AWS Certified Generative AI Developer – Professional (AIP-C01) Exam Learning Path

Table of Contents hide

The AWS Certified Generative AI Developer – Professional (AIP-C01) is AWS’s newest professional-level certification, validating the ability to integrate foundation models (FMs) into applications and business workflows using AWS technologies.

This is a hands-on certification focused on building and deploying GenAI solutions — not just theory, but production-ready implementations.
Target audience: Developers with 2+ years of AWS experience and 1+ year of hands-on GenAI solution implementation.

🎓 Recommended Course
Stephane Maarek – AWS Certified Generative AI Developer Professional — Comprehensive course covering all 5 exam domains with hands-on labs.

AIP-C01 Exam Content

Validates ability to design and implement solutions using vector stores, RAG, knowledge bases, and GenAI architectures
Tests integration of foundation models into applications and business workflows
Covers prompt engineering and management techniques
Tests implementation of agentic AI solutions
Validates optimization for cost, performance, and business value
Covers security, governance, and Responsible AI practices
Tests troubleshooting, monitoring, and optimization of GenAI applications

Domain 1: Foundation Model Integration, Data Management & Compliance — 31%
- Select and configure FMs for specific use cases
- Design data pipelines for GenAI (vector stores, embeddings, chunking strategies)
- Implement RAG architectures with Amazon Bedrock Knowledge Bases
- Ensure data compliance and governance
Domain 2: Implementation and Integration — 26%
- Implement GenAI solutions using Amazon Bedrock, SageMaker, and open-source models
- Design and implement agentic workflows (Bedrock Agents, AgentCore)
- Apply prompt engineering techniques (few-shot, chain-of-thought, system prompts)
- Integrate FMs into existing applications and workflows

Domain 3: AI Safety, Security & Governance — 20%
- Implement Bedrock Guardrails for content filtering
- Apply Responsible AI practices (bias detection, toxicity filtering)
- Secure GenAI workloads (IAM, VPC, encryption, data isolation)
- Implement model governance and versioning
Domain 4: Operational Efficiency & Optimization — 12%
- Optimize inference costs (model selection, caching, batch inference)
- Implement performance optimization (provisioned throughput, model distillation)
- Design for scalability and reliability
Domain 5: Testing, Validation & Troubleshooting — 11%
- Evaluate FM outputs (BLEU, ROUGE, human evaluation)
- Implement testing strategies for GenAI applications
- Monitor and troubleshoot GenAI workloads
- Implement feedback loops and continuous improvement

Amazon Bedrock — Primary service for FM access, Knowledge Bases, Agents, Guardrails, Model Evaluation
Amazon Bedrock Agents — Agentic AI workflows with tool use and multi-step reasoning
Amazon Bedrock Knowledge Bases — Managed RAG with vector stores (OpenSearch Serverless, Aurora, Pinecone)

RAG (Retrieval Augmented Generation) — Grounding FM responses with external data

Vector Databases & Embeddings — Semantic search, chunking strategies, embedding models
Prompt Engineering — System prompts, few-shot learning, chain-of-thought, temperature/top-p
Agentic AI — Tool use, function calling, multi-step reasoning, ReAct pattern
Fine-tuning vs RAG — When to customize the model vs augment with external data

Model Evaluation — Automated metrics (BLEU, ROUGE, BERTScore), human evaluation, LLM-as-judge
Responsible AI — Bias detection, hallucination mitigation, content safety, model cards
Inference Optimization — Provisioned throughput, caching, batch inference, model distillation

Guardrails — Content filters, denied topics, word filters, PII redaction, contextual grounding

Hands-on with Bedrock is highly recommended — While the exam is multiple-choice, questions are scenario-based and require practical understanding of Bedrock configurations
Build at least 2-3 RAG applications using Bedrock Knowledge Bases