AIP-C01 launched in late 2025 and completed its beta run on March 31, 2026, meaning many candidates taking the exam today are among the first group pursuing this credential. Because the certification is relatively new, there is still limited community feedback about which domains candidates find most challenging. AWS designed this exam to assess your ability to build, deploy, and operate production-ready generative AI applications using Amazon Bedrock—not simply your ability to create effective prompts. If you expect the exam to focus primarily on chatbot techniques, its five content domains provide a much broader picture of the skills being assessed.
This version keeps the original meaning while maintaining a professional, exam-focused tone.
The exam guide breaks the content into five domains, and the weighting tells you where to spend your study hours. Foundation Model Integration, Data Management, and Compliance carries 31 percent and is the largest single domain by far. It covers vector store design (OpenSearch, Aurora with pgvector, Bedrock Knowledge Bases), chunking strategy, embedding model selection, and prompt management through Bedrock Prompt Flows. Implementation and Integration sits at 26 percent and is where agentic AI lives: Strands Agents, AWS Agent Squad, Model Context Protocol servers, and orchestration through Step Functions. AI Safety, Security, and Governance takes 20 percent and focuses on Bedrock Guardrails, PII detection with Comprehend and Macie, and responsible AI documentation like model cards. The final two domains, Operational Efficiency (12 percent) and Testing, Validation, and Troubleshooting (11 percent), are smaller but dense: token cost optimization, semantic caching, hallucination detection, and RAG evaluation methodology all live here.
This is not a first AWS certification for anyone. The target candidate profile calls for two or more years building production applications on AWS or open source, plus at least a year of hands on GenAI work. In practice, that means engineers who have already shipped a Bedrock integration or a RAG pipeline at their job, often holding an associate level certification like AWS Certified Machine Learning Engineer or AWS Certified Data Engineer already. A meaningful share of candidates are ML engineers moving from experimentation into production ownership, and platform engineers whose teams adopted Bedrock and now need someone who can speak to security and cost controls, not just architecture diagrams.
The official AIP-C01 exam runs 65 scored questions plus 10 unscored ones seeded in for future calibration, and you cannot tell which is which, so every question deserves full attention. Scoring is scaled from 100 to 1,000 with a passing mark of 750, and AWS uses a compensatory model, meaning a weak section can be offset by strength elsewhere as long as your overall score clears the bar. What catches people off guard is the question variety. Beyond standard multiple choice and multiple response, AIP-C01 uses ordering items (sequence 3 to 5 steps correctly) and matching items (pair up to 7 prompts with responses). These take longer to work through than a straight multiple choice question, and if you have only practiced traditional AWS exam formats before, budget extra time per question rather than pacing yourself against an old benchmark.
Because the exam leans so heavily on scenario based skills like "design a resilient architecture using Cross-Region Inference" or "choose the right chunking strategy for this document type," treat wrong answers here as a signal to go re-read the specific AWS service documentation, not just the exam guide bullet point. When you see a question about vector store architecture, don't just memorize that OpenSearch supports vector search. Understand why you would pick OpenSearch with the Neural plugin over Aurora with pgvector for a given latency and scale requirement, since AIP-C01 tests that judgment directly. Run through the ordering and matching style questions here more than once. They test recall of correct sequence, not just recognition, so repetition builds the muscle memory that a single pass through multiple choice questions will not.
Two patterns show up consistently with this exam. First, candidates who are strong on Bedrock basics but have never touched Strands Agents, Agent Squad, or MCP server implementation get blindsided by Domain 2's depth on agentic tooling. This is a newer part of the AWS ecosystem and it carries exam weight, so skipping it because it feels unfamiliar is a mistake. Second, people underestimate Domain 3's governance content. It is easy to assume "safety and governance" means knowing that Guardrails exists, but the exam goes further into audit logging with CloudTrail, data lineage tracking through Glue Data Catalog, and bias drift monitoring, topics that read as compliance paperwork until you realize they are scored at 20 percent of the exam, nearly as much as the entire implementation domain.
Because AIP-C01 explicitly excludes model training and feature engineering from scope, resist the urge to over-study SageMaker's ML training workflow. Time spent there is largely wasted; the exam guide is explicit that this is out of bounds. Instead, build a small RAG pipeline yourself using Bedrock Knowledge Bases if you have not already, since the exam's scenario questions read very differently once you have debugged a chunking strategy or tuned a reranker in a console. Also keep the AWS in-scope and out-of-scope service lists from the exam guide open while you study. AWS specifically excludes services like GuardDuty, Security Hub, and Redshift from this exam even though they show up constantly on other AWS security and analytics certifications, and assuming they are fair game wastes study time Looking for another practice test? Try AWS ML Engineer exam questions for free.
Work through this practice test in blocks by domain rather than straight through, since the domain weighting means Foundation Model Integration and Implementation together make up well over half your score. Once you can consistently explain why a wrong answer is wrong, not just which option was right, you are in a strong position to schedule the thing.
Prompt Engineering and Prompt Optimization
Last updated on Oct, 9 2026
