AWS Certified Generative AI Developer - Professional (AIP-C01) Fact Sheet¶

Exam Overview¶

Question scoring rules: - No penalty for guessing - always answer every question. - Multiple-response questions are all-or-nothing - you must select all correct responses to get credit. - Unscored questions are not identified during the exam.

Official references: - Exam page - Exam guide PDF - Exam guide HTML overview

Target Candidate¶

The target candidate should have:

• 2+ years of experience building production-grade applications on AWS or with open-source technologies
• General AI/ML or data engineering experience
• 1+ year of hands-on experience implementing GenAI solutions

Recommended AWS knowledge¶

• AWS compute, storage, networking services
• AWS security best practices and identity management (IAM)
• AWS deployment and infrastructure as code (CloudFormation, CDK, Terraform)
• AWS monitoring and observability (CloudWatch, X-Ray)
• AWS cost optimization principles

Job tasks out of scope for the candidate¶

• Model development and training from scratch
• Advanced ML techniques (math-heavy theory)
• Data engineering and feature engineering (deep Spark/Glue/EMR work)

If you don't already do these things in your day job, don't try to learn them in a week. The exam tests integrating FMs, not building them.

Exam Domains and Weightings¶

Domain 1: Foundation Model Integration, Data Management, and Compliance (31%)¶

Tasks (full breakdown in Domain 1 deep-dive): - 1.1 Analyze requirements and design GenAI solutions - 1.2 Select and configure FMs (incl. fine-tuning, LoRA, Model Registry, lifecycle) - 1.3 Implement data validation and processing pipelines for FM consumption - 1.4 Design and implement vector store solutions - 1.5 Design retrieval mechanisms for FM augmentation (RAG) - 1.6 Implement prompt engineering strategies and governance for FM interactions

Domain 2: Implementation and Integration (26%)¶

Tasks (full breakdown in Domain 2 deep-dive): - 2.1 Implement agentic AI solutions and tool integrations - 2.2 Implement model deployment strategies - 2.3 Design and implement enterprise integration architectures - 2.4 Implement FM API integrations - 2.5 Implement application integration patterns and development tools

Domain 3: AI Safety, Security, and Governance (20%)¶

Tasks (full breakdown in Domain 3 deep-dive): - 3.1 Implement input and output safety controls - 3.2 Implement data security and privacy controls - 3.3 Implement AI governance and compliance mechanisms - 3.4 Implement responsible AI principles

Domain 4: Operational Efficiency and Optimization for GenAI Applications (12%)¶

Tasks (full breakdown in Domain 4 deep-dive): - 4.1 Implement cost optimization and resource efficiency strategies - 4.2 Optimize application performance - 4.3 Implement monitoring systems for GenAI applications

Domain 5: Testing, Validation, and Troubleshooting (11%)¶

Tasks (full breakdown in Domain 5 deep-dive): - 5.1 Implement evaluation systems for GenAI - 5.2 Troubleshoot GenAI applications

Technologies and Concepts (from the official guide)¶

These concepts may appear on the exam, in no particular order of importance:

• Retrieval Augmented Generation (RAG)
• Vector databases and embeddings
• Prompt engineering and management
• Foundation model (FM) integration
• Agentic AI systems
• Responsible AI practices
• Content safety and moderation
• Model evaluation and validation
• Cost optimization for AI workloads
• Performance tuning for AI applications
• Monitoring and observability for AI systems
• Security and governance for AI applications
• API design and integration patterns
• Event-driven architectures
• Serverless computing
• Container orchestration
• Infrastructure as code (IaC)
• CI/CD for AI applications
• Hybrid cloud architectures
• Enterprise system integration

In-Scope AWS Services (from the official guide)¶

These services may appear on the exam. Memorize the Machine Learning category - that's where the bulk of testable detail lives.

