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The AWS Certified AI Practitioner (AIF-C01) is a foundational, concept-focused exam with five scored domains. You need to recognize AI and machine-learning approaches, judge where generative AI and foundation models fit, and understand responsible use, security, compliance, and governance on AWS. You are not expected to build models or perform advanced mathematics. The domains carry different weights: Applications of Foundation Models is largest at 28%, followed by Fundamentals of GenAI at 24% and Fundamentals of AI and ML at 20%. The remaining two domains are 14% each.
This guide explains what each domain means in practical terms, how the exam is scored, what is inside and outside the intended scope, and how to organize preparation without treating AIF-C01 like an ML-engineering certification.
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What is the AWS AI Practitioner AIF-C01 exam?
AIF-C01 tests whether you can discuss core AI concepts and select appropriate AWS AI capabilities for common business situations. AWS describes it as being “designed for individuals who want to demonstrate a foundational understanding of AI concepts and AWS AI tools.” The intended candidate may have up to six months of exposure to AI/ML technologies on AWS and may use solutions without building them.
That distinction determines how you study. You should be able to explain what a model does, distinguish training from inference, recognize when a generative approach is suitable, and identify security or governance concerns. You should not expect an exam centered on writing model code, deriving equations, tuning hyperparameters, or constructing deployment infrastructure.
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The official outline assigns percentages to scored content, not a guaranteed number of questions from each domain. All five areas matter because the final result is based on your overall performance.
What are the five AWS AI Practitioner exam domains?
The current AIF-C01 guide divides scored content into five domains. Use the weights to set priorities, but review every domain because AWS does not publish a separate pass mark for each one.
| Domain | Weight of scored content | What it means in plain language |
|---|---|---|
| 1. Fundamentals of AI and ML | 20% | Vocabulary, learning approaches, data, inference, use cases, and the AI/ML lifecycle. |
| 2. Fundamentals of GenAI | 24% | How generative systems work, where they help, their limitations, and the AWS building blocks around them. |
| 3. Applications of Foundation Models | 28% | The largest domain: understanding the application of foundation models at a conceptual level. |
| 4. Guidelines for Responsible AI | 14% | Recognizing responsible-AI considerations when evaluating or using AI systems. |
| 5. Security, Compliance, and Governance for AI Solutions | 14% | Protecting AI workloads and data while meeting governance and compliance obligations. |
The guide notes that its content outline is not exhaustive and that AWS reviews it periodically. Revisions can be published before they appear on the exam, so check the current AIF-C01 guide and certification page when you schedule.
What does Domain 1, Fundamentals of AI and ML, cover?
Domain 1 is the vocabulary and reasoning foundation for the rest of the exam. At 20%, it rewards clear distinctions between related terms and the ability to connect an approach to a practical use case.
Know the essential distinctions
- Artificial intelligence (AI) is the broad field of systems performing tasks associated with human intelligence.
- Machine learning (ML) uses data to learn patterns for predictions or decisions rather than relying only on hand-written rules.
- Deep learning is a machine-learning approach based on multilayer neural networks.
- Generative AI produces new content such as text, images, audio, video, or code.
- Agentic AI describes systems that can plan or take actions toward a goal, often by using tools or services.
- A model is the learned representation used to produce an output; an algorithm is the procedure used to learn or apply that representation.
- Training is the learning phase. Inference is using a trained model to generate a prediction or response.
- Bias is a systematic skew that can produce unfair or inaccurate outcomes. Fairness concerns whether outcomes are appropriate across relevant groups.
Recognize data and learning approaches
Expect to identify common data types and the broad learning pattern that fits a scenario. Supervised learning uses labeled examples, unsupervised learning finds structure in unlabeled data, and reinforcement learning learns through feedback from actions. Questions can also test whether you understand the stages of an AI/ML lifecycle, from defining a problem and preparing data through training, evaluation, inference, monitoring, and improvement.
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Keep the depth foundational
AWS explicitly places model coding, feature engineering, hyperparameter tuning, deployment pipelines, and mathematical or statistical analysis outside the expected target tasks. You still need to recognize why those activities exist, but AIF-C01 is not asking you to implement them.
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What does Domain 2, Fundamentals of GenAI, cover?
Domain 2 accounts for 24% and checks whether you understand the parts of a generative system well enough to assess a proposed use. The emphasis is conceptual, not on training a large model from scratch.
