AWS Certified Generative AI Developer – Professional Practise Exam

Who it is for

• Candidates preparing for the AWS Certified Generative AI Developer – Professional Certification (AIP-C01).
• Practitioners who implement production GenAI applications on AWS and need practice integrating foundation models, RAG, agents, safety controls, and operational monitoring.

What this exam covers

• Foundation Model Integration, Data Management, and Compliance — 31%
• Implementation and Integration — 26%
• AI Safety, Security, and Governance — 20%
• Operational Efficiency and Optimization for GenAI Applications — 12%
• Testing, Validation, and Troubleshooting — 11%

What you will practice

• Select FMs on Amazon Bedrock, build RAG with Amazon Bedrock Knowledge Bases and Amazon OpenSearch Service, and manage prompts with Amazon Bedrock Prompt Management and Amazon Bedrock Guardrails.
• Implement agents with AWS Step Functions and AWS Lambda, deploy FMs with Amazon Bedrock and Amazon SageMaker AI, and integrate APIs with Amazon API Gateway and Amazon EventBridge.
• Apply Amazon Bedrock Guardrails, detect PII with Amazon Comprehend and Amazon Macie, and govern access with IAM and AWS CloudTrail.
• Reduce token cost with prompt caching and Amazon Bedrock provisioned throughput, cut latency with streaming Amazon Bedrock models, and monitor token usage with Amazon CloudWatch.
• Evaluate FMs with Amazon Bedrock Model Evaluations, and troubleshoot RAG and prompt issues with Amazon CloudWatch Logs and AWS X-Ray.

About this practice exam

The AWS Certified Generative AI Developer – Professional exam tests a candidate’s ability to integrate foundation models (FMs) into applications and business workflows and to implement generative AI (GenAI) solutions in production on AWS: design solutions that use vector stores, Retrieval Augmented Generation (RAG), knowledge bases, and other GenAI architectures; apply prompt engineering and management; implement agentic AI; optimize GenAI applications for cost, performance, and business value; implement security, governance, and Responsible AI practices; and troubleshoot, monitor, and evaluate FMs. This practice exam prepares you for AWS Certified Generative AI Developer – Professional (AIP-C01) across Foundation Model Integration, Data Management, and Compliance; Implementation and Integration; AI Safety, Security, and Governance; Operational Efficiency and Optimization for GenAI Applications; and Testing, Validation, and Troubleshooting.

The exam emphasizes selecting and configuring FMs with Amazon Bedrock, Amazon SageMaker AI, Amazon SageMaker JumpStart, and Amazon SageMaker Model Registry; building RAG with Amazon Bedrock Knowledge Bases, Amazon Titan embeddings, Amazon OpenSearch Service, and Amazon Aurora with the pgvector extension; applying prompt engineering with Amazon Bedrock Prompt Management, Amazon Bedrock Prompt Flows, and Amazon Bedrock Guardrails; implementing agentic AI with AWS Step Functions, AWS Lambda, Amazon API Gateway, Strands Agents, and AWS Agent Squad; securing and governing GenAI with IAM, Amazon Comprehend, Amazon Macie, AWS CloudTrail, and Amazon CloudWatch; optimizing cost and latency with Amazon Bedrock provisioned throughput, prompt caching, and Amazon CloudWatch token metrics; and evaluating and troubleshooting with Amazon Bedrock Model Evaluations, Amazon CloudWatch Logs, and AWS X-Ray. It does not primarily assess model development and training, advanced machine learning (ML) techniques, or data engineering and feature engineering.

The official AWS Certified Generative AI Developer – Professional (AIP-C01) exam is 75 questions in 180 minutes. This practice exam follows the five official Generative AI Developer – Professional domains and their published weights.

The certification validates understanding of:

• Foundation Model Integration, Data Management, and Compliance: architectural designs and proofs of concept with Amazon Bedrock and the AWS Well-Architected Framework Generative AI Lens; FM selection and customization with Amazon SageMaker AI, Amazon SageMaker Model Registry, AWS Lambda, Amazon API Gateway, AWS AppConfig, and Amazon Bedrock Cross Region Inference; data validation and processing with AWS Glue Data Quality, Amazon SageMaker Data Wrangler, Amazon SageMaker Processing, and Amazon Transcribe; vector stores and RAG with Amazon Bedrock Knowledge Bases, Amazon OpenSearch Service, Amazon RDS, Amazon DynamoDB, Amazon Aurora with the pgvector extension, and Amazon Titan embeddings; and prompt engineering and governance with Amazon Bedrock Prompt Management, Amazon Bedrock Prompt Flows, Amazon Bedrock Guardrails, Amazon S3, AWS CloudTrail, and Amazon CloudWatch Logs.
• Implementation and Integration: agentic AI with Strands Agents, AWS Agent Squad, Model Context Protocol (MCP), AWS Step Functions, and AWS Lambda; FM deployment with Amazon Bedrock provisioned throughput and Amazon SageMaker AI endpoints; enterprise integration with Amazon API Gateway, Amazon EventBridge, AWS CodePipeline, and AWS CodeBuild; FM APIs with Amazon Bedrock streaming APIs, Amazon SQS, and AWS X-Ray; and developer tooling with Amazon Q Developer, AWS Amplify, and Amazon Bedrock Prompt Flows.
• AI Safety, Security, and Governance: input and output safety with Amazon Bedrock Guardrails, Amazon Bedrock Knowledge Bases, Amazon Comprehend, and Amazon API Gateway; data security and privacy with Amazon VPC endpoints, IAM, Amazon Macie, and Amazon S3 Lifecycle; governance and lineage with Amazon SageMaker AI model cards, AWS Glue Data Catalog, AWS CloudTrail, and Amazon CloudWatch Logs; and responsible AI with Amazon Bedrock Prompt Management, Amazon Bedrock Prompt Flows, and LLM-as-a-judge evaluations.
• Operational Efficiency and Optimization for GenAI Applications: token and cost controls including prompt caching, context pruning, and Amazon Bedrock provisioned throughput; latency and throughput with streaming, batch inference, and latency-optimized Amazon Bedrock models; and monitoring with Amazon CloudWatch, Amazon Bedrock Model Invocation Logs, token usage metrics, and hallucination-rate tracking.
• Testing, Validation, and Troubleshooting: Amazon Bedrock Model Evaluations, A/B and canary testing, RAG and Amazon Bedrock Agent evaluations, and LLM-as-a-Judge techniques; and troubleshooting with Amazon CloudWatch Logs Insights, AWS X-Ray prompt observability pipelines, context window diagnostics, and retrieval and embedding quality checks.