Free AWS AI Practitioner Practice Test 2026 — AIF-C01 Exam Questions

Master the AWS Certified AI Practitioner (AIF-C01) exam with 600+ free practice questions covering all 4 AWS CAIP study domains. Each question includes a detailed explanation with AWS service context — no signup required.

AWS Certified AI Practitioner Exam Overview

Practice by AWS CAIP Domain

Domain 1: AWS AI Services (~28%)

Free AIF-C01 practice questions on Amazon Bedrock, SageMaker, Amazon Q, Rekognition, Comprehend, Textract, Polly, Transcribe, Translate, Lex, and Personalize. Practice this domain →

Domain 2: Machine Learning Fundamentals (~27%)

Free AIF-C01 practice questions on supervised / unsupervised / reinforcement learning, deep learning, foundation models, LLMs, prompt engineering, RAG, fine-tuning, model evaluation, and responsible AI. Practice this domain →

Domain 3: Data Engineering for AI (~22%)

Free AIF-C01 practice questions on S3 data lakes, AWS Glue, Athena, Lake Formation, SageMaker Ground Truth, Data Wrangler, Feature Store, and vector databases. Practice this domain →

Domain 4: Model Deployment and Operations (~23%)

Free AIF-C01 practice questions on SageMaker endpoints, Bedrock Guardrails, MLOps pipelines, model monitoring, SageMaker Clarify, IAM, KMS, and compliance for AI workloads. Practice this domain →

8 Free AWS AI Practitioner Sample Questions with Answers

Each question below includes 4 answer options, the correct answer, and a detailed explanation. These are real questions from the FlashGenius AIF-C01 question bank.

Sample Question 1 — AWS AI Services

A retail company wants to analyze customer reviews to determine the overall sentiment and categorize them by product feature. Which AWS service should they use to efficiently perform sentiment analysis and entity recognition?

  1. A. Amazon Comprehend (Correct answer)
  2. B. Amazon Rekognition
  3. C. Amazon Lex
  4. D. Amazon Polly

Correct answer: A

Explanation: Amazon Comprehend is designed for natural language processing tasks, including sentiment analysis and entity recognition. Amazon Rekognition is for image and video analysis, Amazon Lex is for building conversational interfaces, and Amazon Polly is for text-to-speech conversion.

Sample Question 2 — AWS AI Services

A financial institution needs to automate the extraction of data from scanned documents to streamline their loan processing. Which AWS service is most appropriate for this task?

  1. A. Amazon Textract (Correct answer)
  2. B. Amazon Rekognition
  3. C. Amazon Translate
  4. D. Amazon SageMaker

Correct answer: A

Explanation: Amazon Textract is specifically designed to extract text and data from scanned documents. Amazon Rekognition focuses on image and video analysis, Amazon Translate is for language translation, and Amazon SageMaker is for building, training, and deploying machine learning models.

Sample Question 3 — Data Engineering for AI

A retail company wants to analyze customer reviews to identify key themes and sentiments about their products. Which AWS service is best suited for this task?

  1. A. Amazon Rekognition
  2. B. Amazon Comprehend (Correct answer)
  3. C. Amazon Polly
  4. D. Amazon SageMaker

Correct answer: B

Explanation: Amazon Comprehend is designed for natural language processing tasks such as sentiment analysis and entity recognition. Amazon Rekognition is for image and video analysis, Amazon Polly is for text-to-speech, and Amazon SageMaker is for building and deploying machine learning models.

Sample Question 4 — Data Engineering for AI

A financial firm needs to detect fraudulent transactions in real-time. Which AWS service can help them deploy a machine learning model for this use case?

  1. A. Amazon SageMaker (Correct answer)
  2. B. AWS Glue
  3. C. Amazon Lex
  4. D. Amazon Translate

Correct answer: A

Explanation: Amazon SageMaker is a fully managed service that provides tools to build, train, and deploy machine learning models quickly. AWS Glue is for ETL, Amazon Lex is for building conversational interfaces, and Amazon Translate is for language translation.

Sample Question 5 — Machine Learning Fundamentals

Your company needs to build a real-time recommendation system for your e-commerce website. Which AWS service should you use to implement this solution efficiently?

  1. A. Amazon Comprehend
  2. B. Amazon SageMaker
  3. C. Amazon Personalize (Correct answer)
  4. D. Amazon Rekognition

Correct answer: C

Explanation: Amazon Personalize is specifically designed for building personalized recommendation systems. Amazon Comprehend is used for natural language processing, Amazon SageMaker is a general-purpose machine learning service, and Amazon Rekognition is for image and video analysis.

Sample Question 6 — Machine Learning Fundamentals

A healthcare company wants to extract insights from unstructured text data in medical records. Which AWS service would be most appropriate for this task?

  1. A. Amazon Rekognition
  2. B. Amazon Comprehend Medical (Correct answer)
  3. C. Amazon SageMaker
  4. D. Amazon Lex

Correct answer: B

Explanation: Amazon Comprehend Medical is designed for extracting information from unstructured medical text. Amazon Rekognition is for image analysis, Amazon SageMaker is a general-purpose machine learning platform, and Amazon Lex is for building conversational interfaces.

Sample Question 7 — Model Deployment and Operations

A retail company wants to deploy a machine learning model to predict customer churn. They require a service that allows easy deployment with built-in monitoring and automatic scaling. Which AWS service should they use?

  1. A. Amazon SageMaker (Correct answer)
  2. B. AWS Lambda
  3. C. Amazon Comprehend
  4. D. Amazon Rekognition

Correct answer: A

Explanation: Amazon SageMaker provides a fully managed service to deploy machine learning models with built-in capabilities for monitoring and automatic scaling. AWS Lambda is not suited for model deployment, while Amazon Comprehend and Amazon Rekognition are specific to text and image processing, respectively.

Sample Question 8 — Model Deployment and Operations

A healthcare startup needs to deploy a machine learning model that processes sensitive patient data. Which AWS feature should they consider to ensure compliance with data privacy regulations?

  1. A. Amazon SageMaker Model Monitor
  2. B. AWS Key Management Service (KMS) (Correct answer)
  3. C. Amazon Rekognition
  4. D. AWS CloudTrail

Correct answer: B

Explanation: AWS Key Management Service (KMS) helps manage encryption keys and ensure data is encrypted at rest and in transit, which is crucial for compliance with data privacy regulations. SageMaker Model Monitor is for model performance monitoring, Rekognition is for image analysis, and CloudTrail is for logging API calls.

Quick 10-Question AWS AI Practitioner Practice Test

Take a free 10-question AWS AI Practitioner quick-start practice test covering all 4 AIF-C01 domains. Get instant scoring with detailed explanations — perfect for a quick readiness check.

Free AWS AI Practitioner Cheat Sheet

Download the free AWS AI Practitioner cheat sheet — a one-page summary of every AIF-C01 domain covering Bedrock, SageMaker, responsible AI, and security at a glance.

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