Understand the language of generative systems
- Tokens are the units a language model processes; they may be words, word fragments, or symbols.
- Chunking divides longer source material into manageable sections before processing or retrieval.
- Embeddings represent content as numerical vectors so that semantic similarity can be calculated.
- Prompt engineering structures instructions and context to improve a model’s response.
- Transformers are the architecture behind many modern large language models (LLMs).
- Foundation models are broadly trained models that can be adapted to many tasks. Multimodal models work across more than one type of input or output, such as text and images.
- Diffusion models generate content by learning to reverse a process that adds noise, a pattern commonly used for image generation.
Match capabilities to business tasks
Be ready to recognize suitable uses such as summarization, conversational assistants, translation, code generation, customer-service responses, search, and recommendation. The question is not simply whether a model can produce an output; it is whether the quality, latency, cost, data requirements, and risk are acceptable for the business need.
Know the limitations
Generative systems can produce plausible but incorrect content, reflect bias in their data, expose sensitive information if handled poorly, or fail when a prompt lacks necessary context. A sound answer accounts for validation, grounding, access controls, and human review where the consequences justify them. The domain also includes AWS infrastructure and technologies used to build generative-AI applications, so learn the role of those building blocks rather than memorizing isolated product names.
What is tested in Domain 3, Applications of Foundation Models?
Applications of Foundation Models is the largest domain at 28% of scored content. The official guide establishes the domain title and weight, but the detailed objective list should be read in the current guide rather than inferred from third-party summaries.
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Prepare to reason about how a broadly trained foundation model can support an application and what trade-offs a business owner must consider. Keep your preparation at the level the guide supports: identify an appropriate application pattern, understand the role of the model in that pattern, and recognize when additional controls or adaptation are needed. Do not treat an unlisted technique as an official AIF-C01 objective simply because it appears in a general machine-learning course.
What is tested in Domain 4, Guidelines for Responsible AI?
Responsible AI represents 14% of scored content. The domain title and weight are official; consult the current guide for the precise objective wording before making a detailed checklist.
For preparation, develop the habit of asking who could be affected by an AI output, whether the data and evaluation process are appropriate, how a decision can be explained, and where human oversight belongs. Responsible use is a judgment area: a technically possible system may still be unsuitable if its impact, transparency, or accountability is unacceptable.
What does Domain 5, Security, Compliance, and Governance cover?
Domain 5 is 14% and turns broad AI knowledge into operational awareness. You should recognize controls and governance questions across the data, model, prompt, and output paths.
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Security and access
- Understand how IAM roles and permissions limit who or what can access AI resources and data.
- Recognize encryption and private connectivity as ways to protect information in transit and at rest.
- Apply the AWS shared-responsibility idea: AWS secures the underlying cloud infrastructure, while the customer remains responsible for configured services, identities, data, and workload choices.
Data and model protections
- Consider data origins, lineage, quality, and access controls before using data for an AI workload.
- Recognize prompt injection and data leakage as threats that require defensive design.
- Use output filtering, grounding, validation, and logging to reduce unsafe or unsupported responses and to support investigation.
Governance and compliance
- Account for data residency and retention requirements.
- Use monitoring, review policies, and transparency practices to keep AI use accountable over time.
- Identify when a governance tool, approval process, or documented control is needed instead of treating a model response as self-justifying.
These are representative areas in the domain, not an exhaustive implementation manual. The exam tests recognition of the concern and the appropriate control at a foundational level.
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How should I prioritize study time across AIF-C01?
Start with the published weights, then distribute enough review time to every domain. Domain 3 deserves the largest block, Domain 2 the next largest, and Domain 1 remains essential because its terminology appears throughout scenario questions. Domains 4 and 5 are smaller but can decide borderline results.
- Build a terminology map. Define AI, ML, deep learning, generative AI, agentic AI, model, algorithm, training, inference, bias, fairness, tokens, embeddings, vectors, prompts, foundation models, and multimodality in your own words.
- Practice scenario recognition. For each business problem, identify the data type, desired output, learning or generative approach, and the main risk. Explain why an alternative would be a worse fit.
- Study GenAI mechanics at a diagram level. Trace a request through a prompt, tokenization, model processing, retrieved or supplied context, and output validation. You do not need to implement the pipeline.
