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.

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.

01
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
02
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
03
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
04
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
05
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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