AWS Certified Generative AI Developer – Professional (AIP-C01) Overview
The AWS Certified Generative AI Developer – Professional (AIP-C01) is AWS’s professional-level certification for developers who build and deploy production-ready Generative AI solutions. Launched in 2025, this certification validates your ability to integrate foundation models into applications, implement RAG architectures, design agentic AI systems, and operationalize GenAI solutions on AWS.
| Exam Detail | Information |
|---|---|
| Exam Code | AIP-C01 |
| Full Name | AWS Certified Generative AI Developer – Professional |
| Level | Professional |
| Number of Questions | 75 (+ 10 unscored) |
| Duration | 180 minutes |
| Passing Score | 750 / 1000 |
| Cost | $300 USD |
| Format | Multiple choice & multiple response |
| Testing | Pearson VUE (center or online proctored) |
| Languages | English, Japanese, Korean, Simplified Chinese |
| Validity | 3 years |
Target Candidate Profile
- 2+ years building production-grade applications on AWS
- 1+ year hands-on experience implementing Generative AI solutions
- Experience with AWS compute, storage, networking, and security services
- Understanding of AWS deployment, IaC tools, and monitoring services
- Familiarity with AI/ML concepts and data engineering
Recommended prior certifications (not required): AWS Certified AI Practitioner (AIF-C01), AWS Solutions Architect Associate, AWS Machine Learning Engineer Associate
AIP-C01 Exam Domains & Weightings
| Domain | Weight | Key Topics |
|---|---|---|
| Domain 1: Foundation Model Integration, Data Management & Compliance | 31% | RAG implementation, vector stores, prompt engineering, FM selection & customization, data pipelines |
| Domain 2: Implementation & Integration | 26% | Agentic AI, tool integrations, model deployment, enterprise integration, CI/CD, troubleshooting |
| Domain 3: AI Safety, Security & Governance | 20% | Data privacy, model security, Guardrails, responsible AI, compliance, access control |
| Domain 4: Operational Efficiency & Optimization | 12% | Cost optimization, performance tuning, scaling, monitoring, A/B testing |
| Domain 5: Testing, Validation & Troubleshooting | 11% | Model evaluation metrics, benchmarking, quality assurance, debugging |
Domain 1: Foundation Model Integration, Data Management & Compliance (31%)
This is the largest domain and covers the core of building GenAI solutions on AWS.
Key Topics
- Solution Design: Architecture design using FMs, proof-of-concept implementations, Well-Architected Framework GenAI Lens
- FM Selection & Configuration: Model benchmarking, cross-region inference, fine-tuning (LoRA, adapters), model lifecycle management via SageMaker Model Registry
- Data Pipelines: Data validation workflows (AWS Glue Data Quality), multimodal data processing, input formatting for FM inference
- Vector Stores: Vector database architecture (OpenSearch, Aurora pgvector, Bedrock Knowledge Bases), metadata frameworks, embedding solutions (Amazon Titan Embeddings)
- Retrieval Mechanisms (RAG): Document chunking strategies, hybrid search (keyword + vector), reranking models, query expansion & decomposition
- Prompt Engineering & Governance: Amazon Bedrock Prompt Management, parameterized templates, prompt flows, chain-of-thought patterns, quality assurance
AWS Services to Study
- Amazon Bedrock – FM access, Prompt Management, Prompt Flows
- Amazon Bedrock Knowledge Bases – Managed RAG, vector stores, chunking
- Amazon Nova Models – AWS first-party foundation models
- Amazon SageMaker AI – Model deployment, fine-tuning, registry
- Amazon OpenSearch Service – Vector search, Neural plugin
- Amazon Titan Embeddings – Text & multimodal embeddings
Domain 2: Implementation & Integration (26%)
This domain focuses on building production systems with agentic AI and enterprise integrations.
