AWS Certified Generative AI Developer – Professional Cheat Sheet (AIP-C01)
Every Bedrock feature, RAG and agent pattern, and safety / observability decision the AIP-C01 exam actually tests — organised by the five exam domains with reference architectures and runbooks
About This Cheat Sheet
- 14 pages
- Every in-scope AIP-C01 service
- For 2+ years of AWS development and prior AI/ML
- 5 reference architectures
- 23 answer-pattern scenarios
- Updated April 2026
Exam domains
Share of the scored AIP-C01 content
- 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%
Services covered
Amazon Bedrock
- Playgrounds
- Model Evaluation
- Guardrails
- Custom Models
- Bedrock Studio
- Knowledge Bases
- Bedrock Data Automation
- Prompt Management
- Prompt Flows
- Bedrock Agents
Bedrock model providers
- Amazon Nova
- Amazon Titan
- Anthropic Claude
- Meta Llama
- Mistral
- Cohere
- AI21
- Stability
Agents
- Bedrock AgentCore
- Strands Agents
- Agent Squad
- Amazon Augmented AI
Vector stores & databases
- OpenSearch Serverless
- OpenSearch Service
- Aurora
- RDS
- Neptune
- Neptune Analytics
- DocumentDB
- MemoryDB
- ElastiCache
- DynamoDB
SageMaker AI
- Unified Studio
- JumpStart
- Data Wrangler
- Ground Truth
- Clarify
- Model Monitor
- Model Registry
- Processing
- Neo
AI services & Amazon Q
- Comprehend
- Kendra
- Lex
- Rekognition
- Textract
- Transcribe
- Q Business
- Q Business Apps
- Q Developer
- Connect
Show 65 more servicesShow fewer
Compute
- Lambda
- Lambda@Edge
- App Runner
- EC2
- ECS
- EKS
- Fargate
- ECR
- Outposts
- Wavelength
Storage & data
- S3
- EBS
- EFS
- DataSync
- Transfer Family
- Glue
- EMR
- Kinesis
- MSK
- Athena
- QuickSight
Application integration
- EventBridge
- Step Functions
- SNS
- SQS
- AppFlow
- AppConfig
- Chatbot
API & edge
- API Gateway
- AppSync
- CloudFront
- Global Accelerator
- PrivateLink
- ELB
- Route 53
- Amplify
Security
- IAM
- IAM Access Analyzer
- IAM Identity Center
- Cognito
- KMS
- AWS Encryption SDK
- Secrets Manager
- Macie
- WAF
Observability & cost
- CloudWatch
- CloudWatch Logs
- CloudWatch Synthetics
- X-Ray
- CloudTrail
- Managed Grafana
- Systems Manager
- Service Catalog
- Auto Scaling
- Well-Architected Tool
- Cost Anomaly Detection
- Cost Explorer
DevOps
- CDK
- CloudFormation
- CodePipeline
- CodeBuild
- CodeDeploy
- CodeArtifact
- CLI
- AWS Tools & SDKs
Feature details
- Bedrock inference
- On-Demand · Provisioned Throughput · Batch Inference · Cross-Region Inference · Latency-Optimized Inference
- Guardrails
- Content, denied-topic, sensitive-info, contextual-grounding and prompt-injection filters
- Knowledge Bases
- Managed RAG · Supported vector stores · RetrieveAndGenerate vs Retrieve · Reranking
- Bedrock AgentCore
- Runtime / Gateway / Memory / Identity / Observability / Code Interpreter / Browser tool
- Vector stores
- OpenSearch Serverless with Neural plugin · Aurora pgvector · Neptune Analytics for GraphRAG · DynamoDB + Streams
- EC2
- p5/trn/inf/g-series · Capacity Blocks
- S3
- Intelligent-Tiering · Lifecycle · Cross-Region Replication
- Evaluation
- Bedrock Model Evaluation automatic + human · Clarify FM evaluation
- Well-Architected Tool
- GenAI lens
Concepts covered
- GenAI foundations
- GenAI vocabulary · Customisation ladder: prompt engineering → RAG → fine-tuning → continued pre-training → train from scratch
- Retrieval
- Chunking · Embedding · Hybrid search · Query transformation
- Prompt engineering
- Foundational + reasoning + structural patterns · Inference parameter settings
- Testing & metrics
- RAGAS-style RAG metrics · BLEU / ROUGE / METEOR / BERTScore · Perplexity
- Responsible AI & cost
- Responsible-AI controls · Responsible-AI checklist · Cost-control playbook · Multi-Region + resilience
- Reference architectures
- Document-grounded chatbot · Agentic assistant with internal tools · Fine-tuned domain model · Multimodal intake pipeline · Amazon Q Business deployment
- Runbooks
- Latency regression · Prompt-injection incident · KB data drift · Agent cost runaway
- Exam practice
- 23-scenario answer patterns · 10 common pitfalls
Built from the official AIP-C01 exam guide; every service on its in-scope list is audited present.
