D1 · Fundamentals of AI and ML
Exam weight 20%
Covers the conceptual foundation of AI and ML — what types of problems each paradigm solves, how models are developed from data, and which AWS services map to those paradigms.
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AWS Certified AI Practitioner Study Hub
AI/ML fundamentals, generative AI, foundation models, responsible AI, and AWS security & compliance. 90-question timed exam, 175 flashcards, per-domain drills, 12-week plan.
Exam Domains
The 5 domains on the AIF-C01 exam, with their weight on the test. Each has its own objectives page, flashcard deck, and drill in the hub.
D2 · Fundamentals of Generative AI
Exam weight 24%
Covers how foundation models and large language models work, how they are deployed on AWS, and the concepts that govern their outputs — including tokenization, embeddings, temperature, and prompting.
D3 · Applications of Foundation Models
Exam weight 28%
The largest domain — covers practical decisions around deploying and customizing FMs: choosing the right model, fine-tuning vs. RAG, evaluating output quality, and integrating FMs into real applications.
D4 · Guidelines for Responsible AI
Exam weight 14%
Responsible AI governs how models are designed, evaluated, and monitored to ensure they are safe, fair, and trustworthy. Covers bias, fairness, transparency, and AWS governance tooling.
D5 · Security, Compliance, and Governance for AI
Exam weight 14%
Covers the security controls, compliance frameworks, and governance practices specific to AI workloads on AWS — including identity, data protection, network isolation, and audit mechanisms.
What's Inside This Hub
Everything is free and built from the official exam outline.
Domain Breakdowns
Every AIF-C01 domain decomposed into objectives with weights, key terms, and exam-day focus areas.
163 Flashcards
Flip, tag as known or review, shuffle, and track progress. Your session survives a refresh.
90-Question Practice Exam
Timed, with a question navigator, flags, auto-save resume, and per-question explanations with a domain breakdown of your score.
Objective Drills
Per-domain timed drills to isolate weak domains, plus an AI-generated targeted drill for members.
12-Week Study Plan
Checkable weekly milestones, daily hour targets, and a final-week sprint. Copy-to-clipboard export.
Cheat Sheet & Resources
Printable one-page reference for the final week, plus vetted books, courses, labs, and practice-exam vendors.
Sample Flashcards
Five of the 163 cards in the hub. Tap a card to see the answer.
Artificial Intelligence (AI)
The broad field of computer science focused on creating systems capable of performing tasks that typically require human intelligence, such as visual perception, speech recognition, decision-making, and language translation.
Machine Learning (ML)
A subset of AI where systems learn patterns from data to make predictions or decisions without being explicitly programmed for each scenario. ML algorithms improve through experience.
Generative AI
AI systems that can create new content — text, images, audio, video, code — based on patterns learned from training data. Unlike discriminative AI that classifies, generative AI produces novel outputs.
Foundation Model
A large AI model trained on broad, diverse data at scale that can be adapted (fine-tuned) to a wide range of downstream tasks. Examples: GPT, Claude, BERT, Stable Diffusion.
Text Summarization
Using AI to condense long text into shorter versions while preserving key information. Can be extractive (selecting key sentences) or abstractive (generating new summary text). Common LLM use case.
Sample Practice Questions
Two of the 90 questions in the timed practice exam. Every question has an explanation like these.
A retail company wants to predict the price of a house based on features like square footage, number of bedrooms, and location. Which type of ML task is most appropriate?
A) Binary classification B) Multi-class classification C) Regression D) Clustering Correct answer: C. Regression is correct because the output is a continuous numerical value (price). Binary classification predicts one of two discrete categories. Multi-class classification predicts one of several discrete categories. Clustering groups data without predefined labels — it's unsupervised.
A developer wants to build an application that generates product descriptions using a pre-trained LLM. They want to reduce hallucinations by providing relevant product data. Which technique should they use?
A) Increase the model temperature to maximum B) Retrieval-Augmented Generation (RAG) C) Remove all context from the prompt D) Use a smaller model with fewer parameters Correct answer: B. RAG is correct — it retrieves relevant product data and includes it in the prompt, grounding the model's responses in factual information. Higher temperature increases randomness and hallucinations. Removing context gives the model nothing to ground on. Smaller models may hallucinate more.
How to Use This Hub
The path most learners follow. Move at your own pace.
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Domains
Read the domain breakdowns and note the exam weights.
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Flashcards
Work each deck until every card is tagged known.
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Practice
Take the timed exam, review explanations, then drill weak domains.
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Final Week
Follow the sprint in the study plan and print the cheat sheet.
AWS Certified AI Practitioner FAQ
Is this hub free?
Yes. VetTech Learning Hub is free with no paywall. A free account is optional and unlocks the AI exam tutor, which runs on your own AI provider key.
Can I get instructor support?
Yes. Ask about veteran cohorts, which pair the study hub with instructor-led sessions, labs, mentoring, and career support.
Are exam fees included?
Exam fees are set by the credential vendor and paid to their testing provider. We prepare you and help you schedule.
