AWS Certified AI Practitioner Practice Exam & Full Question Bank
Master the official AWS Certified AI Practitioner (AIF-C01) examination blueprint. Train with 65 authentic, scenario-based questions with comprehensive 4-option distractor autopsies, memorable technical takeaways, and official AWS documentation citations.
AWS AI Practitioner Practice Questions
Official AIF-C01 Examination Blueprint Weightings
Official AWS domain allocations and question counts:
Fundamentals of AI and ML
Core definitions of artificial intelligence, machine learning, and deep learning; distinguishing supervised, unsupervised, and reinforcement learning; data preprocessing and feature engineering; overfitting vs. underfitting; model evaluation metrics (accuracy, precision, recall, F1 score, ROC-AUC, confusion matrices).
- AI vs. Machine Learning vs. Deep Learning
- Supervised, Unsupervised & Reinforcement Learning
- Regression vs. Classification vs. Clustering
Fundamentals of Generative AI
Foundation models, large language models (LLMs), multi-modal models, diffusion models; tokens and context window limitations; inference parameters (temperature, top-p, top-k, repetition penalty); prompt engineering techniques (zero-shot, few-shot, chain-of-thought); embeddings and vector spaces; Retrieval-Augmented Generation (RAG) vs. fine-tuning vs. pre-training from scratch.
- Foundation Models & Transformer Architecture Basics
- Tokens, Tokenization & Context Window Limits
- Inference Parameters (Temperature, Top-P, Top-K)
Applications of Foundation Models on AWS
Amazon Bedrock foundation model choice (Anthropic Claude, Meta Llama, Amazon Titan, Mistral, AI21 Labs); Bedrock Knowledge Bases for managed RAG; Bedrock Agents for multi-step task execution; Guardrails for Amazon Bedrock; Amazon SageMaker JumpStart; SageMaker Canvas no-code ML; Amazon Q Business and Amazon Q Developer.
- Amazon Bedrock Model Catalog & Selection
- Knowledge Bases for Amazon Bedrock (Managed RAG)
- Agents for Amazon Bedrock & Action Groups
Guidelines for Responsible AI and Security
The pillars of responsible AI: fairness, explainability, privacy, transparency, and safety/robustness; detecting and mitigating bias in training data; AWS Shared Responsibility Model for AI/ML; data governance, compliance, and privacy safeguards in Amazon Bedrock (customer data is never used to train provider foundation models); auditing model inferences with Amazon CloudWatch and AWS CloudTrail.
- Pillars of Responsible AI (Fairness, Explainability, Robustness)
- Bias Detection & Data Drift in Machine Learning
- AWS Shared Responsibility Model for AI Services
Complementary AWS AI Practitioner Study Resources
Comprehensive Study Guide
16 lessons covering AI/ML math, Generative AI mechanisms, Bedrock services, and Responsible AI.
Read Study Guide →AIF-C01 Cram Cheat Sheet
High-yield matrices: RAG vs fine-tuning, Bedrock model selection, inference hyperparameters, and Guardrails.
View Cheat Sheet →3-Week Mastery Study Plan
Structured daily study schedule with Pearson VUE first-5-minute dump sheet formulas.
Open Study Plan →Frequently Asked Questions: AWS Certified AI Practitioner (AIF-C01)
What is the AWS Certified AI Practitioner (AIF-C01) certification?
The AWS Certified AI Practitioner (AIF-C01) is AWS's premier foundational certification designed to validate overall understanding of artificial intelligence (AI), machine learning (ML), and generative AI concepts, as well as hands-on architectural knowledge of AWS services like Amazon Bedrock, SageMaker JumpStart, and Amazon Q.
What is the format, duration, and passing score of the AIF-C01 exam?
The AIF-C01 exam consists of 65 questions (multiple choice and multiple response) administered over 90 minutes. The passing score is 700 on a scaled score range of 100 to 1,000 (roughly 70% raw accuracy).
How is the AIF-C01 exam weighted across AWS's 4 blueprint domains?
The 65 questions are proportioned strictly according to the official AWS blueprint: Domain 1: Fundamentals of AI and ML (20%, ~13 Qs); Domain 2: Fundamentals of Generative AI (24%, ~16 Qs); Domain 3: Applications of Foundation Models (28%, ~18 Qs); and Domain 4: Guidelines for Responsible AI (28%, ~18 Qs).
What is the difference between Amazon Bedrock and Amazon SageMaker JumpStart?
Amazon Bedrock is a fully managed serverless service providing single-API access to foundation models without managing instances. Amazon SageMaker JumpStart is an ML hub providing open-weight models that deploy directly onto customer-managed, dedicated SageMaker instances with complete control over VPC networking and custom containers.
Does AWS use customer prompts in Amazon Bedrock to train public base models?
No. AWS explicitly guarantees that customer prompts and completions in Amazon Bedrock are never used to train the base foundation models from Amazon or third-party providers (like Anthropic or Meta), and are never shared with model vendors.