- Read the current official guide for Domains 3 and 4. Their weights are clear, but detailed objectives can change as AWS revises the outline. Use the guide’s wording as the authority instead of an inferred checklist.
- Add security and governance to every practice scenario. Ask who can access the data, how it is protected, how outputs are logged and filtered, what retention or residency rule applies, and where human review is required.
- Use official preparation material. AWS points candidates to an exam-preparation plan in Skill Builder, an official practice question set and pretest, courses, labs, and a practice exam. Treat practice questions as a way to learn the reasoning pattern, not as a promise that identical questions will appear.
Does the AI Practitioner exam require coding?
No. The intended candidate uses, but does not necessarily build, AI/ML solutions. Coding models, feature engineering, optimization, deployment infrastructure, statistical analysis, implementing security protocols, and building governance frameworks are outside the stated target tasks.
Basic technical literacy still matters. You should be able to read a short scenario, identify the service or approach that fits at a high level, and explain why a control is needed. If a study resource spends most of its time on Python notebooks or mathematical derivations, it is going deeper than this certification’s stated boundary.
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1Fix the driver behind crashes, sound loss and screen glitches2Repair Windows errors before they cause bigger problems3Scan for outdated or missing drivers - takes under a minuteWhat is the AIF-C01 exam format and passing score?
The official guide and certification page describe the current format as follows. Logistics and fees can change, so verify them on AWS before booking.
| Item | Current information |
|---|---|
| Total questions | 65 |
| Scored questions | 50 |
| Unscored questions | 15 |
| Question formats | Multiple choice, multiple response, ordering, and matching |
| Time limit | 90 minutes |
| Passing score | 700 on a 100–1,000 scaled score range |
| Exam price | USD 100, as listed on the certification page accessed October 25, 2026 |
| Delivery options | Online proctoring or a Pearson VUE test center |
Unanswered questions are marked incorrect, and there is no penalty for guessing. The score is compensatory: you need to pass overall, not achieve a separate threshold in each domain. Domain-level performance information should therefore be treated as directional rather than as individual pass/fail results.
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How hard is the AWS AI Practitioner exam?
It is approachable for someone with basic AWS and AI literacy, but “foundational” does not mean trivial. The difficulty comes from choosing the best answer in a realistic situation, separating similar concepts, and noticing a security, responsible-use, or governance issue that a purely technical answer misses.
Your preparation is on track when you can explain a concept without a memorized definition, eliminate options that require skills outside the candidate scope, and justify a choice in terms of business fit and risk. You are probably over-studying when you are spending most of your time deriving algorithms or configuring production infrastructure rather than practicing conceptual decisions.
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- Studying only the largest domain. Domain 3 has the highest weight, but every domain contributes to the overall score.
- Confusing capability with suitability. A model may generate an answer yet still be a poor choice because of accuracy, privacy, latency, cost, or compliance requirements.
- Memorizing product names without roles. Learn what an AWS capability does and which problem it addresses at a high level.
- Ignoring unscored questions. You cannot identify them during the exam, so answer all questions and manage time consistently.
- Assuming domain pass marks exist. AWS reports an overall scaled result; do not build a plan around passing five separate mini-exams.
- Using an old outline. AWS reviews the guide periodically. Confirm domain weights, objectives, price, and delivery details immediately before scheduling.
Frequently Asked Questions
Can I take AIF-C01 without prior machine-learning work experience?
Yes, the target profile is foundational exposure rather than professional model-building experience. Familiarity with core AWS services, IAM, shared responsibility, and AWS pricing models is recommended, so learn those basics if they are new to you.
How many questions do I need to answer correctly to pass?
AWS does not publish a fixed raw-question cutoff because results use a scaled score from 100 to 1,000. The minimum passing scaled score is 700, and the exam’s compensatory scoring means performance is combined across domains.
Are the 15 unscored questions identified during the test?
No. The exam includes 50 scored and 15 unscored questions, but candidates cannot tell which questions belong to either group. Treat every item as scored and answer all of them.
When should I recheck the AIF-C01 exam price and time limit?
Check the AWS certification page when you are ready to schedule and again before payment. The listed figures are 90 minutes, 65 questions, and USD 100 as accessed October 25, 2026, but AWS can change logistics or fees.
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