Free AWS AI Practitioner Quick Practice Test 2026 — 10 Mixed-Domain Questions

Take a fast, free AWS Certified AI Practitioner (AIF-C01) practice test with 10 mixed-domain questions covering all 4 AWS CAIP domains. Perfect for a quick readiness check before exam day — instant scoring with detailed explanations on every question.

What's Covered (All 4 AWS CAIP Domains)

10 Free AWS AI Practitioner Practice Questions with Answers

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 — AWS AI Services

A company wants to implement a chatbot that can handle customer queries and provide personalized responses in multiple languages. Which combination of AWS services would best meet their needs?

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

Correct answer: B

Explanation: Amazon Lex is used to build conversational interfaces, and Amazon Translate can translate text into multiple languages, making them ideal for a multilingual chatbot. Amazon Polly is for text-to-speech, and Amazon Rekognition is for image analysis, neither of which are directly applicable to this scenario.

Sample Question 4 — 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 5 — 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 6 — Data Engineering for AI

A media company wants to automate the process of generating subtitles for their video content. Which AWS service should they use?

  1. A. Amazon Transcribe (Correct answer)
  2. B. Amazon Rekognition
  3. C. Amazon Kendra
  4. D. Amazon Forecast

Correct answer: A

Explanation: Amazon Transcribe is used for converting audio to text, making it suitable for generating subtitles. Amazon Rekognition is for image and video analysis, Amazon Kendra is for enterprise search, and Amazon Forecast is for time-series forecasting.

Sample Question 7 — 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 8 — 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 9 — Machine Learning Fundamentals

Your team is tasked with developing a chatbot that can handle customer inquiries. Which AWS service should you choose to build this chatbot with minimal effort?

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

Correct answer: C

Explanation: Amazon Lex is designed for building conversational interfaces and chatbots. Amazon Polly is for text-to-speech conversion, Amazon SageMaker is a general-purpose machine learning service, and Amazon Rekognition is for image and video analysis.

Sample Question 10 — 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.

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