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AWS Certified AI Practitioner

AWS Certified AI Practitioner (AIF-C01) is AWS's foundation-level AI credential. Five domains: Fundamentals of AI and ML, Fundamentals of GenAI, Applications of Foundation Models, Guidelines for Responsible AI, and Security/Compliance/Governance for AI Solutions. 65 questions (50 scored, 15 unscored) in 90 minutes, scaled score 100-1,000, passing score 700.

Offered in: Arabic, English, French, Japanese, Korean, Portuguese, Spanish, Chinese.

Owner: AWS · Read: · Source: AWS Certified AI Practitioner

Exam fee at last check: not confirmed (the owner's page could not be read for the price on 2026-09-26; we hold $100 from an earlier check), per the vendor. The official objectives are at aws.amazon.com/certification/certified-ai-practitioner/. Verified 2026-09-26.

Practice is free. The full analysis adds the right answer, the explanation and the source for every question, readiness by domain and a study plan, for one payment.

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Timed mock · Where you stand

How the AWS Certified AI Practitioner exam is weighted
  1. Fundamentals of AI and ML20%
  2. Fundamentals of GenAI24%
  3. Applications of Foundation Models28%
  4. Guidelines for Responsible AI14%
  5. Security, Compliance, and Governance for AI Solutions14%

Source: Vendor exam objectives. Checked 2026-09-26. Bar length is relative to the heaviest domain; the figure beside each name is the published weight.

What's on the exam

Fundamentals of AI and ML 20%

Core AI/ML terminology, practical use-case fit, and the AI/ML development lifecycle.

  • AI, ML, deep learning, neural networks, computer vision, NLP, and the differences between AI, ML, GenAI, and agentic AI
  • Types of inferencing: batch, real-time, asynchronous, serverless
  • Supervised, unsupervised, and reinforcement learning
  • When AI/ML is (and is not) the appropriate tool, and choosing regression vs classification vs clustering
  • The AI/ML pipeline, MLOps fundamentals, and model vs business performance metrics

Primary sources: AWS Certified AI Practitioner (AIF-C01) Exam Guide

Fundamentals of GenAI 24%

Generative AI concepts, business fit, and the AWS infrastructure used to build GenAI applications.

  • Tokens, chunking, embeddings, vectors, prompt engineering, transformer-based LLMs, foundation models, diffusion models
  • Token-based pricing and its effect on cost and performance
  • Agentic AI concepts: multi-agent systems, Model Context Protocol (MCP), tool usage, orchestration
  • GenAI advantages and disadvantages, including hallucination and nondeterminism
  • AWS GenAI infrastructure: Amazon Bedrock, SageMaker AI, SageMaker JumpStart, Strands Agents, Bedrock AgentCore

Primary sources: AWS Certified AI Practitioner (AIF-C01) Exam Guide, AWS Bedrock developer guide

Applications of Foundation Models 28%

The largest domain: design considerations, prompt engineering technique, fine-tuning, and FM evaluation.

  • FM selection criteria, inference parameters (temperature, input/output length), and RAG with vector databases (OpenSearch, Aurora, Neptune, RDS for PostgreSQL)
  • Cost tradeoffs across pre-training, fine-tuning, in-context learning, RAG, and model distillation
  • Prompt engineering techniques: zero-shot, few-shot, chain-of-thought, and risks like prompt hijacking and jailbreaking
  • Fine-tuning methods: instruction tuning, continuous pre-training, RLHF-informed data preparation
  • FM evaluation: human-in-the-loop, benchmark datasets, ROUGE, BLEU, BERTScore, LLM-as-a-judge

Primary sources: AWS Certified AI Practitioner (AIF-C01) Exam Guide, AWS Bedrock RAG overview

Guidelines for Responsible AI 14%

Building responsible AI systems and recognizing the importance of transparency and explainability.

  • Bias, fairness, inclusivity, robustness, safety, veracity, and Amazon Bedrock Guardrails
  • Legal risks of GenAI: IP infringement, biased outputs, hallucinations, loss of trust
  • Effects of bias and variance, including overfitting and underfitting
  • Transparent and explainable models, and tools like Amazon SageMaker Model Cards and Bedrock Model Evaluations

Primary sources: AWS Certified AI Practitioner (AIF-C01) Exam Guide, AWS Bedrock Guardrails

Security, Compliance, and Governance for AI Solutions 14%

Securing AI systems and the governance/compliance frameworks that apply to them.

