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AWS AIF-C01 Exam Questions
AIF-C01 sits at the foundational tier, which fools a lot of candidates into treating it as a vocabulary test. It is not. AWS asks you to pick the right service for a business problem, judge when a foundation model is the wrong tool, and recognise where responsible-AI obligations bite. The AIF-C01 exam questions below come from the practice bank our candidates work through before test day, and every one of them tracks the current AWS exam guide for the AI Practitioner exam. Start with the questions if your booking is close, and use the domain weightings further down to decide where the rest of your study time goes.
| Exam name | AWS Certified AI Practitioner |
| Exam code | AIF-C01 |
| Certification | Amazon Foundational |
| Practice questions in our bank | 401 |
| Questions on the real exam | 65 |
| Time allowed | 90 minutes |
| Passing score | 700 of 1000 |
| Exam fee | USD 100 |
| Level | Foundational |
| Recommended experience | Up to 6 months of exposure to AI and ML on AWS |
AIF-C01 Exam Practice Questions
The questions below are free samples from the same AIF-C01 bank our candidates prepare with, ordered the way AWS weights the domains.
A digital devices company wants to predict customer demand for memory hardware. The company does not have coding experience or knowledge of ML algorithms and needs to develop a data-driven predictive model. The company needs to perform analysis on internal data and external data.
Which solution will meet these requirements?
Correct Answer: D
Amazon SageMaker Canvas is a visual, no-code machine learning interface that allows users to build machine learning models without having any coding experience or knowledge of machine learning algorithms. It enables users to analyze internal and external data, and make predictions using a guided interface.
Option D (Correct): 'Import the data into Amazon SageMaker Canvas. Build ML models and demand forecast predictions by selecting the values in the data from SageMaker Canvas': This is the correct answer because SageMaker Canvas is designed for users without coding experience, providing a visual interface to build predictive models with ease.
Option A: 'Store the data in Amazon S3 and use SageMaker built-in algorithms' is incorrect because it requires coding knowledge to interact with SageMaker's built-in algorithms.
Option B: 'Import the data into Amazon SageMaker Data Wrangler' is incorrect. Data Wrangler is primarily for data preparation and not directly focused on creating ML models without coding.
Option C: 'Use Amazon Personalize Trending-Now recipe' is incorrect as Amazon Personalize is for building recommendation systems, not for general demand forecasting.
AWS AI Practitioner Reference:
Amazon SageMaker Canvas Overview: AWS documentation emphasizes Canvas as a no-code solution for building machine learning models, suitable for business analysts and users with no coding experience.
Which technique involves training AI models on labeled datasets to adapt the models to specific industry terminology and requirements?
Correct Answer: B
Fine-tuning involves training a pre-trained AI model on a labeled dataset specific to a particular task or domain, adapting it to industry terminology and requirements. This process adjusts the model's parameters to better fit the target use case, such as understanding specialized vocabulary or meeting domain-specific needs.
Exact Extract from AWS AI Documents:
From the AWS Bedrock User Guide:
'Fine-tuning allows you to adapt a pre-trained foundation model to your specific use case by training it on a labeled dataset. This technique is commonly used to customize models forindustry-specific terminology, improving their accuracy for specialized tasks.'
(Source: AWS Bedrock User Guide, Model Customization)
Detailed
Option A: Data augmentationData augmentation involves generating synthetic data to expand a training dataset, typically for tasks like image or text generation. It does not specifically adapt models to industry terminology or requirements.
Option B: Fine-tuningThis is the correct answer. Fine-tuning trains a pre-trained model on a labeled dataset tailored to the target domain, enabling it to learn industry-specific terminology and requirements, as described in the question.
Option C: Model quantizationModel quantization reduces the precision of a model's weights to optimize it for deployment (e.g., on edge devices). It does not involve training on labeled datasets or adapting to industry terminology.
Option D: Continuous pre-trainingContinuous pre-training extends the initial training of a model on a large, general dataset. While it can improve general performance, it is not specifically tailored to industry requirements using labeled datasets, unlike fine-tuning.
