AWS · AIF-C01
Validates foundational understanding of AI, ML, and generative AI concepts on AWS, with focus on practical business applications.
Questions
426
Duration
90 minutes
Passing Score
700/1000
Difficulty
FoundationalLast Updated
Jan 2026
Use this AWS AI Practitioner practice exam to build confidence with the concepts tested on AIF-C01: artificial intelligence, machine learning, generative AI, responsible AI, model selection, and the AWS services that support AI workloads. The questions are written to help you recognize how AI is applied in real business scenarios, not just memorize service names.
Start with the free preview, then work through the full question bank in short review sessions. Pay close attention to explanations after each attempt; they connect the answer choice back to AWS terminology, common use cases, and the practical tradeoffs that appear in foundational AI certification questions.
The AWS Certified AI Practitioner (AIF-C01) is a foundational-level certification that validates a candidate's understanding of artificial intelligence, machine learning, and generative AI concepts as they apply to AWS services and practical business scenarios. It is designed for professionals who use—rather than build—AI/ML solutions, covering the full landscape from core ML terminology and learning paradigms to the application of large language models (LLMs), foundation models, and responsible AI principles.
The certification demonstrates proficiency across five content domains: the fundamentals of AI and ML, the fundamentals of generative AI, the practical application of foundation models, responsible AI guidelines, and security and governance for AI solutions on AWS. Candidates are expected to be familiar with key AWS services including Amazon SageMaker AI, Amazon Bedrock, Amazon Comprehend, Amazon Lex, Amazon Polly, Amazon Transcribe, Amazon Translate, and AWS Identity and Access Management, among others. The exam was introduced as part of AWS's expanding foundational certification track and is valid for three years from the date of passing.
This certification is ideal for non-technical and semi-technical professionals who work alongside AI/ML teams and need a verified understanding of AI concepts, AWS AI tooling, and responsible AI practices. Target roles include business analysts, product and project managers, marketing professionals, IT support staff, line-of-business managers, and sales professionals. It is also well-suited for IT generalists and AWS practitioners looking to formally document their AI fluency without needing a software development or data science background.
The ideal candidate has up to six months of exposure to AI/ML technologies on AWS and is comfortable using—but not necessarily implementing—AI solutions. Those with an existing AWS Cloud Practitioner or Associate-level certification can move directly into AI-focused preparatory content for this exam.
There are no formal prerequisites for the AIF-C01 exam, as it is a foundational-level certification. However, AWS recommends that candidates new to the AWS ecosystem first complete foundational training such as AWS Cloud Practitioner Essentials or AWS Technical Essentials before attempting this exam.
Candidates are expected to have general familiarity with core AWS concepts including the shared responsibility model, AWS IAM, and AWS service pricing models. Practical knowledge of services like Amazon EC2, Amazon S3, AWS Lambda, Amazon Bedrock, and Amazon SageMaker AI is recommended. No programming, mathematical modeling, or data engineering experience is required, as the exam explicitly excludes tasks such as coding AI/ML models, performing hyperparameter tuning, or implementing security and governance frameworks from scratch.
The AIF-C01 exam consists of 65 total questions to be completed within 90 minutes, of which 50 are scored and 15 are unscored pretest questions embedded throughout the exam without identification. The exam costs $100 USD and is delivered either at a Pearson VUE testing center or via online proctoring. It is available in 12 languages including English, Arabic, French, German, Italian, Japanese, Korean, Portuguese (Brazil), Spanish, and Simplified and Traditional Chinese.
Question types include multiple choice (one correct answer from four options), multiple response (two or more correct answers from five or more options, requiring all correct selections for credit), ordering (arranging three to five steps in the correct sequence), and matching (pairing responses to three to seven prompts). The exam uses a compensatory scoring model—no section needs to be passed independently. Scores are reported on a scaled range of 100–1,000, with a minimum passing score of 700. There is no penalty for guessing.
Holding the AWS Certified AI Practitioner certification signals to employers that a professional can meaningfully engage in AI strategy, vendor conversations, and cross-functional AI projects—even without an engineering background. AWS-commissioned research found that employers are willing to pay 41–47% more to hire workers with AI skills depending on the function, with IT professionals commanding the highest premiums. This certification serves as evidence of that skill set in a verifiable, vendor-recognized format.
The AIF-C01 is particularly valuable for professionals transitioning toward AI-adjacent roles or seeking to add AI credibility to existing business or IT careers. It complements AWS Cloud Practitioner and can serve as a stepping stone toward more technical certifications such as AWS Certified Machine Learning Engineer – Associate. Earning the ML Engineer – Associate certification will also automatically recertify the AI Practitioner, making it a logical progression path for those looking to deepen their AWS AI expertise over time.
5 sample questions with answers and explanations. Start a practice session to test yourself across all 426 questions.
Preview — answers shown1. The company must ensure that vulnerabilities are identified and addressed throughout the software development lifecycle for AI workloads. Which foundational security capability aligns with this strategy?
Explanation
Application security focuses on identifying and addressing vulnerabilities throughout the development lifecycle, ensuring robust security for AI systems from design to deployment.
2. An ML engineer has developed a foundation model for a help desk chatbot. The responses are technically accurate but don't match the organization's communication style. The engineer wants to modify the responses to align with company tone without incurring high costs. Which method will achieve these requirements most cost-effectively?
Explanation
Prompt engineering is the most cost-effective method for adjusting model outputs to match organizational communication style. It involves crafting specific input prompts to guide the model's tone and style without requiring expensive model retraining. Hyperparameter tuning is computationally expensive, feature engineering affects inputs not outputs, and data preprocessing is for training data preparation. Prompt engineering provides immediate control over response style without significant costs.
3. An AI engineer is developing a credit scoring system and wants to ensure the model's decision-making process is transparent and understandable. Which core dimension of responsible AI does this relate to?
Explanation
Transparency ensures the model's decision-making process is clear and understandable, fostering trust and accountability in financial decision-making.
4. A financial institution, 'EquiLend Solutions', is developing an AI model for loan applications. They are committed to ensuring their model does not unfairly discriminate against applicants based on protected characteristics and that its decisions are explainable. This commitment primarily aligns with which core principle of responsible AI development?
Explanation
Fairness and transparency are key principles of responsible AI. Fairness involves ensuring that AI systems do not perpetuate or amplify unjust biases, while transparency relates to understanding how AI models make decisions, which is crucial for accountability and trust.
5. An e-commerce platform, 'StyleSuggest AI', wants to provide personalized product recommendations to its users based on their browsing history and past purchases. Which AWS AI service is specifically designed for building and deploying real-time recommendation systems?
Explanation
Amazon Personalize is a machine learning service that makes it easy for developers to create individualized recommendations for customers using their applications. It abstracts away the complexity of building, training, and deploying recommendation models.
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