AIF-C01 Practice Tests

AWS AI Practitioner

Establish foundational AI knowledge with our comprehensive AWS AI Practitioner practice tests. Understand AI, ML, and GenAI concepts, learn AWS AI services, and practice responsible AI patterns with exam-aligned questions and detailed explanations.

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Duration

90 minutes

Questions

65 questions (50 scored, 15 unscored)

Cost

$100 USD

Where to register

Amazon Web Services

Issued by Amazon Web Services. Delivered via Pearson VUE or online proctored exam. Foundational AWS certification designed for those with up to 6 months exposure to AI/ML technologies.

01·Overview

Certification overview

The format, prerequisites, and what to expect on exam day.

Exam details

Exam Code
AIF-C01
Duration
90 minutes
Questions
65 questions (50 scored, 15 unscored)
Format
Multiple choice, multiple response, ordering, and matching
Passing Score
700/1000
Cost
$100 USD
Validity
3 years
Languages
English, Arabic, French, German, Italian, Japanese, Korean, Portuguese (Brazil), Spanish (Latin America and Spain), Simplified Chinese, Traditional Chinese

Prerequisites

Up to 6 months of exposure to AI/ML technologies on AWS
Familiarity with core AWS services like EC2, S3, Lambda, Bedrock, and SageMaker
Understanding of AWS shared responsibility model for security and compliance
Familiarity with AWS Identity and Access Management (IAM)
Basic knowledge of AWS service pricing models

02·Domains

Exam domains

Topics on the official blueprint, with their relative weight.

Fundamentals of AI and ML

20%

AI, ML, deep learning, neural networks, and computer vision concepts
Natural language processing (NLP), large language models (LLMs), and generative AI
Types of inferencing: batch, real-time, asynchronous, and serverless
Supervised, unsupervised, and reinforcement learning methods
Model training, inferencing, bias, and fairness concepts
AI/ML development lifecycle and pipeline components
AWS managed AI/ML services: SageMaker, Transcribe, Translate, Comprehend, Lex, Polly

Fundamentals of Generative AI

24%

Tokens, chunking, embeddings, vectors, and transformers
Foundation models, multi-modal models, and diffusion models
Prompt engineering and context engineering fundamentals
Agentic AI concepts and multi-agent systems
Model Context Protocol (MCP) for connecting agents to external systems
Token-based pricing models and cost implications
FM lifecycle: data selection, pre-training, fine-tuning, evaluation, deployment
AWS GenAI services: Bedrock, SageMaker JumpStart, Amazon Q, Kiro

Applications of Foundation Models

28%

Foundation model selection criteria: cost, latency, modality, customization
Inference parameters: temperature and input/output length effects
Retrieval Augmented Generation (RAG) and knowledge bases
Vector databases: OpenSearch, Aurora, Neptune, RDS for PostgreSQL
FM customization approaches: fine-tuning, in-context learning, model distillation
Prompt engineering techniques: chain-of-thought, few-shot, zero-shot learning
Fine-tuning methods and data preparation for FMs
FM performance evaluation: ROUGE, BLEU, BERTScore, LLM-as-a-judge

Guidelines for Responsible AI

14%

Responsible AI principles: bias, fairness, inclusivity, robustness, safety, veracity
Bias detection and mitigation in AI systems
Transparent and explainable models versus black-box approaches
Legal risks of GenAI: IP infringement, biased outputs, customer trust
Dataset characteristics: inclusivity, diversity, balance, curation
Tools for monitoring responsible AI: SageMaker Clarify, Model Monitor, Amazon A2I
Effects of bias and variance on model performance and demographic groups
Human-centered design for explainable AI

Security, Compliance, and Governance for AI Solutions

14%

IAM roles, policies, and permissions for AI systems
Encryption at rest and in transit for AI workloads
AWS PrivateLink and shared responsibility model
Data lineage and cataloging for data origins
Privacy-enhancing technologies and data access control
Prompt injection, hallucination detection, and output validation
Audit trails, logging, and compliance monitoring
Governance frameworks and compliance regulations for AI
AWS services: CloudTrail, Config, Audit Manager, Inspector, Artifact

03·Key topics

What you actually study

Service families and concept clusters that show up across questions.

Amazon Bedrock

Foundation models and managed APIs
Bedrock Guardrails for responsible AI
Knowledge Bases for RAG applications
Bedrock Model Evaluation tools
Prompt management and versioning

SageMaker AI Suite

SageMaker JumpStart for pre-built solutions
SageMaker Clarify for bias detection and monitoring
Model Cards for model transparency
Model Monitor for production monitoring
Training and fine-tuning workflows

AI Services and APIs

Amazon Transcribe for speech recognition
Amazon Translate for language translation
Amazon Comprehend for NLP and text analysis
Amazon Lex for conversational interfaces
Amazon Polly for text-to-speech generation

Data and Vector Stores

Amazon OpenSearch for vector database capabilities
Amazon Aurora PostgreSQL with pgvector
Amazon Neptune for graph databases
Amazon RDS for relational data storage
Data governance and lifecycle management

Responsible AI and Governance

Fairness, bias, and inclusivity principles
Transparency and explainability requirements
Data privacy and protection strategies
Compliance and regulatory frameworks
Audit and monitoring tools

GenAI Development Patterns

Prompt engineering best practices
In-context learning and few-shot techniques
RAG implementation and grounding
Fine-tuning and transfer learning
Agent orchestration and workflow patterns

04·Study tips

How to actually pass it

Practical strategies for the weeks before, and the morning of.

Preparation strategy

Master the core AI concepts: understand the difference between AI, ML, deep learning, and GenAI before diving into AWS-specific implementations.
Practice with AWS Bedrock Playground to experiment with foundation models and understand prompt engineering in a hands-on environment.
Study the official exam guide domains in order; focus on foundational concepts in domains 1 and 2 before advancing to applications.
Use AWS Skill Builder for structured learning on AI/ML services; the foundational-level courses align directly with AIF-C01.
Explore AWS Well-Architected Framework guidelines for AI/ML workloads to understand design considerations and best practices.
Review case studies on AWS website to understand real-world applications of AI services and responsible AI practices.
Practice identifying when GenAI is appropriate versus when traditional ML is better suited for specific business problems.

Exam day

Read each question carefully to identify whether it asks for a single-select or multiple-response answer; missing even one correct option on multiple-response questions means no credit.
Manage time across 65 questions in 90 minutes; allocate roughly 80 seconds per question and flag difficult ones for later review.
Focus on practical business applications rather than deep mathematical or technical implementation details; the exam emphasizes understanding over coding.
Answer every question; unanswered questions are scored as incorrect and there is no penalty for guessing.
Remember the passing score is 700/1000; you do not need perfect performance on every domain due to the compensatory scoring model.
Pay attention to responsibility and governance aspects of AI; these are emphasized throughout domains 4 and 5.
Use elimination strategies for prompt engineering and foundation model selection questions; often one or two options are clearly inappropriate.

Demonstrate foundational AI understanding.

Align your learning with the official exam blueprint. Start free, no card required.

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