Preparation guide: coming soon. We are mapping this exam's topics to our lessons, so you can see what you already know and what to study. In the meantime, the Foundations courses cover the core ideas.
- Issuer
- Amazon Web Services
- Level
- Foundational
- Exam code
- AIF-C01
- Exam format
- 65 questions (50 scored, 15 unscored) · 90 minutes · 700 of 1,000 to pass
- Valid for
- 3 years
- Focus
- AI literacy (no coding)
- Who it is for
- People in cloud, development, data, IT or business roles who are exploring AI on AWS.
- Before you start
- None required. AWS suggests up to 6 months of exposure to AI/ML on AWS, and Cloud Practitioner Essentials first if you are new to AWS.
What the exam covers
Listed as the issuer lists them, with weights where the issuer publishes them.
- Fundamentals of AI and ML (20%)
- Fundamentals of generative AI (24%)
- Applications of foundation models (28%)
- Guidelines for responsible AI (14%)
- Security, compliance and governance for AI solutions (14%)
Key terms to know
Words from this certification's topic areas, in plain English. Know these and the study material will be much easier to follow.
- Machine learning
- Software that learns patterns from examples instead of following hand-written rules.
- Supervised learning
- Learning from examples that already come with the right answer.
- Overfitting
- When a model memorises its training examples and does badly on new ones.
- Foundation model
- A large model trained on broad data that can be adapted to many tasks.
- Large language model (LLM)
- A foundation model that reads and writes text.
- Prompt engineering
- Writing instructions so a model gives better answers.
- Hallucination
- When a model states something false in a confident way.
- Retrieval-augmented generation (RAG)
- Looking up relevant documents first, then giving them to the model to answer from.
- Fine-tuning
- Training an existing model further on your own examples.
- Embedding
- A list of numbers that captures the meaning of text, so similar meanings end up close together.
- Guardrails
- Rules and filters that block unsafe inputs or outputs.
- Bias
- Unfair skew in a model's results, often caused by skewed training data.
- Responsible AI
- Building and using AI fairly, safely, transparently and with accountability.
Build the knowledge here
Our lessons are not exam prep for this certification, but they cover the same ground.
Details last checked Oct 7, 2026 against the issuer's own pages. Exams, versions and rules change. Confirm on the official page before you book. We are not affiliated with Amazon Web Services.