Frequently Asked Questions (FAQ)
Do I need AWS ML experience for AIP-C01?
Yes. This is a professional-level exam. 2+ years of hands-on AWS machine learning experience and 1+ year of generative AI implementation are strongly recommended. If you have not worked with SageMaker, Bedrock, or similar services in production, the implementation and troubleshooting domains will be difficult. AWS Certified AI Practitioner or equivalent knowledge provides a solid foundation.
How many practice questions should I aim for?
Aim for at least 800 unique questions across all domains, prioritizing quality over quantity. ExamOS provides scenario-based practice questions across every domain, designed to explain the rationale behind both correct and incorrect answers. Your readiness score tracks volume and coverage so you know when you have done enough.
When should I book the exam?
Book when your readiness score is stable and above 80, and you have covered every domain at least once. Consistent strong performance under timed conditions is a better signal than one high score.
How long is the exam?
180 minutes for 75 questions (65 scored, 10 unscored). That averages to roughly 2 minutes and 16 seconds per question. Scenario-heavy questions with long context may take longer, so keep an eye on the clock. Answer every question; the unscored ones are indistinguishable from scored ones.
What is the passing score?
750 out of 1000. AWS uses scaled scoring. The raw number of correct answers needed varies slightly between exam versions. Do not try to calculate how many questions you can afford to miss.
Can I use reference materials during the exam?
No. The AIP-C01 exam is closed-book. You cannot access documentation, websites, or any external resources during the test. All questions are designed to be answerable from your knowledge of AWS GenAI services, architecture patterns, and best practices.
What is the most important domain to study?
Foundation Model Integration, Data Management, and Compliance (31%) is the heaviest domain. Combined with Implementation and Integration (26%), the first two domains represent 57% of the exam. RAG and agents are the core topics within these domains and appear in the majority of scenario questions.
What is the difference between Bedrock and SageMaker for GenAI?
Bedrock provides managed access to foundation models through an API. You use it for inference, RAG, agents, and guardrails without managing infrastructure. SageMaker is for training, fine-tuning, and deploying custom models with full control over the training process. The exam tests both and expects you to know when each is the right choice. Bedrock is for consuming foundation models. SageMaker is for building and customizing them.
What is the trade-off framework for fine-tuning vs RAG vs prompt engineering?
The exam tests this distinction frequently:
- Prompt engineering is the fastest and cheapest approach. Use it when the model's existing knowledge is sufficient and you need to guide output format or behavior.
- RAG is best when you need the model to access specific, up-to-date, or proprietary information that was not in its training data.
- Fine-tuning is best when you need the model to consistently follow a specific style, format, or domain-specific behavior that prompting alone cannot achieve reliably.
The binding constraint in the scenario (cost, accuracy, data freshness, latency) usually points to one of these three.
What are the most common ways people fail?
- Treating every AI question as a Bedrock question. Some scenarios require SageMaker for fine-tuning or custom training. Knowing when Bedrock is not enough is essential.
- Not understanding RAG depth. The exam goes beyond basic retrieve-and-generate. You need to understand chunking strategies, embedding model selection, hybrid search, query rewriting, and reranking.
- Ignoring AI safety. At 20% of the exam, safety and governance are not a footnote. Bedrock Guardrails, prompt injection defenses, and compliance frameworks show up regularly.
- Confusing on-demand and provisioned throughput. Know the cost and latency trade-offs and when each model is justified.
- Not practicing agent design. Bedrock Agents with action groups, function calling, and multi-agent orchestration are core topics. If you have not built one, you will struggle with these questions.
- Not practicing under time pressure. 75 questions in 180 minutes is generous, but scenario-heavy questions consume more time than you expect. Practice timed quizzes to build pacing.
Is there a renewal option?
Yes. AIP-C01 is valid for 3 years. To renew, you retake the current version of the exam. AWS also offers recertification pathways through their certification portal. Check the AWS Certification portal for the most current renewal options.