Free AWS AI Practitioner Data Engineering for AI Practice Test 2026 — AIF-C01 Questions
This free AWS AI Practitioner Data Engineering for AI practice test covers data sourcing, storage, labeling, feature engineering, and pipelines on AWS — S3, AWS Glue, Athena, Lake Formation, SageMaker Ground Truth, Feature Store, and Data Wrangler. Each question includes a detailed explanation with AWS service context — perfect for AIF-C01 exam prep.
Key Topics in AWS AI Practitioner Data Engineering for AI
- S3 Data Lakes
- AWS Glue & Athena
- Lake Formation
- SageMaker Ground Truth
- Feature Store
- Data Wrangler
6 Free AWS AI Practitioner Data Engineering for AI Practice Questions with Answers
Sample Question 1 — 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?
- A. Amazon Rekognition
- B. Amazon Comprehend (Correct answer)
- C. Amazon Polly
- 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 2 — 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?
- A. Amazon SageMaker (Correct answer)
- B. AWS Glue
- C. Amazon Lex
- 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 3 — 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?
- A. Amazon Transcribe (Correct answer)
- B. Amazon Rekognition
- C. Amazon Kendra
- 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 4 — Data Engineering for AI
A healthcare provider needs to extract and analyze medical information from patient records. Which AWS service can assist in this task?
- A. Amazon Comprehend Medical (Correct answer)
- B. Amazon Polly
- C. Amazon SageMaker
- D. Amazon Personalize
Correct answer: A
Explanation: Amazon Comprehend Medical is specifically designed for extracting medical information from unstructured text. Amazon Polly is for text-to-speech, Amazon SageMaker is for building ML models, and Amazon Personalize is for personalized recommendations.
Sample Question 5 — Data Engineering for AI
A logistics company wants to optimize its delivery routes using historical data. Which AWS service is most appropriate for predicting future delivery times based on this data?
- A. Amazon Forecast (Correct answer)
- B. Amazon Rekognition
- C. Amazon Translate
- D. Amazon Lex
Correct answer: A
Explanation: Amazon Forecast is a time-series forecasting service that can predict future events based on historical data. Amazon Rekognition is for image and video analysis, Amazon Translate is for language translation, and Amazon Lex is for building conversational interfaces.
Sample Question 6 — Data Engineering for AI
Which AWS service would you recommend for a company looking to build a chatbot to handle customer inquiries?
- A. Amazon Lex (Correct answer)
- B. Amazon Comprehend
- C. Amazon SageMaker
- D. Amazon Rekognition
Correct answer: A
Explanation: Amazon Lex is used to build conversational interfaces for applications, making it ideal for chatbots. Amazon Comprehend is for text analysis, Amazon SageMaker is for building ML models, and Amazon Rekognition is for image and video analysis.
About the AWS AI Practitioner Exam
- Questions: 65 (50 scored + 15 unscored)
- Time: 90 minutes
- Passing score: 700 / 1000
- Cost: $100 USD
- Domains: 4 (this is ~22% of the exam)
- Validity: 3 years (recertifiable)
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