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AWS Generative AI Developer Professional Study Guide

Domain-by-domain study guide for AWS Certified Generative AI Developer - Professional (AIP-C01). Covers all 5 exam objectives with topic checklists, quick notes, and practice links.

Download PDF Notes

Exam Overview

Key facts before you start studying

Duration

180 min

Questions

Exam Code

AIP-C01

Domains

About this exam

Citation-ready facts for AWS Generative AI Developer Professional (AIP-C01)

AWS Generative AI Developer Professional (AIP-C01) is a AWS certification exam with about 65 questions over 180 minutes, typically requiring a scaled score of 750 to pass. Official domain weights on CertStud: Foundation Model Integration with Amazon Bedrock (31%); GenAI Application Implementation and Integration (26%); AI Safety, Security, and Governance (20%); Operational Efficiency and Cost Optimization (12%); Testing and Validation (11%).

Use this study guide with free practice questions, the week-by-week study plan, and detailed notes. Confirm current objectives and scoring on the official AWS exam page before booking.

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Jump to a specific exam domain

1. Foundation Model Integration with Amazon Bedrock (31%)
2. GenAI Application Implementation and Integration (26%)
3. AI Safety, Security, and Governance (20%)
4. Operational Efficiency and Cost Optimization (12%)
5. Testing and Validation (11%)

Domain 1

31%

Foundation Model Integration with Amazon Bedrock

Select and integrate foundation models using Amazon Bedrock APIs and features

Topics to Master

Amazon Bedrock model catalog and selection criteria
Bedrock API invocation and streaming responses
Model comparison: Anthropic Claude, Amazon Titan, Meta Llama, Cohere
Prompt engineering: zero-shot, few-shot, chain-of-thought
Amazon Bedrock Agents and multi-step task orchestration
Bedrock Knowledge Bases for RAG implementation
Embedding models and vector store integration
Amazon Bedrock Guardrails for content filtering
Model customization: fine-tuning and continued pre-training
Bedrock Flows for no-code GenAI workflow orchestration

Domain 2

26%

GenAI Application Implementation and Integration

Build, integrate, and deploy generative AI applications on AWS

Topics to Master

RAG (Retrieval-Augmented Generation) architecture patterns
Vector databases: Amazon OpenSearch Serverless, pgvector, Pinecone
LangChain and LlamaIndex integration with Bedrock
Bedrock Converse API for multi-turn conversations
Tool use (function calling) with Bedrock agents
Structured output and JSON schema enforcement
AWS Lambda for serverless GenAI invocation
Amazon API Gateway for GenAI API exposure
Streaming responses and WebSocket integration
Multi-modal applications: text, image, and document processing

Domain 3

20%

AI Safety, Security, and Governance

Implement responsible AI practices and govern GenAI deployments

Topics to Master

Amazon Bedrock Guardrails: topic denial, content filters, PII redaction
Responsible AI principles: fairness, explainability, privacy
Hallucination detection and grounding techniques
AWS IAM for Bedrock permissions and resource policies
Data encryption and VPC endpoint for Bedrock
Audit logging with AWS CloudTrail for Bedrock calls
Model evaluation and bias detection
Watermarking and provenance for AI-generated content
Compliance frameworks for AI systems
Human-in-the-loop review workflows

Domain 4

12%

Operational Efficiency and Cost Optimization

Optimize GenAI workloads for performance, cost, and scalability

Topics to Master

Bedrock Provisioned Throughput for latency-sensitive workloads
Token usage optimization and context window management
Caching strategies for repeated GenAI requests
Amazon CloudWatch metrics for Bedrock monitoring
Cost estimation for on-demand vs provisioned throughput
Batch inference for high-volume, asynchronous workloads
Latency optimization techniques for real-time applications
Auto-scaling patterns for GenAI applications

Domain 5

11%

Testing and Validation

Evaluate, test, and validate generative AI applications

Topics to Master

Amazon Bedrock Model Evaluation for automated benchmarking
Human evaluation workflows and annotation
RAGAS and other RAG evaluation frameworks
A/B testing for model comparison
Prompt regression testing strategies
Output quality metrics: BLEU, ROUGE, BERTScore
End-to-end integration testing for Bedrock agents
Load testing GenAI APIs with realistic traffic patterns

Continue Your Prep

Free CertStud resources for AIP-C01

Study Notes Exam Study Plan Start Practice Test

Back to AWS Generative AI Developer Professional

AWS Generative AI Developer Professional Study Guide

Domain-by-domain study guide for AWS Certified Generative AI Developer - Professional (AIP-C01). Covers all 5 exam objectives with topic checklists, quick notes, and practice links.