Machine Learning (highest priority)¶

• Amazon Bedrock - Managed FM access, the central service for this exam
• Amazon Bedrock AgentCore - Production runtime for AI agents (newer; secure agent execution)
• Amazon Bedrock Knowledge Bases - Managed RAG with vector store + retrieval
• Amazon Bedrock Prompt Management - Prompt template repository + versioning
• Amazon Bedrock Prompt Flows - No-code conditional/sequential prompt orchestration
• Amazon Augmented AI (A2I) - Human review workflows for ML predictions
• Amazon Comprehend - NLP: entities, sentiment, PII detection, topic modeling
• Amazon Kendra - Intelligent enterprise search; can be a retriever for RAG
• Amazon Lex - Conversational AI / chatbot builder
• Amazon Q Business - GenAI assistant for internal data
• Amazon Q Business Apps - Custom apps inside Q Business
• Amazon Q Developer - GenAI coding assistant
• Amazon Rekognition - Computer vision (image/video)
• Amazon SageMaker AI - End-to-end ML platform; hosts custom/fine-tuned models
• Amazon SageMaker Clarify - Bias and explainability
• Amazon SageMaker Data Wrangler - Data prep for ML
• Amazon SageMaker Ground Truth - Data labeling (incl. RLHF labeling)
• Amazon SageMaker JumpStart - Pre-built models and solution templates
• Amazon SageMaker Model Monitor - Drift detection in production
• Amazon SageMaker Model Registry - Model versioning and approval workflow
• Amazon SageMaker Neo - Compile models for edge/optimized inference
• Amazon SageMaker Processing - Pre/post-processing jobs
• Amazon SageMaker Unified Studio - Unified ML/data IDE
• Amazon Textract - Document OCR + structure extraction
• Amazon Titan - AWS-built FM family (text, embeddings, multimodal)
• Amazon Transcribe - Speech-to-text

Application Integration¶

• Amazon AppFlow, AWS AppConfig, Amazon EventBridge, Amazon SNS, Amazon SQS, AWS Step Functions

Compute¶

• AWS App Runner, Amazon EC2, AWS Lambda, AWS Lambda@Edge, AWS Outposts, AWS Wavelength

Containers¶

• Amazon ECR, Amazon ECS, Amazon EKS, AWS Fargate

Customer Engagement¶

• Amazon Connect

Database¶

• Amazon Aurora (with pgvector for vector search), Amazon DocumentDB, Amazon DynamoDB (+ Streams), Amazon ElastiCache, Amazon Neptune, Amazon RDS

Developer Tools¶

• AWS Amplify, AWS CDK, AWS CLI, AWS CloudFormation, AWS CodeArtifact, AWS CodeBuild, AWS CodeDeploy, AWS CodePipeline, AWS Tools and SDKs, AWS X-Ray

Analytics¶

• Amazon Athena, Amazon EMR, AWS Glue, Amazon Kinesis, Amazon OpenSearch Service (with Neural plugin for vector search), Amazon QuickSight, Amazon MSK

Management and Governance¶

• AWS Auto Scaling, AWS Chatbot, AWS CloudTrail, Amazon CloudWatch (+ CloudWatch Logs, CloudWatch Synthetics), AWS Cost Anomaly Detection, AWS Cost Explorer, Amazon Managed Grafana, AWS Service Catalog, AWS Systems Manager, AWS Well-Architected Tool

Migration and Transfer¶

• AWS DataSync, AWS Transfer Family

Networking and Content Delivery¶

• Amazon API Gateway, AWS AppSync, Amazon CloudFront, ELB, AWS Global Accelerator, AWS PrivateLink, Amazon Route 53, Amazon VPC

Security, Identity, and Compliance¶

• Amazon Cognito, AWS Encryption SDK, IAM, IAM Access Analyzer, IAM Identity Center, AWS KMS, Amazon Macie, AWS Secrets Manager, AWS WAF

Storage¶

• Amazon EBS, Amazon EFS, Amazon S3 (+ Intelligent-Tiering, Lifecycle policies, Cross-Region Replication)

Out-of-Scope AWS Services (from the official guide)¶

Don't waste time studying these:

Pay attention to the ML out-of-scope list: the AIF-C01 (Foundational) covers some of these (Forecast, Fraud Detector, Lookout, etc.), but AIP-C01 does not. Don't carry over old study notes.

Service short-name policy¶

The exam uses official short names for well-known services to reduce reading load. For example, "Amazon Simple Notification Service (Amazon SNS)" appears as "Amazon SNS". There's a Help feature in the exam interface that lists short-name to full-name mappings for any service that appears with an abbreviation.

Last-week pacing reminders¶

• Read each domain note's "Exam tasks and skills" section first - that's the verbatim official blueprint. If you can't explain a Skill in plain English with the AWS services it uses, study it.
• Don't try to memorize service feature matrices. The exam tests scenario understanding ("which service or pattern fits this requirement?"), not feature checklists.
• Practice with timing: 205 min / 85 questions = ~2.4 min per question average. Read the question first, then the choices.
• For multiple-response questions, count the required number of correct answers the question states. If it says "select TWO", select exactly two.
• Eliminate distractors first. Pro-level exams use plausible-sounding wrong answers - identifying obviously wrong ones is faster than picking the best of two reasonable ones.