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Exam structure

What's on the AI Practitioner exam

Exam code: AIF-C01

65
Questions
90 min
Duration
700 / 1000
To pass
None
Prerequisites

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The AWS Certified AI Practitioner (AIF-C01) is a foundational certification that validates a broad understanding of artificial intelligence, machine learning, and generative AI concepts on AWS. It is designed for people who work with, or make decisions about, AI in a business context — including business analysts, project managers, and early-career technologists — as well as engineers who want a structured introduction to AWS AI services. It does not require you to write code or build models.

Domains covered

The exam is organized into weighted domains. The percentages indicate roughly how much of the exam each domain represents, which is a useful guide for allocating study time.

Fundamentals of AI and ML 20%

Core concepts and terminology of artificial intelligence and machine learning, common use cases, and the stages of an ML development lifecycle.

Fundamentals of Generative AI 24%

Generative AI concepts, foundation models, and the capabilities and limitations of generative AI, along with relevant AWS services such as Amazon Bedrock.

Applications of Foundation Models 28%

Designing applications with foundation models, including prompt engineering, retrieval-augmented generation (RAG), fine-tuning approaches, and evaluating model performance.

Guidelines for Responsible AI 14%

Responsible AI practices including fairness, bias detection, transparency, explainability, and the trade-offs involved in deploying AI systems.

Security, Compliance, and Governance for AI 14%

Securing AI systems, data governance, and the compliance and regulatory considerations that apply to AI/ML workloads on AWS.

How to prepare

The exam rewards breadth over depth: you need to understand what AI and generative AI can do, which AWS service fits a given need, and how to apply these technologies responsibly — not the underlying mathematics. Note that of the 65 questions, 50 are scored and 15 are unscored trial questions that do not affect your result. Working through full practice papers is an effective way to find the handful of service-mapping or responsible-AI topics where your understanding is thin.

AI/ML concepts mapped to AWS services

The exam frequently asks you to match a business scenario to the right AWS service. Use this table as a reference — know what each service does and when to reach for it over an alternative.

AI / ML ConceptAWS ServiceHow It Links
Responsible AI & Explainability
Bias detection & removalSageMaker ClarifyDetects bias in data and models, explains predictions
Model explainability (XAI)SageMaker ClarifyFeature attribution and SHAP values for interpretability
Human-in-the-loop reviewAmazon A2IRoutes low-confidence predictions to human reviewers
Model Training, Tuning & MLOps
Automated ML / low-code trainingSageMaker AutopilotSageMaker CanvasAuto-builds, trains, and tunes models with minimal code
Model training & tuningSageMaker TrainingAutomatic Model TuningManaged training jobs and hyperparameter optimisation
Model monitoring / drift detectionSageMaker Model MonitorDetects data and concept drift, quality degradation over time
MLOps / model lifecycleSageMaker PipelinesModel RegistryCI/CD for ML, versioning, and deployment workflows
Data labellingSageMaker Ground TruthManaged and automated data annotation for training sets
Feature managementSageMaker Feature StoreCentral repository for storing, sharing, and reusing features
Generative AI & Foundation Models
Foundation models / GenAIAmazon BedrockAccess to FMs (Claude, Titan, and others) via API
GenAI guardrails / responsible AIBedrock GuardrailsContent filtering, PII redaction, and denied-topics control
RAG (Retrieval Augmented Generation)Bedrock Knowledge BasesGrounds LLM responses in your own data sources
AI agents / orchestrationBedrock AgentsMulti-step task execution with tool and API invocation
Managed AI Services
Natural Language ProcessingAmazon ComprehendEntity extraction, sentiment analysis, key phrases, PII detection
Computer vision / image analysisAmazon RekognitionObject and face detection, content moderation, text in images
Document extraction (OCR+)Amazon TextractExtracts text, forms, and tables from documents
Speech-to-textAmazon TranscribeAudio and video transcription with speaker diarisation
Text-to-speechAmazon PollyNatural-sounding voice synthesis
Language translationAmazon TranslateNeural machine translation across languages
Conversational AI / chatbotsAmazon LexIntent recognition and dialogue management
Personalisation / recommendationsAmazon PersonalizeReal-time recommendation engine
Time-series forecastingAmazon ForecastDemand and inventory prediction using ML
Anomaly detectionAmazon LookoutSageMaker RCFDetects outliers in metrics, equipment, and data streams
Fraud detectionAmazon Fraud DetectorIdentifies fraudulent activity via ML
AI-Powered Developer & Enterprise Tools
Code generation / dev assistantAmazon Q DeveloperAI-powered coding suggestions and debugging
Enterprise GenAI assistantAmazon Q BusinessRAG over enterprise data sources for internal Q&A
Intelligent searchAmazon KendraML-powered semantic enterprise search
Exam formats and passing scores are updated periodically by AWS. Always confirm the current details on the official AWS certification page before booking your exam.

Ready to test yourself?

Do you think you are ready? Put your knowledge to the test with a free, timed practice exam that mirrors the AI Practitioner format — with instant scoring, per-domain breakdowns, and full answer explanations.

Start a practice exam →