Key Topics
- Agentic AI: Bedrock Agents, Strands Agents, AWS Agent Squad, MCP (Model Context Protocol), ReAct patterns, multi-agent systems
- Tool Integrations: Function calling, MCP servers (Lambda & ECS), custom tool behaviors, error handling
- Model Deployment: Lambda for on-demand inference, Bedrock provisioned throughput, SageMaker endpoints, container-based deployment
- Enterprise Integration: API Gateway, EventBridge event-driven architectures, CI/CD pipelines (CodePipeline, CodeBuild), GenAI gateway architectures
- Troubleshooting: Context window overflow, prompt debugging, retrieval system diagnostics, embedding drift monitoring
AWS Services to Study
- Amazon Bedrock Agents – Autonomous AI agents with tool use
- Amazon Q Developer – AI-powered development assistant
- AWS Step Functions – Workflow orchestration for AI pipelines
- AWS Lambda – Serverless inference, MCP servers
- Amazon API Gateway – Enterprise API integrations
- AWS CodePipeline / CodeBuild – CI/CD for GenAI
Domain 3: AI Safety, Security & Governance (20%)
Security and responsible AI are critical at the professional level.
Key Topics
- Data Privacy: Data encryption (at rest/in transit), PII detection and redaction, data residency compliance
- Model Security: IAM least-privilege access to FMs, identity federation, role-based access control
- Guardrails: Amazon Bedrock Guardrails – content filtering, topic denial, PII redaction, grounding checks
- Responsible AI: Bias detection, fairness evaluation, transparency, human-in-the-loop workflows
- Compliance: Cross-jurisdiction deployments (Outposts, Wavelength), audit logging (CloudTrail), governance frameworks
AWS Services to Study
- Amazon Bedrock Guardrails – Content filtering, responsible AI controls
- AWS IAM – Fine-grained access control for AI services
- AWS CloudTrail – Audit logging for AI operations
- AWS KMS – Encryption key management
- Amazon Macie – PII detection in data stores
Domain 4: Operational Efficiency & Optimization (12%)
Key Topics
- Cost Optimization: Model cascading (smaller models for simple tasks), provisioned throughput vs. on-demand, right-sizing
- Performance Tuning: Latency optimization, token processing capacity, GPU utilization
- Scaling: Auto-scaling SageMaker endpoints, Bedrock cross-region inference, load balancing
- Monitoring: CloudWatch metrics for AI workloads, observability pipelines (X-Ray), drift detection
Domain 5: Testing, Validation & Troubleshooting (11%)
Key Topics
- Model Evaluation: Relevance scoring, hallucination detection, semantic drift, RAGAS metrics
- Agent Evaluation: Task completion rates, tool usage effectiveness, Amazon Bedrock Agent evaluations
- Retrieval Quality: Context matching verification, retrieval latency, embedding quality diagnostics
- Deployment Validation: A/B testing, canary deployments, synthetic user workflows, automated quality checks
Recommended Study Resources
Video Courses
| Course | Platform | Notes |
|---|---|---|
| Ultimate AWS Certified Generative AI Developer Professional by Stephane Maarek | Udemy | Comprehensive course with hands-on labs and 75-question practice exam |
| AWS Certified Generative AI Developer Professional AIP-C01 | Udemy | Security, governance, cost optimization focus |
| Exam Prep: AWS Certified Generative AI Developer | AWS Skill Builder | Official AWS exam prep (free with subscription) |
| Generative AI Developer Professional | KodeKloud | Hands-on labs with AWS sandbox environments |
Practice Tests
| Resource | Platform | Questions |
|---|---|---|
| [Practice Exams] AWS Certified Generative AI Developer Pro by Stephane Maarek & Abhishek Singh | Udemy | Multiple full-length exams with explanations |
| AWS Certification Official Practice Question Set | AWS Skill Builder | 20 official questions (free) |
| AWS Certification Official Pretest | AWS Skill Builder | Full-length readiness assessment |
| Whizlabs AIP-C01 Practice Tests | Whizlabs | Multiple practice exams with explanations |
Documentation & Reading
- Official AIP-C01 Exam Guide – Read this first!