AWS Bedrock User Guide: Model Customization (https://docs.aws.amazon.com/bedrock/latest/userguide/custom-models.html)
AWS AI Practitioner Learning Path: Module on Model Training and Customization
Amazon SageMaker Developer Guide: Fine-Tuning Models (https://docs.aws.amazon.com/sagemaker/latest/dg/algos.html)
An education company wants to build a private tutor application. The application will give users the ability to enter text or provide a picture of a question. The application will respond with a written answer and an explanation of the written answer.
Which model type meets these requirements?
Correct Answer: B
Comprehensive and Detailed Explanation From Exact AWS AI documents:
A multimodal large language model (LLM) can:
Accept both text and image inputs
Understand visual and textual context
Generate coherent written explanations
AWS generative AI guidance positions multimodal LLMs as the best choice for applications requiring cross-modal understanding and text generation.
Why the other options are incorrect:
Computer vision (A) does not generate text explanations.
Diffusion models (C) generate images.
Text-to-speech (D) converts text to audio.
AWS AI document references:
Multimodal Foundation Models on AWS
Building AI Tutors with Generative Models
A company wants to use generative AI to increase developer productivity and software development. The company wants to use Amazon Q Developer.
What can Amazon Q Developer do to help the company meet these requirements?
Correct Answer: C
Amazon Q Developer is a tool designed to assist developers in increasing productivity by generating code snippets, managing reference tracking, and handling open-source license tracking. These features help developers by automating parts of the software development process.
Option A (Correct): 'Create software snippets, reference tracking, and open-source license tracking': This is the correct answer because these are key features that help developers streamline and automate tasks, thus improving productivity.
Option B: 'Run an application without provisioning or managing servers' is incorrect as it refers to AWS Lambda or AWS Fargate, not Amazon Q Developer.
Option C: 'Enable voice commands for coding and providing natural language search' is incorrect because this is not a function of Amazon Q Developer.
Option D: 'Convert audio files to text documents by using ML models' is incorrect as this refers to Amazon Transcribe, not Amazon Q Developer.
AWS AI Practitioner Reference:
Amazon Q Developer Features: AWS documentation outlines how Amazon Q Developer supports developers by offering features that reduce manual effort and improve efficiency.
An AI practitioner who has minimal ML knowledge wants to predict employee attrition without writing code. Which Amazon SageMaker feature meets this requirement?
Correct Answer: A
The correct answer is A because Amazon SageMaker Canvas is designed specifically for users with little or no machine learning or programming experience. It provides a visual interface to build ML models by simply uploading data, performing analysis, and generating predictions using a no-code environment.
From the AWS documentation:
'Amazon SageMaker Canvas enables business analysts and other users to generate accurate ML predictions using a visual, point-and-click interface without writing code or having prior ML experience.'
This feature allows the user to:
Import datasets (e.g., HR data)
Automatically explore the data
Select the prediction column (e.g., attrition)
Train the model
Generate and export predictions
Explanation of other options:
B . SageMaker Clarify is used to detect bias and explain ML predictions but not to build models or make predictions without code.
C . SageMaker Model Monitor monitors model quality in production but doesn't build or train models.
D . SageMaker Data Wrangler is used for data preprocessing and transformation but still requires some technical configuration.
Referenced AWS AI/ML Documents and Study Guides:
Amazon SageMaker Canvas Developer Guide
AWS Certified Machine Learning Specialty Study Guide -- AutoML and No-Code Tools Section
AWS Machine Learning Blog: ''Predict Employee Attrition with SageMaker Canvas''
AIF-C01 Exam Domains and Weightings
Domains and weightings follow the official AWS Certified AI Practitioner exam guide.
Fundamentals of AI and ML
20%Terminology, the difference between AI, machine learning and deep learning, supervised versus unsupervised learning, inferencing, and where SageMaker AI fits. Questions here reward precise definitions: expect to separate a training job from an inference endpoint, or classification from regression, under exam pressure.
Fundamentals of Generative AI
24%Tokens, embeddings, prompt engineering, foundation models, and the Amazon Bedrock service model. Expect scenarios that hand you a business need and ask whether a foundation model suits it at all, plus items on the economics and latency trade-offs of generative workloads.
Applications of Foundation Models
28%The largest domain. Prompt design, retrieval augmented generation, fine-tuning versus prompt engineering, evaluation methods, and the AWS services that carry them. Questions typically describe a customer requirement and ask which approach reaches it with the least cost or effort.