Download PDF Notes

Exam Overview

Key facts before you start studying

Duration

180 min

Questions

Exam Code

AIP-C01

Domains

About this exam

Citation-ready facts for AWS Generative AI Developer Professional (AIP-C01)

Use this study guide with free practice questions, the week-by-week study plan, and detailed notes. Confirm current objectives and scoring on the official AWS exam page before booking.

Quick Navigation

Jump to a specific exam domain

1. Foundation Model Integration with Amazon Bedrock (31%)
2. GenAI Application Implementation and Integration (26%)
3. AI Safety, Security, and Governance (20%)
4. Operational Efficiency and Cost Optimization (12%)
5. Testing and Validation (11%)

Domain 1

31%

Foundation Model Integration with Amazon Bedrock

Select and integrate foundation models using Amazon Bedrock APIs and features

Topics to Master

Amazon Bedrock model catalog and selection criteria
Bedrock API invocation and streaming responses
Model comparison: Anthropic Claude, Amazon Titan, Meta Llama, Cohere
Prompt engineering: zero-shot, few-shot, chain-of-thought
Amazon Bedrock Agents and multi-step task orchestration
Bedrock Knowledge Bases for RAG implementation
Embedding models and vector store integration
Amazon Bedrock Guardrails for content filtering
Model customization: fine-tuning and continued pre-training
Bedrock Flows for no-code GenAI workflow orchestration

Domain 2

26%

GenAI Application Implementation and Integration

Build, integrate, and deploy generative AI applications on AWS

Topics to Master

RAG (Retrieval-Augmented Generation) architecture patterns
Vector databases: Amazon OpenSearch Serverless, pgvector, Pinecone
LangChain and LlamaIndex integration with Bedrock
Bedrock Converse API for multi-turn conversations
Tool use (function calling) with Bedrock agents
Structured output and JSON schema enforcement
AWS Lambda for serverless GenAI invocation
Amazon API Gateway for GenAI API exposure
Streaming responses and WebSocket integration
Multi-modal applications: text, image, and document processing

Domain 3

20%

AI Safety, Security, and Governance

Implement responsible AI practices and govern GenAI deployments

Topics to Master

Amazon Bedrock Guardrails: topic denial, content filters, PII redaction
Responsible AI principles: fairness, explainability, privacy
Hallucination detection and grounding techniques
AWS IAM for Bedrock permissions and resource policies
Data encryption and VPC endpoint for Bedrock
Audit logging with AWS CloudTrail for Bedrock calls
Model evaluation and bias detection
Watermarking and provenance for AI-generated content
Compliance frameworks for AI systems
Human-in-the-loop review workflows

Domain 4

12%

Operational Efficiency and Cost Optimization

Optimize GenAI workloads for performance, cost, and scalability

Topics to Master

Bedrock Provisioned Throughput for latency-sensitive workloads
Token usage optimization and context window management
Caching strategies for repeated GenAI requests
Amazon CloudWatch metrics for Bedrock monitoring
Cost estimation for on-demand vs provisioned throughput
Batch inference for high-volume, asynchronous workloads
Latency optimization techniques for real-time applications
Auto-scaling patterns for GenAI applications

Domain 5

11%

Testing and Validation

Evaluate, test, and validate generative AI applications

Topics to Master

Amazon Bedrock Model Evaluation for automated benchmarking
Human evaluation workflows and annotation
RAGAS and other RAG evaluation frameworks
A/B testing for model comparison
Prompt regression testing strategies
Output quality metrics: BLEU, ROUGE, BERTScore
End-to-end integration testing for Bedrock agents
Load testing GenAI APIs with realistic traffic patterns

Continue Your Prep

Free CertStud resources for AIP-C01

Study Notes Exam Study Plan Start Practice Test