AWS Certified AI Practitioner (AIF-C01) study guide

How to use this guide

What to focus on:

• Using the core terms precisely: model, inference, training, embedding, token
• Supervised, unsupervised and reinforcement learning, and what each is for
• When traditional ML beats generative AI, and when neither is the right tool
• The AWS ML stack: SageMaker, and the managed AI services above it

What to focus on:

• Foundation models, and how pre-training differs from fine-tuning
• Tokens, context windows, embeddings and vector representations
• Amazon Bedrock and Amazon Q: what each is for and how they differ
• Where generative AI genuinely fits a business problem, and where it does not

What to focus on:

• Prompt engineering: zero-shot, few-shot, chain-of-thought, and prompt injection risk
• Retrieval-augmented generation: vector stores, chunking, and why retrieval quality drives output quality
• Choosing between prompting, RAG, fine-tuning and continued pre-training on cost and effort
• Evaluating outputs: relevance, groundedness, hallucination, and human review

What to focus on:

• Bias and fairness: where it enters, and how it is measured
• Transparency and explainability, including model cards
• Guardrails for Amazon Bedrock and content filtering
• Human-in-the-loop review, and when it is not optional

What to focus on:

• Protecting training and inference data with encryption, IAM, and least privilege for model access
• Data residency, retention, and what leaves your account when you call a model
• Auditability and monitoring of AI workloads
• Regulatory and IP considerations around model output

Common mistakes

• ✕Treating generative AI as the answer to every scenario. Plenty of questions are checking whether you spot the case where classic ML, or no AI at all, is correct.
• ✕Confusing fine-tuning with RAG. Fine-tuning changes model behaviour; RAG changes what the model can see. Questions turn on which problem you actually have.
• ✕Underestimating responsible AI. Bias, transparency and guardrails are 14% on their own, and they are tested as judgment rather than definitions.
• ✕Mixing up Amazon Bedrock and Amazon Q. One is model access for building, the other is an assistant product.
• ✕Forgetting that data governance still applies. Questions often hinge on what happens to prompts and outputs, not on the model itself.
• ✕Ignoring the non-multiple-choice formats and meeting ordering or matching items cold.

Exam-day tips

• ✓Answer everything. Scoring is compensatory, so a weak domain cannot fail you on its own.
• ✓There is no penalty for guessing. Never leave an item blank.
• ✓Read all the steps before placing any of them. Ordering questions are graded on the whole sequence.
• ✓Be wary of any option that promises to eliminate bias or guarantee accuracy. Responsible-AI answers are hedged for a reason.
• ✓Spend scarce study time on prompting and RAG. Applications of Foundation Models is 28% of the exam.
• ✓Keep moving. At 65 questions in 90 minutes you have under 90 seconds each, so flag and come back.

How to know you’re ready

One good practice score can be luck. What counts is scoring at or above the real pass mark (700 on a 100 to 1000 scaled score) across several full-length sets in a row, with no single domain trailing far behind the others. Kwizza tracks your per-domain readiness automatically as you practice.