- AWS AI & Generative AI Services Cheat Sheet – Quick reference for all AWS AI services
- Amazon Bedrock Deep Dive
- Bedrock Agents, Knowledge Bases & Guardrails
- Amazon Nova Models
- AWS SageMaker
- Amazon Q Developer & Q Business
- AWS Well-Architected Generative AI Lens
10-Week Study Plan
| Week | Focus Area | Activities |
|---|---|---|
| Week 1 | Exam Overview & Foundations | Read exam guide, review AI Services Cheat Sheet, understand all 5 domains and weightings |
| Week 2 | Amazon Bedrock Core | Study Amazon Bedrock, FM selection, model invocation APIs, Nova models, Titan Embeddings |
| Week 3 | RAG & Vector Stores | Study Bedrock Knowledge Bases, chunking strategies, OpenSearch vector search, hybrid search, reranking |
| Week 4 | Prompt Engineering & Fine-tuning | Bedrock Prompt Management, Prompt Flows, chain-of-thought, LoRA fine-tuning, SageMaker model customization |
| Week 5 | Agentic AI & Tool Integration | Study Bedrock Agents, Strands Agents, MCP, function calling, multi-agent orchestration, ReAct patterns |
| Week 6 | Enterprise Integration & Deployment | API Gateway integration, Step Functions workflows, CI/CD for GenAI (CodePipeline), container deployment patterns, Q Developer |
| Week 7 | Security, Governance & Responsible AI | Bedrock Guardrails, IAM for AI services, data privacy, PII handling, compliance, responsible AI practices |
| Week 8 | Optimization & Monitoring | Cost optimization (model cascading, provisioned throughput), performance tuning, CloudWatch metrics, X-Ray observability |
| Week 9 | Testing, Evaluation & Troubleshooting | Model evaluation metrics, agent evaluations, retrieval quality testing, deployment validation, debugging GenAI apps |
| Week 10 | Review & Practice Exams | Take 2-3 full practice exams, review weak areas, re-read exam guide, focus on scenario-based questions |
Study Tips
- Hands-on practice is essential – This is a professional-level exam; build actual RAG pipelines and deploy agents on AWS
- Focus on Domain 1 & 2 – Together they represent 57% of the exam
- Understand scenario-based questions – Questions are long and test architectural decision-making, not memorization
- Know the trade-offs – When to use Bedrock vs. SageMaker, on-demand vs. provisioned throughput, different chunking strategies
- Practice with time management – 180 minutes for 75 complex questions means ~2.4 minutes per question
AIP-C01 Practice Questions
Question 1
A company is building a customer support chatbot using Amazon Bedrock. The chatbot needs to answer questions based on 50,000 internal product documents that are updated weekly. The solution must minimize hallucinations and provide source citations. Which architecture best meets these requirements?
- Fine-tune a foundation model on all product documents monthly
- Use Amazon Bedrock Knowledge Bases with automatic chunking, vector store synchronization, and source attribution enabled
- Include all product documents in the system prompt for each request
- Train a custom model using Amazon SageMaker with the product documents as training data
Show Answer
Answer: B – Amazon Bedrock Knowledge Bases provides managed RAG with automatic document chunking, scheduled sync for weekly updates, vector store management, and built-in source attribution. Fine-tuning (A/D) doesn’t provide up-to-date factual recall, and including all documents in the prompt (C) exceeds context window limits.
Question 2
A developer is implementing an agentic AI solution that needs to query a company’s internal database, call external APIs, and generate reports. The solution must handle failures gracefully and maintain conversation state. Which combination of services should be used? (Select TWO)
- Amazon Bedrock Agents with action groups and Lambda functions
- Amazon Comprehend with custom entity recognition
- Amazon DynamoDB for conversation history and session state
- Amazon Kinesis Data Streams for real-time processing
- Amazon Rekognition for document analysis
Show Answer
Answer: A, C – Bedrock Agents with action groups handle tool orchestration (database queries, API calls) with built-in error handling and ReAct reasoning. DynamoDB stores conversation history for state management. Comprehend (B), Kinesis (D), and Rekognition (E) don’t address the agentic workflow requirements.
