Certification guide
The AWS Certified AI Practitioner (AIF-C01) is AWS’s foundational credential for people who work with artificial intelligence in a business context — analysts, project managers, architects, developers, and leaders who need to make sound decisions about AI, machine learning, and generative AI on AWS without necessarily building models themselves. It validates that you can explain core AI/ML and generative-AI concepts, match the right technology to a business problem, apply foundation models effectively, and use all of it responsibly and securely.
The exam assumes up to six months of exposure to AI/ML technologies on AWS and familiarity with core services such as Amazon S3, AWS Lambda, and Amazon SageMaker. You will not write code or tune hyperparameters. You will be tested on judgment: when generative AI fits and when it does not, RAG versus fine-tuning versus prompt engineering, which managed AI service solves which problem, and how bias, transparency, security, and governance obligations shape real deployments. The 2026 exam-guide refresh added agentic AI concepts — Amazon Bedrock Agents and the AgentCore runtime, Bedrock Data Automation, Prompt Management, and business-alignment metrics such as task completion rate and cost per interaction — and our question banks reflect that current outline.
Fundamentals of AI and ML
Domain 1 · 20%Core concepts and vocabulary: AI versus ML versus deep learning versus generative and agentic AI; supervised, unsupervised, and reinforcement learning; classification, regression, clustering, and anomaly detection; overfitting, bias, and evaluation metrics such as precision, recall, and F1; inferencing types from batch to real time; the ML development lifecycle and MLOps; and knowing when AI is — and is not — the right tool. Expect service-to-use-case mapping across SageMaker, Comprehend, Transcribe, Textract, Rekognition, Personalize, Forecast, and Lex.
Fundamentals of Generative AI
Domain 2 · 24%How foundation models work and what they are good for: tokens, embeddings, context windows, transformers, and diffusion models; multimodality; inference parameters like temperature, top-p, and top-k; hallucination, nondeterminism, and interpretability limits; the foundation model lifecycle; and the AWS generative-AI stack — Amazon Bedrock, Amazon Q Business and Q Developer, SageMaker JumpStart, and PartyRock — including on-demand versus provisioned-throughput cost trade-offs.
Applications of Foundation Models
Domain 3 · 28%The heaviest and most technical domain: model-selection criteria; prompt engineering from zero-shot and few-shot to chain-of-thought and negative prompting; prompt attacks such as injection, jailbreaking, and leaking; Retrieval Augmented Generation with Bedrock Knowledge Bases and vector stores like OpenSearch and Aurora pgvector; the customization spectrum from prompting through RAG and fine-tuning to continued pre-training; Bedrock Agents, Guardrails, and Prompt Management; and evaluation — ROUGE, BLEU, BERTScore, human review, and business-alignment metrics.
Guidelines for Responsible AI
Domain 4 · 14%Fairness, bias sources and dataset representativeness, transparency and explainability, veracity, safety, and human oversight — plus the tooling that operationalizes them: SageMaker Clarify for bias detection and explainability, Amazon A2I for human review, SageMaker Model Cards for documentation, and AWS AI Service Cards for provider transparency. Expect scenario questions about interpretable-versus-accurate model trade-offs and the legal risks of hallucinated content.
Security, Compliance, and Governance for AI Solutions
Domain 5 · 14%Securing AI systems and governing their data: IAM least privilege, KMS encryption, PrivateLink/VPC endpoints for private model access, Macie for sensitive-data discovery, CloudTrail and Bedrock model invocation logging for auditability, and the shared responsibility model. Governance spans data lineage and cataloging, retention, vendor due diligence, AWS Artifact, Config, Audit Manager, and frameworks such as the NIST AI RMF and ISO/IEC 42001.
Our practice exams mirror the official form: 65 questions in 90 minutes, weighted exactly to the blueprint above, with each set slanted toward a different industry so scenarios stay fresh across attempts. AWS reports a scaled score of 100–1,000 with 700 to pass; we set the practice pass mark at 70% as the honest raw-score equivalent. Because every set carries the official domain weighting, clearing the bar here means you covered the blueprint the way the real exam distributes it — earned across all five domains, not luck in the heavy ones.
AWS Certified AI Practitioner - Practice Exam
Full-length 65-question AIF-C01 practice exam weighted to the official blueprint.
Subscribe to startTrademark notice & independence. AWS, Amazon Web Services, AWS Certified AI Practitioner, Amazon Bedrock, Amazon SageMaker, and related marks are trademarks of Amazon.com, Inc. or its affiliates. Certifym is an independent study resource and is not affiliated with, endorsed by, or sponsored by Amazon Web Services. AIF-C01 exam objectives referenced here are drawn from the publicly available AWS exam guide.
All practice questions and explanations on this site are original content written to the published exam objectives. They are not actual exam questions, and no braindump material is used or tolerated.