What's Inside
GenAI Vocabulary & Customisation Ladder
The GenAI terms the exam assumes, and the customisation ladder from prompting up to training a model from scratch.
- Foundation models, LLMs vs multimodal, embeddings, tokens + context window
- Inference parameters: temperature, top-p, top-k, max_tokens, stop sequences
- Frequency + presence penalty
- RAG, agents, Model Context Protocol (MCP)
- Customisation ladder: prompt engineering → RAG → fine-tuning → continued pre-training → train from scratch
- Cost and applicability guidance for each step
Amazon Bedrock Core
The Bedrock model catalogue, its inference modes and the built-in features for evaluating, guarding and customising models.
- Model providers: Amazon Nova + Titan; Anthropic Claude Haiku/Sonnet/Opus
- Meta Llama 3.x; Mistral Small/Large; AI21 Jamba; Stability SD 3.x
- Cohere Command R/R+ + Embed + Rerank
- Inference modes: On-Demand, Provisioned Throughput, Batch Inference (~50% cheaper)
- Cross-Region Inference, Latency-Optimized Inference
- Bedrock Playgrounds, Model Evaluation (automatic + human), Bedrock Studio
- Guardrails: content filters, denied topics, sensitive-info, contextual-grounding, prompt-attack, automated reasoning
- Custom Models: fine-tune + continued pre-training + distillation + import
Knowledge Bases, Prompt Management & Flows
Managed RAG with Bedrock Knowledge Bases, from sources and chunking to vector stores, plus prompt versioning and visual flows.
- Bedrock Knowledge Bases sources: S3 / Confluence / SharePoint / Salesforce / web crawler
- Chunking: default + fixed + semantic + hierarchical
- Vector stores: OpenSearch Serverless (default + managed), Aurora pgvector, Pinecone
- More vector stores: Redis Enterprise, MongoDB Atlas, Neptune Analytics
- RetrieveAndGenerate vs Retrieve; citations + filters + Bedrock rerank
- Bedrock Data Automation for complex docs
- Bedrock Prompt Management: versioning + approval workflows + CloudTrail
- Prompt Flows: visual DAG of prompt, Lambda, KB, condition, iterator, agent nodes
Amazon Bedrock Agents & AgentCore
The ways to build agents on AWS, from the managed Bedrock runtime to open-source SDKs and human review.
- Bedrock Agents: managed single-agent runtime with action groups + KBs + Guardrails
- Bedrock AgentCore: framework-agnostic production runtime
- AgentCore Runtime (sandbox), Gateway (MCP + auth), Memory (short + long-term)
- AgentCore Identity (OAuth workforce + customer), Observability, Code Interpreter, Browser tool
- Strands Agents: AWS-maintained open-source SDK
- Agent Squad multi-agent orchestrator
- Augmented AI (A2I) for human-in-the-loop review of low-confidence outputs
Vector Stores & Retrieval
Choosing a vector store and tuning retrieval quality, from chunking and embeddings to hybrid search and reranking.
- OpenSearch Serverless vector engine (default); Aurora pgvector for transactional metadata
- Neptune Analytics for GraphRAG; DocumentDB for MongoDB-compat
- MemoryDB / ElastiCache for ultra-low-latency; DynamoDB for metadata references
- Chunking strategies, embedding model selection
- Titan Text Embeddings v2 at 256/512/1024 dims; Cohere Embed
- Hybrid search (BM25 + semantic), reranking
- Query transformation: expansion + decomposition + HyDE
- Incremental ingestion + scheduled refresh
Amazon SageMaker AI Suite
The full SageMaker AI ML platform, for when you train, tune, host and monitor your own models.
- SageMaker AI: notebooks, training, hosting, MLOps Pipelines
- Hosting: real-time / serverless / async / batch transform / multi-model / shadow
- SageMaker Unified Studio: Studio + Data + Lake Formation + Athena + Redshift + Bedrock
- JumpStart: curated open-source + partner FM catalog for deploy or fine-tune
- Data Wrangler; Ground Truth (labeling + RLHF); Clarify (bias + FM eval)
- Model Monitor: data / quality / bias / feature drift
- Model Registry (approval gates); Processing (managed batch containers); Neo (edge compilation)
AWS AI Services & Amazon Q
Pre-trained AI services for language, vision, speech and search, and the Amazon Q assistants for business users and developers.