Question 3
An organization needs to ensure their GenAI application does not generate responses about competitor products, does not reveal PII from training data, and stays within approved topic boundaries. Which approach provides the MOST comprehensive solution?
- Implement input validation using AWS Lambda functions
- Configure Amazon Bedrock Guardrails with denied topics, PII filters, and content filters
- Use system prompts to instruct the model to avoid certain topics
- Fine-tune the model to remove knowledge about competitors
Show Answer
Answer: B – Amazon Bedrock Guardrails provides configurable denied topics, automated PII detection/redaction, and content filters that work at both input and output levels. System prompts (C) can be bypassed through prompt injection. Lambda validation (A) only handles input. Fine-tuning (D) cannot reliably remove specific knowledge.
Question 4
A team has deployed a GenAI application using Amazon Bedrock. After launch, they notice that response latency increases during peak hours and costs are 3x their budget. The application handles both simple FAQ queries and complex analytical questions. What is the MOST cost-effective optimization strategy?
- Switch all requests to the largest available model for better performance
- Implement model cascading: route simple queries to a smaller/cheaper model and complex queries to a larger model using a classification layer
- Purchase provisioned throughput for the maximum expected load
- Cache all responses in Amazon ElastiCache and serve cached answers for all queries
Show Answer
Answer: B – Model cascading routes simple queries to smaller, faster, cheaper models while reserving larger models for complex tasks. This optimizes both cost and latency. Using only the largest model (A) increases cost. Maximum provisioned throughput (C) over-provisions for average load. Caching all responses (D) doesn’t work for analytical questions requiring unique answers.
Question 5
A developer is building a RAG application and notices that retrieved documents are often irrelevant, leading to poor response quality. The documents are technical manuals with hierarchical structure (chapters, sections, subsections). Which combination of improvements will MOST effectively address retrieval quality? (Select TWO)
- Increase the chunk size to 10,000 tokens to capture more context
- Implement hierarchical chunking that preserves document structure and parent-child relationships
- Use hybrid search combining semantic vector search with keyword-based BM25 scoring
- Reduce the number of retrieved documents to 1 to increase precision
- Switch from vector search to simple keyword search
Show Answer
Answer: B, C – Hierarchical chunking preserves the document structure, maintaining context relationships between sections. Hybrid search combines the semantic understanding of vector search with the precision of keyword matching, improving relevance for technical content. Very large chunks (A) reduce precision. Only 1 document (D) may miss relevant information. Keyword-only search (E) loses semantic understanding.
Related Posts
- Amazon Bedrock
- Amazon Bedrock Agents, Knowledge Bases & Guardrails
- Amazon Nova Models
- AWS SageMaker
- Amazon Q Developer & Q Business
- AWS AI & Generative AI Services Cheat Sheet
References
- AWS Certified Generative AI Developer – Professional (Official Page)
- AIP-C01 Exam Guide (AWS Documentation)
- AWS Skill Builder – AIP-C01 Exam Prep Plan
Frequently Asked Questions
What is the AIP-C01 exam?
The AWS Certified AI Practitioner Professional (AIP-C01) validates ability to build, deploy, and operationalize generative AI solutions on AWS. It covers RAG implementation, agent design, MLOps, model security, and evaluation — requiring hands-on experience with Bedrock, SageMaker, and related services.
How does AIP-C01 differ from AIF-C01?
AIF-C01 (AI Practitioner) is foundational — testing conceptual knowledge of AI/ML. AIP-C01 (AI Professional) is advanced — testing hands-on ability to implement Gen AI solutions, fine-tune models, build agents, deploy with MLOps pipelines, and secure AI applications.
What experience do I need for AIP-C01?
AWS recommends 2+ years of hands-on experience building ML/Gen AI solutions on AWS, including working with Bedrock, SageMaker, and implementing RAG, fine-tuning, and agent architectures in production.