AWS Certified AI Practitioner
270 practice questions
Last reviewed: April 2026
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The AWS Certified AI Practitioner (AIF-C01) is a foundational-level credential introduced by AWS in October 2024 to validate practical understanding of AI, machine learning, and generative AI services on AWS. It targets non-engineering roles — product managers, business analysts, sales engineers, and technical decision-makers — as well as developers stepping into the AWS AI ecosystem for the first time. The exam is heavy on conceptual fluency rather than hands-on coding: expect questions on foundation models, prompt engineering, responsible-AI guidelines, and matching AWS services (Amazon Bedrock, SageMaker, Comprehend, Transcribe, Rekognition) to typical use cases.
Core ML vocabulary (supervised vs. unsupervised, training vs. inference, model evaluation). Light statistics. About 1 in 5 questions sit here.
Foundation models, transformer basics, embeddings, RAG patterns, prompt engineering techniques. The largest single domain at 24%.
Largest domain by weight. Choosing the right AWS GenAI service for a use case (Bedrock for LLMs, SageMaker JumpStart for fine-tuning, Comprehend for NLP). Expect scenario questions.
Bias, fairness, explainability, hallucination mitigation, data governance. Lower weight (14%) but high-density questions.
Services you'll encounter on the exam and why each one matters.
Fully managed service that exposes foundation models from Anthropic, Meta, Mistral, AI21, Cohere, Stability AI, and Amazon Titan through a single API.
Why it's on the exam: Bedrock is the centerpiece of Domain 3 (Applications of Foundation Models) — expect scenario questions on model selection, inference parameters, and managed vs. self-hosted tradeoffs.
Managed RAG (retrieval-augmented generation) on top of S3 documents and a vector store, with built-in chunking, embeddings, and citation support.
Why it's on the exam: RAG is the canonical answer to "how do you ground a foundation model in private data without fine-tuning?" — a recurring AIF-C01 scenario.
Policy layer that filters harmful content, blocks denied topics, redacts PII, and grounds responses to reduce hallucinations.
Why it's on the exam: Domain 4 (Guidelines for Responsible AI) asks how to mitigate hallucinations and unsafe outputs — Guardrails is the AWS-native answer.
Orchestration layer that lets a foundation model call APIs, query knowledge bases, and chain multi-step actions via tool use.
Why it's on the exam: Agents map directly to the "AI orchestration / action-taking" scenarios under Domain 3; expect questions distinguishing Agents from plain Bedrock inference.
AWS-managed generative-AI assistant — Q Developer for coding inside IDEs and the AWS Console, Q Business for enterprise data Q&A.
Why it's on the exam: AIF-C01 introduces Q as the packaged consumer of foundation-model capabilities; questions test when to pick Q vs. build on Bedrock directly.
End-to-end ML platform covering notebooks, training jobs, hyperparameter tuning, managed inference endpoints, and MLOps pipelines.
Why it's on the exam: SageMaker is the reference platform for the full ML lifecycle in Domain 2 (Fundamentals of AI and ML) — expect questions on training vs. inference vs. deployment.
Catalogue of pre-trained foundation and task-specific models with one-click deployment and fine-tuning notebooks.
Why it's on the exam: AIF-C01 distinguishes "use a pre-built model" (JumpStart) from "call a hosted API" (Bedrock) — knowing the boundary is a frequent exam question.
Bias-detection and explainability tool that produces SHAP feature attributions and pre/post-training bias metrics on tabular and foundation-model outputs.
Why it's on the exam: Domain 4 (Responsible AI) tests bias detection and explainability — Clarify is the named AWS service in those questions.
Managed NLP service for sentiment analysis, entity recognition, key-phrase extraction, language detection, and PII identification.
Why it's on the exam: AIF-C01 expects you to pick the right pre-built AI service per use case; Comprehend is the canonical answer for "analyze text without training a model."
Computer-vision service for label detection, face analysis, content moderation, celebrity recognition, and video activity detection.
Why it's on the exam: The reference answer for "I have images/video, no ML team" scenarios — appears in service-selection questions across Domains 2 and 3.
Document-understanding service that extracts text, key-value pairs, tables, and form fields from PDFs and scanned images.
Why it's on the exam: Distinguishing Textract (structured document extraction) from Rekognition Detect Text (generic OCR) is a recurring AIF-C01 distractor pattern.
Speech-to-text service with speaker identification, custom vocabularies, real-time streaming, and medical/call-analytics variants.
Why it's on the exam: Pairs with Comprehend in "audio → text → analysis" pipelines that appear in scenario questions on integrated AI workflows.
Text-to-speech service with neural and generative voices across dozens of languages, SSML support, and custom lexicons.
Why it's on the exam: The pre-built TTS option you contrast with custom voice training — questions test when Polly is sufficient vs. when SageMaker is needed.
Neural machine translation across 75+ languages, with custom terminology and Active Custom Translation for domain-specific phrasing.
Why it's on the exam: The expected answer whenever a question asks how to localize content without training a translation model.
Conversational-AI service for building text and voice chatbots with intents, slots, and Polly-backed speech output.
Why it's on the exam: AIF-C01 frames Lex as the pre-built conversational option to contrast with Bedrock-Agents-driven custom assistants.
