Free AWS AI Practitioner Machine Learning Fundamentals Practice Test 2026 — AIF-C01 Questions
This free AWS AI Practitioner Machine Learning Fundamentals practice test covers core ML concepts — supervised vs unsupervised learning, deep learning, generative AI, foundation models, prompt engineering, RAG, fine-tuning, evaluation metrics, and the AWS ML lifecycle. Each question includes a detailed explanation with AWS service context — perfect for AIF-C01 exam prep.
Key Topics in AWS AI Practitioner Machine Learning Fundamentals
- Supervised vs Unsupervised
- Deep Learning & Neural Nets
- Foundation Models & LLMs
- Prompt Engineering
- Bias & Fairness
- ML Lifecycle
6 Free AWS AI Practitioner Machine Learning Fundamentals Practice Questions with Answers
Sample Question 1 — 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?
- A. Amazon Comprehend
- B. Amazon SageMaker
- C. Amazon Personalize (Correct answer)
- 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 2 — 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?
- A. Amazon Rekognition
- B. Amazon Comprehend Medical (Correct answer)
- C. Amazon SageMaker
- 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 3 — 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?
- A. Amazon SageMaker
- B. Amazon Polly
- C. Amazon Lex (Correct answer)
- 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 4 — Machine Learning Fundamentals
A media company wants to analyze thousands of images to identify objects and scenes. Which AWS service should they use for this task?
- A. Amazon Comprehend
- B. Amazon SageMaker
- C. Amazon Rekognition (Correct answer)
- D. Amazon Textract
Correct answer: C
Explanation: Amazon Rekognition is specifically designed for image and video analysis, including object and scene detection. Amazon Comprehend is for natural language processing, Amazon SageMaker is a general-purpose machine learning platform, and Amazon Textract is for extracting text and data from documents.
Sample Question 5 — Machine Learning Fundamentals
Your company is considering using machine learning to improve its supply chain efficiency. Which AWS service provides pre-built machine learning models that could help with demand forecasting?
- A. Amazon Forecast (Correct answer)
- B. Amazon Lex
- C. Amazon Comprehend
- D. Amazon Rekognition
Correct answer: A
Explanation: Amazon Forecast is designed for time-series forecasting, making it ideal for demand forecasting in supply chain management. Amazon Lex is for conversational interfaces, Amazon Comprehend is for text analysis, and Amazon Rekognition is for image and video analysis.
Sample Question 6 — Machine Learning Fundamentals
A financial services company needs to ensure that their machine learning models are fair and unbiased. Which AWS service or feature can assist with model explainability and bias detection?
- A. Amazon Fraud Detector
- B. Amazon SageMaker Clarify (Correct answer)
- C. Amazon Comprehend
- D. Amazon Personalize
Correct answer: B
Explanation: Amazon SageMaker Clarify provides tools for model explainability and bias detection. Amazon Fraud Detector is for identifying potentially fraudulent activity, Amazon Comprehend is for text analysis, and Amazon Personalize is for recommendations.
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 ~27% of the exam)
- Validity: 3 years (recertifiable)
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