational efficiency?

Explanation

AWS Step Functions provides managed orchestration for sequential service tasks and supports task-level Retry and Catch error handling. A state machine can invoke the three Amazon Bedrock agents in order and then invoke the rewards-calculation Lambda function. Configuring retry and catch branches for each task enables targeted recovery or fallback behavior when an individual agent or function fails, without maintaining custom Lambda orchestration and error-handling code.

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QuestionQ2

AI Safety, Security, and Governance

A company uses an AWS Organizations organization with all features enabled to manage multiple AWS accounts. Employees use Amazon Bedrock in multiple accounts. The company must prevent particular topics and proprietary information from being included in prompts submitted to Amazon Bedrock models. The company must ensure that employees can use only approved Amazon Bedrock models. The company centrally administers IAM roles for employees.

Which combination of solutions will satisfy these requirements?

Choose two
Explanation

Amazon Bedrock Guardrails can block denied topics and sensitive information in model input prompts. A block filtering policy prevents the content from being processed, whereas a mask policy redacts detected values. Deploying the guardrail configuration through AWS CloudFormation StackSets provides consistent central deployment to member accounts. An SCP can explicitly deny model invocations that do not include the required guardrail identifier, while permissions boundaries on centrally managed employee roles restrict invocation permissions to approved model resources.

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QuestionQ3

Foundation Model Integration, Data Management, and Compliance

A company is developing a generative AI (GenAI) application that generates content from a range of internal and external data sources. The company needs to ensure that all generated output is fully traceable. The application must support registering data sources and allow metadata tagging to attribute content to its original source. It must also retain audit logs of data access and usage across the entire pipeline.

Which solution meets these requirements?

Explanation

AWS Glue Data Catalog centralizes data-source registration and metadata management, including tags that can preserve source attribution. AWS CloudTrail records API activity across AWS services, providing an audit trail for data access and pipeline activity.

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QuestionQ4

Operational Efficiency and Optimization for GenAI Applications

A company is developing a generative AI (GenAI) application that uses Amazon Bedrock APIs to process complex customer inquiries. During periods of peak use, the application has intermittent API timeouts that result in issues such as broken response chunks and delayed data delivery. The application has difficulty ensuring prompts stay within token limits when processing complex customer inquiries of different lengths. Users have reported truncated inputs and incomplete responses. The company has also identified foundation model (FM) invocation failures.

The company requires a retry strategy that automatically manages transient service errors and avoids overwhelming Amazon Bedrock during peak usage periods. The strategy must adapt to changing service availability and support response streaming and token-aware request handling.

Which solution meets these requirements?

Explanation

The requirement to "adapt to changing service availability" without overwhelming Bedrock points to the AWS SDK adaptive retry mode, which layers dynamic client-side rate limiting (a token bucket that adjusts the call rate based on observed throttling) on top of standard mode's exponential backoff with jitter and circuit-breaking. Option B combines adaptive retries, exponential backoff with jitter, and a circuit breaker with a streaming handler that buffers received chunks and resumes from the last chunk, satisfying transient-error handling, streaming support, and adaptability. Option C fixes the retry mode to "standard," which does not adapt the call rate to changing availability, and its "return cached completions for failed streaming requests" would serve stale or incorrect data. Options A (fixed 1-second delay) and D (static timeouts/caps) are neither adaptive nor jittered.

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QuestionQ5

Operational Efficiency and Optimization for GenAI Applications

An enterprise application uses an Amazon Bedrock foundation model (FM) to process and analyze technical documents of 50 to 200 pages. Users experience inconsistent responses and truncated outputs when processing documents that exceed the FM's context-window limits.

Which solution will solve this problem?

Explanation

Amazon Bedrock Knowledge Bases hierarchical chunking indexes smaller child chunks for precise retrieval and replaces retrieved child chunks with their broader parent chunks, providing the FM with more comprehensive relevant context while keeping each inference within its context window. Bedrock semantic chunking does not support a buffer size of 3; its valid buffer-size range is 0–1.

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