ML-powered enterprise search across documents, SharePoint, Confluence, and databases with natural-language query understanding.
Why it's on the exam: Appears as a non-generative retrieval baseline to compare against Bedrock Knowledge Bases when latency or freshness matters more than synthesis.
Account-wide access control: users, roles, policies, federation, and least-privilege permissions for every AI service call.
Why it's on the exam: Domain 5 (Security, Compliance, and Governance) tests least-privilege patterns for Bedrock/SageMaker access — IAM roles and policies are the named mechanism.
Managed creation and control of cryptographic keys used to encrypt training data, model artifacts, and inference outputs at rest.
Why it's on the exam: Encryption-at-rest with customer-managed keys is the standard exam answer for protecting model weights and training corpora.
Managed sensitive-data discovery service that uses ML to find PII, credentials, and financial data in Amazon S3 buckets.
Why it's on the exam: Cited in Domain 4/5 questions about scanning training corpora and RAG document sets for sensitive data before they reach a model.
Metrics, logs, and alarms across AWS services — including Bedrock invocation logs, SageMaker endpoint metrics, and model-monitor outputs.
Why it's on the exam: The exam expects CloudWatch for ongoing monitoring of model drift, cost, and operational health post-deployment.
$90k–$135k–$195k USD annual
Range covers US-based, mid-to-senior AI/ML roles where AWS proficiency is required. Entry roles and non-coastal markets trend lower; FAANG / unicorn senior roles trend significantly higher (often $250k+ TC). The cert alone does not unlock these salaries — it complements demonstrated experience.
Source: levels.fyi 2025 cloud-AI roles, U.S. BLS OEWS May 2024 (15-1252 software developers, 15-2099 ML scientists). Figures are approximate; actual compensation depends on role, region, and experience.
AI/ML hiring on AWS-centric stacks accelerated through 2024–2026 as enterprise GenAI adoption moved from pilot to production. The AIF-C01 functions as a screening signal in roles where deep ML coding is not required — recruiters and hiring managers use it to filter for candidates who can talk credibly about Bedrock, SageMaker, RAG architectures, and responsible-AI tradeoffs. As a foundational credential it does not by itself qualify candidates for ML engineering roles; for those, the AWS Machine Learning Engineer Associate (MLA-C01) or specialty certs are stronger signals.
There are no formal prerequisites. AWS recommends six months of exposure to AI/ML use cases on AWS, but the exam is genuinely accessible to anyone who has worked through the official AWS AI Practitioner learning path (~20 hours of Skill Builder content) and has a working understanding of cloud basics.
If you have no AWS background at all, completing the AWS Certified Cloud Practitioner (CLF-C02) first will make AIF-C01 noticeably easier — many AIF-C01 questions assume baseline familiarity with AWS service names, the shared-responsibility model, and basic IAM concepts.
AIF-C01 is rated foundational — it is one of the more approachable AWS certifications. Expect to study 30–60 hours over 4–6 weeks if you have no prior AI/ML or AWS background; 15–25 hours over 2–3 weeks if you have either of those. The exam is multiple-choice and multiple-response, 65 scored questions in 90 minutes, with no hands-on labs.
The most common stumbling block is the breadth of AWS GenAI service names — there are around a dozen named services across Bedrock, SageMaker, Comprehend, Transcribe, Polly, Translate, Textract, Kendra, Rekognition, and Q. Memorizing which service maps to which use case (text generation vs. summarization vs. classification vs. transcription) is most of what separates passing from failing.
Initial general availability. Beta exam ran August–October 2024 with discounted pricing. Current version as of April 2026.
AIF-C01 (AWS Certified AI Practitioner) is a considered an entry-level exam testing breadth of conceptual understanding rather than hands-on depth Foundational-level exam. Most candidates need 30–80 hours of study spread over 3–6 weeks for foundational-level exams. Most candidates who score consistently above the passing threshold on practice exams pass on their first attempt.
Most candidates need 30–80 hours of study spread over 3–6 weeks for foundational-level exams. Time-to-pass varies widely by prior experience. Engineers with hands-on production experience in the underlying technology typically need less; candidates new to the platform should plan toward the upper end of that range.
AIF-C01 is a recognized credential in the AWS ecosystem and signals validated knowledge to employers, recruiters, and clients. Whether it is worth the time and fee for you depends on your role and goals — it tends to pay off most for cloud engineers, architects, and consultants who work with AWS day-to-day or want to move into roles that do.
The passing score for AIF-C01 is 700 / 1000. The exam contains 65 questions and lasts 1 hr 30 min.
The AIF-C01 exam fee is $100 USD. Fees are set by AWS and may vary by region; always confirm the current price on the official AWS certification page before booking.
AWS certifications are valid for 3 years. Recertify by passing the current version of the same exam, or by passing a higher-level exam in the same path before expiration.
Yes. You can take the exam online (proctored via the provider's secure browser, available 24/7 in most regions) or at an in-person Pearson VUE test center during business hours. Both formats use the same questions, time limit, and passing score.
CertLabPro provides 15 study modes across the practice question bank for AIF-C01. The exam-simulation mode mirrors the real exam: 65 questions in 1 hr 30 min, with the same passing threshold of 700 / 1000. Browse mode lets you read every Q&A statically.