AWS AI Practitioner exam strategy. Time management. Complete cheat sheet for all 5 domains. Key services mapping. Common exam traps.
AWS Certified AI Practitioner (AIF-C01) Exam Prep
Domain 5: Security, Compliance & Governance for AI (14%)
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1. Exam Overview — AIF-C01
Detail
Value
Exam code
AIF-C01
Questions
65 scored + 15 unscored = 80 total
Duration
90 minutes
Passing score
700 / 1000
Cost
$100 USD
Format
Multiple choice + Multiple response
Delivery
Pearson VUE (testing center or online proctored)
Validity
3 years
2. Domain Weights
Domain 1: Fundamentals of AI and ML ████████████ 20% (~13 questions)
Domain 2: Fundamentals of Generative AI █████████████████ 24% (~16 questions)
Domain 3: Applications of Foundation Models ████████████████████ 28% (~18 questions)
Domain 4: Guidelines for Responsible AI █████████ 14% (~9 questions)
Domain 5: Security, Compliance & Governance █████████ 14% (~9 questions)
Strategy: Domain 3 (28%) is the largest — focus most on Bedrock, RAG, Prompt Engineering, Fine-tuning. Domains 1+2 (44%) are foundational — understanding the concepts is sufficient.
3. Time Management Strategy
80 questions / 90 minutes = ~67 seconds per question
Strategy:
┌─────────────────────────────────────────────┐
│ Pass 1 (0-60 min): Answer easy questions │
│ → Skip difficult ones (flag for review) │
│ → Target: 60+ questions answered │
├─────────────────────────────────────────────┤
│ Pass 2 (60-85 min): Review flagged questions│
│ → Eliminate wrong answers first │
│ → Use process of elimination │
├─────────────────────────────────────────────┤
│ Pass 3 (85-90 min): Final review │
│ → Never leave questions blank │
│ → No penalty for guessing │
└─────────────────────────────────────────────┘
4. Domain 1 Cheat Sheet — AI/ML Fundamentals
Concept
Key Points
AI vs ML vs DL
AI ⊃ ML ⊃ DL. DL uses neural networks.
Supervised
Labeled data → Classification (discrete) or Regression (continuous)
Unsupervised
Unlabeled data → Clustering, dimensionality reduction
Reinforcement
Agent + Environment + Rewards → Learn by trial and error
Overfitting
Good on training, bad on test → More data, regularization, dropout
Underfitting
Bad on both → More complex model, more features, longer training
Precision
Of predicted positives, how many are correct? (avoid false positives)
Recall
Of actual positives, how many did we find? (avoid false negatives)
F1 Score
Harmonic mean of Precision and Recall
AUC-ROC
Model's ability to distinguish classes (higher = better)
5. Domain 2 Cheat Sheet — Generative AI
Concept
Key Points
Foundation Models
Large models trained on broad data, adaptable to many tasks
Transformer
Encoder-only (BERT, classify), Decoder-only (GPT, generate), Both (T5, translate)
Tokens
~4 chars = 1 token. Context window = max tokens. Pricing = per token.
Temperature
Low (0) = deterministic/factual. High (1) = creative/diverse.
Top-p
Nucleus sampling. 0.1 = focused. 0.9 = diverse.
Hallucination
Model generates false info. Mitigate with: RAG, guardrails, lower temp.
Embeddings
Text → vectors. Similar meaning = close vectors. Used for search, RAG.
Diffusion models
Image gen by denoising. Stable Diffusion, Titan Image Generator.
Multi-modal
Process multiple data types. Claude 3 (text+image), Titan MM Embeddings.