- Comprehend (NLP + PII); Kendra (enterprise semantic search + Intelligent Ranking)
- Lex (conversational bots); Rekognition (image + video)
- Textract (documents + forms + tables); Transcribe (ASR + Call Analytics + medical)
- Titan embeddings + image generator; Augmented AI (A2I)
- Amazon Q Business: enterprise assistant + Identity Center + 40+ connectors + response customisation
- Q Business Apps: no-code mini-apps
- Q Developer: coding assistant + /doc /test /transform agents
- /transform targets: Java upgrades / .NET → Linux / mainframe
- Amazon Connect with Contact Lens generative summaries
Compute, Storage & Data
The infrastructure under GenAI workloads: accelerated compute, storage, data pipelines and the databases used as vector stores or caches.
- Compute: Lambda + Lambda@Edge, App Runner, ECS + Fargate, EKS + Karpenter, ECR
- EC2 with GPU (p5/p4d), Trainium (trn2/trn1), Inferentia (inf2/inf1), general (g5/g6)
- Capacity Blocks for ML; Outposts + Wavelength
- Storage: S3 (Intelligent-Tiering + Lifecycle policies + Cross-Region Replication)
- EBS gp3 / io2 Block Express, EFS, DataSync, Transfer Family
- Data pipelines: Glue + Data Quality, EMR / EMR Serverless, Kinesis, MSK, Athena, QuickSight
- Databases: Aurora pgvector, RDS, DynamoDB + Streams, DocumentDB vector search
- ElastiCache / MemoryDB semantic cache, Neptune Analytics GraphRAG, OpenSearch Service
Application Integration & APIs
How GenAI apps are wired together and exposed: events, workflows, messaging, runtime config and API front doors.
- EventBridge (bus + Pipes + Scheduler); SNS + SQS
- Step Functions: Standard + Express, Map, task token, ReAct / circuit-breaker patterns
- AppFlow (SaaS → AWS connectors); Chatbot (Slack / Teams / Chime)
- AppConfig: runtime feature flags + validators + CloudWatch alarm rollback
- AppConfig for swapping prompts / models / Guardrails
- API Gateway: REST + HTTP + WebSocket + authorizers + mTLS
- AppSync: GraphQL + subscriptions for streaming tokens
- CloudFront + Global Accelerator + Route 53 + ELB + PrivateLink
- Amplify for front-end hosting + auth
Security & Responsible AI
Securing GenAI apps end to end, and a practical responsible-AI checklist mapped to AWS services.
- IAM + IAM Access Analyzer + IAM Identity Center + Cognito
- KMS + AWS Encryption SDK for client-side envelope encryption
- Secrets Manager for API keys; Macie for PII in S3 before ingest
- WAF rate-based rules on chat endpoints
- Bedrock Guardrails: denied topics, PII redaction, prompt-injection, contextual grounding, relevance
- Checklist: fairness, explainability, privacy, safety, accountability, transparency, veracity, governance
- Mapped to AWS services and Bedrock + SageMaker Clarify features
Observability, DevOps & Cost
Watching GenAI apps in production, shipping them with standard DevOps tooling, and keeping token spend under control.
- CloudWatch: Bedrock InputTokens / OutputTokens / InvocationLatency / InvocationClientErrors
- Custom quality metrics; CloudWatch Logs + Bedrock Model Invocation Logging
- CloudWatch Synthetics for prompt regression canaries; X-Ray distributed tracing
- CloudTrail + Managed Grafana; Systems Manager + Service Catalog + Auto Scaling
- Well-Architected Tool Generative AI lens
- DevOps: CDK + CloudFormation + CodePipeline + CodeBuild + CodeDeploy + CodeArtifact + Amplify + CLI + SDKs
- Cost: Cost Anomaly Detection + Cost Explorer; Batch Inference; Provisioned Throughput
- Token minimisation: summarise, cap max_tokens, compress context
- Caching: ElastiCache semantic cache + Bedrock prompt caching
- Cross-Region Inference for capacity resilience
Testing, Evaluation & Troubleshooting
How to evaluate GenAI output, and the first fixes for each failure mode the exam describes.