  • IAM roles/policies, encryption, Amazon Macie, AWS PrivateLink, Bedrock AgentCore Identity and Policy, Bedrock Guardrails
  • Source citation, data lineage, and secure data engineering practices
  • Hallucination detection and grounding: RAG grounding, output validation, confidence scoring
  • Governance and compliance tooling: AWS Config, Amazon Inspector, AWS Artifact, AWS CloudTrail, AWS Trusted Advisor
  • The Generative AI Security Scoping Matrix and data governance strategies

Primary sources: AWS Certified AI Practitioner (AIF-C01) Exam Guide, AWS Bedrock PrivateLink docs

Exam-day strategy

Domain 3 (Applications of Foundation Models) is 28% of the exam, the single largest domain. Know RAG, prompt engineering techniques, and FM evaluation metrics (ROUGE, BLEU, LLM-as-a-judge) cold.

Match each scenario to one named AWS AI service where possible. The exam tests service-recognition more than deep technical detail.

Watch for distractors that confuse Comprehend (text classification, NER) with Textract (document forms), or Bedrock Knowledge Bases (RAG) with Bedrock Agents (orchestration).

Imbalanced-data accuracy traps appear in multiple questions. Always check whether the question implies imbalanced classes, and reach for F1 score or the confusion matrix instead of accuracy alone.

For Domain 5, distinguish AWS CloudTrail (audit logging of API calls) from Bedrock Guardrails (content filtering) and from Amazon Macie (sensitive-data discovery); exam distractors frequently swap these three.

50 of the 65 questions are scored; unscored questions are not identified, so treat every question as if it counts. Pace at roughly 80 seconds per question.

How to prepare for AWS Certified AI Practitioner

  • EVIDENCE-BACKEDPractice in the same format as the exam. How well practice matches the final test shapes how much it helps. (Yang et al., 2021)
  • EVIDENCE-BACKEDStart practice questions in week one, even on topics you have not studied. Failed attempts before seeing the answer still helped learning. (Kornell et al., 2009 and others)
  • EVIDENCE-BACKEDSpace your reviews over weeks instead of cramming the week before. (Cepeda et al., 2006 and others)
  • PLAUSIBLEDivide the minutes by the questions before you start, and keep a margin for a second pass if the exam allows one. (Sound arithmetic; no study compared time budgets on a licensing exam.)
Why this works

EVIDENCE-BACKED At least one peer-reviewed study or official source found the effect or states the fact. A study can be small.

PLAUSIBLE Reasonable and partly studied, but not tested on exams, or the results are mixed.

Practice that matches the final test format works better than practice that does not.

Failed attempts before seeing the answer still improved later learning of the material asked about; the benefit for material not asked about was virtually nil.

Spacing beat massing across hundreds of experiments and beat cramming with flashcards; combined with retrieval it raised scores on a real high-stakes exam.

Sound arithmetic; no study compared time budgets on a licensing exam.

Sources

  • Yang, C., Luo, L., Vadillo, M. A., Yu, R., & Shanks, D. R. (2021). Testing (quizzing) boosts classroom learning: A systematic and meta-analytic review. Psychological Bulletin, 147(4), 399-435. doi.org/10.1037/bul0000309
  • Kornell, N., Hays, M. J., & Bjork, R. A. (2009). Unsuccessful retrieval attempts enhance subsequent learning. Journal of Experimental Psychology: Learning, Memory, and Cognition, 35(4), 989-998. doi.org/10.1037/a0015729
  • Richland, L. E., Kornell, N., & Kao, L. S. (2009). The pretesting effect: Do unsuccessful retrieval attempts enhance learning? Journal of Experimental Psychology: Applied, 15(3), 243-257. doi.org/10.1037/a0016496
  • St. Hilaire, K. J., Chan, J. C. K., & Ahn, D. (2024). Guessing as a learning intervention: A meta-analytic review of the prequestion effect. Psychonomic Bulletin & Review, 31(2), 411-441. doi.org/10.3758/s13423-023-02353-8
  • Cepeda, N. J., Pashler, H., Vul, E., Wixted, J. T., & Rohrer, D. (2006). Distributed practice in verbal recall tasks: A review and quantitative synthesis. Psychological Bulletin, 132(3), 354-380. doi.org/10.1037/0033-2909.132.3.354
  • Kornell, N. (2009). Optimising learning using flashcards: Spacing is more effective than cramming. Applied Cognitive Psychology, 23(9), 1297-1317. doi.org/10.1002/acp.1537
  • Kang, S. H. K. (2016). Spaced repetition promotes efficient and effective learning: Policy implications for instruction. Policy Insights from the Behavioral and Brain Sciences, 3(1), 12-19. doi.org/10.1177/2372732215624708
  • Janes, J. L., Dunlosky, J., Rawson, K. A., & Jasnow, A. (2020). Successive relearning improves performance on a high-stakes exam in a difficult biopsychology course. Applied Cognitive Psychology, 34(5), 1118-1132. doi.org/10.1002/acp.3699

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Editorial and assessment-design review: Dr. Julian Calvo, instructional and assessment design (Ed.D. in Learning Sciences, University of Miami). He reviews question sets for key defensibility, distractor quality and fit to the cited source, and logs corrections at /corrections. This is not a review by a licensed subject-matter professional.

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