Guidelines for Responsible AI
14%Bias, fairness, explainability, transparency, and the tooling AWS provides for each, including model cards and guardrails. These items usually present a deployment about to go wrong and ask which responsible-AI practice would have caught it.
Security, Compliance, and Governance for AI Solutions
14%IAM, encryption, data lineage, and the compliance obligations attached to model data. The AWS shared responsibility model matters here: several questions turn on knowing which half of a control is yours and which half belongs to AWS.
Official Revisions by AWS
No revision log published — : AWS has not published a change log for AIF-C01 since the exam guide was released. AWS states that exam guide revisions appear at least one month before they reach the exam, so any change will be visible here before it affects test day.
30 April 2026 — : Track the exam guide's Revisions section each review cycle and log dated entries here as AWS publishes them.
Source: the AWS Certified AI Practitioner exam guide. Our question bank is updated on its own cycle and re-checked when AWS revises the guide.
What the AIF-C01 Exam Really Tests, and How
Foundational does not mean recall-only. AWS writes AIF-C01 for people who use AI services rather than build them, so the questions test judgement about tools you have seen rather than mathematics you have derived. Four question shapes carry most of that load.
Choosing the right AWS AI service
Tested via: scenario-based multiple-choice questionsA short business case arrives with a constraint attached: no training data, a tight budget, a latency ceiling. You decide between Bedrock, SageMaker AI, and the managed AI services such as Comprehend or Textract. Knowing what each service does is not enough; the exam turns on knowing which one stops being appropriate when the constraint changes.
Judging generative AI trade-offs
Tested via: multiple-response questionsItems that ask you to select two or three correct statements about fine-tuning, retrieval augmented generation, or prompt engineering. Multiple-response scoring gives no partial credit, so a single wrong selection costs the whole question. These reward candidates who can rule options out, not just recognise the right ones.
Sequencing an AI workflow
Tested via: ordering questionsAWS added ordering items to this exam: you place three to five steps in the correct order, such as the stages of a model lifecycle or the sequence for grounding a model on private data. Credit requires the whole sequence, which makes these the fastest way to lose marks if your mental model of the workflow is fuzzy.
Matching concepts to controls
Tested via: matching questionsA list of responses pairs against three to seven prompts, typically mapping responsible-AI principles, security controls, or evaluation metrics onto their definitions or use cases. Every pair must be right for the point, so these items punish approximate understanding of terminology more heavily than standard multiple choice does.
How to Pass AIF-C01 on Your First Attempt
Most people book AIF-C01 with a few weeks of runway, often alongside a full-time job. That is workable, because the exam rewards breadth rather than depth. What it punishes is preparing as if all five domains carry equal weight.
Sit a full question set before you study anything. Fifty questions cold will locate your weak domain in under an hour, and foundational exams tend to surprise people: candidates who write code every day often lose marks on responsible AI, not on the technical items.
Trace every miss back to the AWS documentation. Look up the service page for whichever option you picked and read what it actually does. AIF-C01 recycles a small set of service boundaries across many questions, so understanding one confusion usually clears several future items at once.
Weight your remaining time toward foundation models. Applications of Foundation Models is 28% of scored content and Fundamentals of GenAI another 24%. Those two together are more than half the exam. Responsible AI and security sit at 14% each, so a gap there costs roughly half as much per hour of repair.
Practise the ordering and matching formats specifically. They carry no partial credit. Run at least one timed set that includes them so test day is not the first time you meet a five-step sequence with the clock running.
What to Look for in AIF-C01 Practice Questions
and Why Prefer Pass4Success Practice Material
An AIF-C01 retake costs another USD 100 and a wait before you can rebook. Against that, what a question bank costs matters far less than whether it prepares you for the exam AWS is running now. Here is what separates material worth your time.
Written against the current exam guide
AWS revises its exam guides periodically and publishes changes at least a month ahead. Questions written for an older guide still read plausibly, which is exactly what makes them risky.
✓ Ours: the bank is updated on a regular cycle to reflect how AWS actually tests, and re-checked whenever the exam guide changes. The last update date sits at the top of this page.
Covers the newer question formats
Ordering and matching items behave differently from multiple choice and score all-or-nothing. A bank that offers only four-option questions leaves you unprepared for a real part of the exam.
✓ Ours: the bank includes ordering and matching formats alongside standalone questions, and the practice test also runs timed sessions so pacing is rehearsed, not improvised.