- Bedrock Model Evaluation (automatic benchmarks + human workforce); SageMaker Clarify FM evaluation
- RAG-specific metrics (RAGAS-style): context precision + recall + answer relevance + faithfulness
- Generation metrics: BLEU / ROUGE / METEOR / BERTScore / perplexity
- Hallucinations: RAG + reranking + contextual-grounding + lower temperature
- Prompt injection: Guardrails + WAF + rate-limit
- Latency: smaller model + latency-optimized + parallelise + cache + async
- Throttling: Cross-Region Inference + Provisioned Throughput
- Agent runaway cost: Step Functions max-iteration + AppConfig kill-switch + Cost Anomaly Detection
- Retrieval drift: scheduled RAGAS eval + re-embed + re-chunk
- Quality regression after model swap: A/B via AppConfig + rollback
Why This Cheat Sheet Helps
AIP-C01 is the deepest generative-AI developer exam AWS publishes. Questions reliably test the subtle differences you only notice after building with the platform: Bedrock Agents vs AgentCore, Knowledge Bases vs Kendra, Prompt Flows vs Step Functions, Provisioned Throughput vs Cross-Region Inference, which Guardrail filter covers which attack surface, when to fine-tune vs RAG, what BERTScore tells you that ROUGE does not, how to engineer a circuit breaker around a ReAct agent. This cheat sheet puts those decisions side by side with the exact AWS vocabulary examiners use.
It assumes you already understand foundation models + prompting + vectors. Use it in the final weeks to map the exam's surface area, reinforce the Bedrock ecosystem (KB + Agents + AgentCore + Guardrails + Prompt Management + Prompt Flows), and rehearse the responsible-AI controls and operational runbooks AWS expects in production GenAI systems.
How to Use It
Skim the whole sheet once to see how the five domains map to sections. Then hammer practice exams — for each wrong answer, find the relevant section and study the tables and decision trees. Pay extra attention to sections 2–4 (Bedrock + KB + Agents), 5 (Vector stores + retrieval), 13 (Security + Responsible AI), 16 (Testing + Troubleshooting), 17 (Reference Architectures), 18 (Scenario → Answer Patterns), 19 (Pitfalls), and 20 (Prompt Engineering deep dive).
In the final week, walk through the five domain weights against your confidence: FM Integration & Data (31%) → sections 1–5, 9, 10; Implementation & Integration (26%) → sections 4, 6, 8, 11, 12, 15; AI Safety, Security & Governance (20%) → sections 13, 21; Operational Efficiency (12%) → sections 14, 22, 23; Testing, Validation & Troubleshooting (11%) → sections 16, 24. Pair with CloudNinja's free AIP-C01 practice exam to surface gaps.
Frequently Asked Questions
Is this AWS Generative AI Developer Professional cheat sheet free?
Yes, completely free with no signup required. Download the PDF directly from CloudNinja and use it as a study reference for the AIP-C01 exam.
How much AWS experience do I need before AIP-C01?
AWS recommends two or more years of hands-on AWS development plus prior AI/ML experience. The exam is Bedrock-heavy and assumes fluent use of Knowledge Bases, Agents, AgentCore, Guardrails, Prompt Management, and Prompt Flows in production, plus SageMaker AI for fine-tuning and hosting. If you do not already hold the AI Practitioner (AIF-C01) and have built at least one RAG or agentic application, start there first.
How is AIP-C01 different from the AI Practitioner (AIF-C01)?
AIF-C01 is foundational — it tests vocabulary, concepts, and which service does what. AIP-C01 is professional-tier and tests how to build it. Expect longer scenarios with detailed implementation choices: which chunking strategy for this content type, when to fine-tune vs RAG, Bedrock Agents vs AgentCore, which vector store fits the transactional constraints, how to design circuit breakers around ReAct agents, which Guardrail + Macie + WAF combination covers a given threat model. Strong programming background expected.
Is this cheat sheet updated for the current AIP-C01 exam?
Yes, it is built directly from the current AIP-C01 exam guide (ai-professional-01) and audited against the full in-scope services list. It reflects current Bedrock features — AgentCore (Runtime / Gateway / Memory / Identity / Observability), Strands Agents, Agent Squad, Model Context Protocol, Cross-Region Inference, Latency-Optimized Inference, Bedrock Data Automation, Guardrails automated reasoning + contextual grounding, Bedrock Prompt Management + Prompt Flows, Amazon Nova model family, SageMaker Unified Studio, and AWS Well-Architected Generative AI Lens.
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