AWS AIF-C01 FAQ for exam format, topics, prep strategy, practice, and common candidate traps.
AIF-C01 is AWS’s foundational certification focused on AI and generative AI concepts plus how those concepts show up in AWS services and solution design.
If you want the fastest orientation, start with the section overview and keep the official exam guide from Resources open while you study.
They are both foundational, but AIF-C01 leans harder into:
If you’re already comfortable with CLF-level AWS concepts, AIF-C01 is mostly about learning AI/GenAI language and patterns.
This exam is strongest for people who can already:
If you keep answering like a researcher or ML engineer when the question is really about business fit and safe use, you will miss easy points.
AWS uses a scaled score (100–1000). The minimum passing score is 700.
No. AWS states that the 15 unscored questions are not identified during the exam, so treat every question as if it counts.
| Failure pattern | Better instinct |
|---|---|
| describing AI terms too vaguely | define the concept and tie it to a practical business outcome |
| mixing RAG, prompting, and fine-tuning as if they are interchangeable | separate retrieval, instruction design, and model adaptation |
| choosing a custom-build path when a managed service is enough | prefer the simplest AWS fit that satisfies the requirement |
| talking about safety only as “add guardrails” | separate guardrails, IAM, encryption, privacy, and human review |
| answering like a model-builder when the exam is about service choice and responsible use | stay in the practitioner lane, not the deep engineering lane |
Not deeply. You should be able to read simple technical descriptions and make good service/design choices (for example: use RAG to ground answers in proprietary documents), but the exam is not a programming test.
AWS also lists these as generally out of scope for this credential:
That out-of-scope list matters. If two answers both sound plausible, the better answer is usually the one that stays at the foundational service-selection and safe-use level, not the one that turns into full MLOps implementation.
No. You should understand concepts like overfitting, evaluation metrics, and training vs inference, but not detailed math derivations.
At a high level, be comfortable with:
Use the Cheat Sheet for a service-by-use-case map.
For AIF-C01, you mainly need to know when each is the better fit. Bedrock is central for foundation-model access and generative-AI building blocks. SageMaker AI matters more as the managed ML platform for broader model development and operations. The exam usually rewards fit and role clarity, not deep implementation detail.
Use a timeline you can actually sustain:
Build your 30/60/90-day schedule from the domain weights, then rotate between the Cheat Sheet and Resources so you keep concepts and official scope aligned.
You do not need a large production environment. A strong minimum baseline is being able to explain:
That baseline is enough to keep the exam from feeling abstract.
Both matter, but the domain weights skew toward gen AI + foundation model applications:
That said, Domain 1 fundamentals are the vocabulary everything else depends on.
| If the miss was really about… | Fix it by doing this next |
|---|---|
| term confusion | rewrite the definition in one sentence and contrast it with the distractor |
| service fit | state the business constraint, then justify the chosen AWS service in plain language |
| RAG vs prompting vs fine-tuning | classify whether the problem is missing knowledge, weak instruction, or model adaptation |
| responsible AI | name the concrete risk first: bias, hallucination, privacy, misuse, or lack of oversight |
| security/governance | separate access control, encryption, auditability, and policy controls instead of saying “secure it” |
Follow a loop:
Do not disappear into:
Before exam day, you should be able to:
Use the current AWS exam guide linked from Resources as the source of truth for scope, weighting, and task framing. If another page sounds deeper, broader, or more engineering-heavy than the AWS guide, trust the AWS guide.