Something you can try before paying
This market has earned its reputation for recycled dumps. Any provider confident in its questions can show you a sample first.
✓ Ours: a free demo of both formats, the PDF and the practice test. The free questions on this page come from the same bank.
Enough coverage to expose a weak domain
Five domains cannot be assessed by twenty sample questions. You need enough items per domain for a pattern in your misses to become visible.
✓ Ours: 401 AIF-C01 questions spanning all five domains.
Priced sensibly against a second attempt
The comparison worth making is not between providers. It is between preparing properly once and paying the exam fee twice.
✓ Ours: the questions bank is $69, a fraction of the $100 exam fee. A failed attempt doesn't just cost the retake fee; it means paying the full $100 again, on top of losing the time you already put in. Preparing properly with practice material now costs less than that risk.
AIF-C01 Exam Discussion: What Recent Candidates Say
Candidates who have already sat AIF-C01 compare notes below: which domains asked more than the exam guide implied, where the ordering and matching items caught them out, and what they would revise differently. If you have taken it, add what you learned.
Ready to Pass AIF-C01 on Your First Attempt?
Get Premium AccessFrequently Asked Questions
What skills are measured on the AIF-C01 exam?
Five domains: Fundamentals of AI and ML (20%), Fundamentals of Generative AI (24%), Applications of Foundation Models (28%), Guidelines for Responsible AI (14%), and Security, Compliance, and Governance for AI Solutions (14%). AWS aims the exam at people who use AI services rather than build them.
What are the prerequisites for taking the AIF-C01 exam?
None. AWS sets no formal prerequisite for AIF-C01 and you can book it as your first AWS certification. The exam guide assumes up to six months of exposure to AI and ML on AWS, plus familiarity with core services such as EC2, S3, Lambda, Bedrock and SageMaker AI.
How hard is the AIF-C01 exam?
Easier than the associate-level AWS exams, but harder than its foundational label suggests. The difficulty sits in service selection and responsible-AI judgement rather than technical depth. Candidates who have used Bedrock or SageMaker AI even briefly tend to find it manageable; those working purely from a glossary usually do not.
What types of questions are on the AIF-C01 exam?
Four types: multiple choice with one correct answer, multiple response requiring every correct option, ordering where you arrange three to five steps, and matching where you pair responses against three to seven prompts. Ordering and matching give no partial credit.
What are the most common mistakes on the AIF-C01 exam?
Confusing Bedrock with SageMaker AI on service-selection items, treating fine-tuning as the default answer where retrieval augmented generation is cheaper and faster, underestimating the responsible-AI domain, and losing whole multiple-response questions to one wrong selection. Several are reading errors rather than knowledge gaps: the deciding constraint is usually stated once.
How do the AIF-C01 topics connect in a real project workflow?
The way an AI project actually runs. You frame a business problem, pick a model or service, ground it on your own data, evaluate whether the output is fair and explainable, then secure the data and access around it. The exam mirrors that: many questions require a service decision and a governance decision at the same time.
What is an effective final-week review plan for AIF-C01?
Drill the two foundation-model domains rather than re-reading everything, since they carry 52% of scored content between them. Take one timed full-length test midweek, review every miss, then stop adding material. The day before, refresh service boundaries and responsible-AI terminology only.
Is AIF-C01 worth it in 2026?
For non-engineering roles that now touch AI work, yes. It is the fastest credible signal that you understand what generative AI can and cannot do on AWS, and it costs USD 100. Engineers heading for hands-on model work usually treat it as a stepping stone rather than a destination.
Where does AIF-C01 fit in the AWS certification path?
It sits at the foundational tier alongside the Cloud Practitioner exam, with no prerequisite and none required after it. Candidates commonly pair it with CLF-C02 for cloud fundamentals, then move to associate-level exams. For hands-on machine learning work, the specialty and associate ML certifications are the next step.
Related AWS Certification Exams
The AIF-C01 bank runs on a scheduled internal review rather than waiting for AWS to touch the exam guide, because how a topic gets tested shifts between official revisions. AWS publishes exam guide changes at least a month before they reach the exam; when one appears, the affected questions and answers are re-checked against current AWS documentation on top of the scheduled pass. The resulting date appears in the exam details table at the top of this page.