AIF-C01 Mock Exams & Practice Exam Questions | AWS Certified AI Practitioner

Tip: AIF-C01 is 65 questions / 90 minutes (50 scored + 15 unscored, unscored items not identified). Include mixed-format practice: multiple choice, multiple response, ordering, and matching.

Suggested progression

1. Task drills (daily): 20–25 questions focused on one domain/task.
2. Mixed sets (2×/week): 30–40 questions spanning 2–3 domains.
3. Timed runs (final week): 2–3 full-length runs (65 questions / 90 minutes); review every miss and re-drill weak tasks.

What to pair with practice

• Study Plan: pick a 30/60/90 day timeline → view
• Syllabus: objective-by-domain outline → view
• Cheatsheet: high-yield definitions + service pickers → open
• Overview: format, domains, and study strategy → read

Exam at a glance

• Exam name: AWS Certified AI Practitioner (AIF-C01)
• Level: Foundational
• Questions: 65 total (50 scored + 15 unscored)
• Question types: multiple choice, multiple response, ordering, matching
• Time: 90 minutes
• Delivery: Pearson VUE testing center or online proctored exam
• Result: Scaled score (100–1000); minimum passing score: 700
• Cost: 100 USD
• Languages offered: Arabic, English, French (France), German, Italian, Japanese, Korean, Portuguese (Brazil), Spanish (Latin America), Spanish (Spain), Simplified Chinese, Traditional Chinese

Tip: AIF-C01 rewards clear definitions, best-fit service choices, and a solid grasp of generative AI risks (hallucinations, privacy, prompt injection, responsible use). Unanswered questions are scored as incorrect, and there is no penalty for guessing.

Domain breakdown (weights)

• Domain 1: Fundamentals of AI and ML — 20%
• Domain 2: Fundamentals of Generative AI — 24%
• Domain 3: Applications of Foundation Models — 28%
• Domain 4: Guidelines for Responsible AI — 14%
• Domain 5: Security, Compliance, and Governance for AI Solutions — 14%

What the exam emphasizes (high level)

Expect scenario-driven items where you choose the best answer for:

• AI vs ML vs generative AI fundamentals (core terminology and lifecycle)
• Where generative AI fits (and where it doesn’t), including limitations and cost/latency trade-offs
• Foundation model application patterns (RAG, prompt engineering, evaluation)
• Responsible AI expectations (fairness, transparency, safety, human oversight)
• Security and governance for AI solutions (privacy, access controls, auditability)

Who should take AIF-C01

This exam is a strong fit for:

• Cloud practitioners and technologists who want to add AI and generative AI literacy to their AWS foundation
• Developers, analysts, and technical PMs who need to select the right AWS AI services and understand the risks
• Anyone preparing for more role-based AWS AI/ML credentials later

Recommended background (official target candidate profile):

• Up to ~6 months exposure to AI/ML technologies on AWS
• Familiarity with core AWS services (for example: EC2, S3, Lambda, Bedrock, SageMaker AI)
• Familiarity with the shared responsibility model, IAM basics, and AWS pricing models

What is generally out of scope

• Coding custom AI/ML models and algorithms
• Data engineering/feature engineering implementation details
• Hyperparameter tuning and model optimization depth
• Building/deploying full AI/ML pipelines and infrastructure
• Deep mathematical or statistical analysis of AI/ML models
• Implementing security/compliance protocols and governance frameworks from scratch

Study plan (efficient)

1. Pick a timeline: 30/60/90-day Study Plan →
2. Work the Syllabus domain-by-domain; drill after each task.
3. Keep a miss log: convert misses into one-liner rules (“RAG for fresh proprietary knowledge”, “Guardrails for policy compliance”).
4. Final week: mixed sets + a few timed runs; review every miss.

Start with the Syllabus if you want a structured, objective-by-objective path.