The complete preparation pathway for AWS Certified Generative AI Developer – Professional (AIP-C01). Bedrock RAG, agents, Guardrails, cost optimization, and timed mock tests included.
It's AWS's Professional-tier certification for developers who integrate foundation models into production applications using AWS services. The 180-minute exam has 65 scored multiple-choice and multiple-response questions across five domains, with a minimum passing score of 750 out of 1,000.
Developers and architects with 2+ years building production applications on AWS and 1 year hands-on experience implementing GenAI solutions. You should understand Bedrock, RAG, agents, IAM/VPC security, and cost optimization — not model training from scratch.
AIP-C01 is Professional-tier and deeply technical — scenario-based questions about Bedrock Knowledge Bases, Step Functions agent orchestration, Guardrails, vector search optimization, and GenAI troubleshooting. The AI Practitioner exam is foundational literacy; AIP-C01 tests production implementation judgment.
Understand what AI actually is — tokens, transformers, agents, and the landscape. Start here if you're new.
11 articles · ~4h →Go from vague requests to precise, reproducible AI outputs. The skill that underpins everything.
12 articles · ~5h →Go from zero to productive with Claude Code — the terminal AI coding agent that ships real projects.
13 articles · ~7h →AWS GenAI Developer Professional: Exam Overview
What AIP-C01 tests, five domain weightings, six scenario frames, scoring (750/1000 to pass), and how to prepare.
Embeddings & Vector Search: Complete Guide
How embeddings power semantic retrieval — Titan models, dimensionality tradeoffs, and vector index design for RAG.
RAG Context Injection Pipeline Design
Chunking strategies, retrieval orchestration, and context assembly for foundation model augmentation.
RAG vs Agentic RAG
When retrieval-augmentation beats agent loops, hierarchical chunking, and multi-step retrieval for enterprise knowledge.
Context Engineering for RAG Systems
Assembling retrieved documents, metadata, and system instructions — the full context package for FM inference.
Prompt Engineering: Zero-Shot, Few-Shot, Chain-of-Thought
Bedrock Prompt Management patterns — role definitions, template governance, and chain-of-thought for complex tasks.
Multi-Agent Orchestration Patterns
Step Functions ReAct loops, Strands Agents, coordinator patterns, and safeguarded AI workflows on AWS.
What Is MCP? Model Context Protocol
MCP tool servers on Lambda/ECS — standardized function definitions for agent-tool interactions in Bedrock workflows.
Build Your First MCP Server
Lambda MCP servers for lightweight tool access — error handling, parameter validation, and consistent access patterns.
ReAct Prompting: Reasoning + Acting for Agents
Thought/Action/Observation loops implemented with Step Functions — structured reasoning for Bedrock agent workflows.
How to Build Your First Agent Loop
Tool invocation cycles, state management, stopping conditions, and timeout mechanisms for production agents.
Bias in AI: Types, Examples, and Mitigation
Fairness evaluations, A/B testing with Bedrock, and responsible AI principles for production FM deployments.
AI Regulation: EU AI Act & US Policy
Compliance frameworks, model cards, data lineage with Glue, and audit logging for regulated GenAI workloads.
Structured Output & JSON Schema Enforcement
JSON Schema for deterministic FM outputs, hallucination reduction, and structured extraction pipelines.
Prompt Caching & LLM Cost Optimization
Semantic caching, token tracking, context pruning, and tiered model routing for cost-effective GenAI at scale.
Temperature, Top-P, and Top-K Sampling
Model parameter tuning for latency-quality tradeoffs — A/B testing configurations in production Bedrock apps.
How to Evaluate Prompt & Model Quality
Bedrock Model Evaluations, regression testing, LLM-as-a-Judge, and continuous evaluation workflows.
AI Benchmarks Explained
Measuring relevance, factual accuracy, latency-to-quality ratios, and business outcomes for FM deployments.
Practice exam
3 timed mock exams with shuffled questions, instant scoring, and per-question explanations. Pass score: 750/1000. The fastest way to find your weak domains before exam day.
18 articles across all five exam domains, approximately 12 hours of study. The pathway mirrors exam weighting: heaviest on Foundation Model Integration & Data (Domain 1 at 31%) and lightest on Testing & Validation (Domain 5 at 11%).
The pathway includes scenario-based quiz questions throughout. After completing the pathway, use the AWS GenAI Developer mock tests at /tests/aws-genai-developer-professional — timed, full-length practice exams with shuffled questions and per-answer explanations.
Comfort with AWS core services (Lambda, S3, IAM, API Gateway), basic ML/AI concepts, and some hands-on Bedrock experience. The Building AI Agents, Context Engineering, and AI Safety pathways on this platform cover prerequisite knowledge if you need to build up first.
Understand and build the loops, harnesses, and protocols that make AI agents reliable and autonomous.
14 articles · ~6h →
