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AIF-C01 practice question: Use cases and ML techniques (1 of 3)

Fundamentals of AI and ML A free question from our original AIF-C01 practice exam. Try it, then open the answer to see why each option is right or wrong.

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A car leasing company wants to estimate the resale value of each used vehicle when its lease ends, using mileage, age and engine size.

It has five years of past lease returns, each with the price the car eventually sold for.

Which type of ML problem is this?

Choose one answer.

  1. A. Regression, because the model outputs a continuous numeric value, a resale price.
  2. B. Binary classification, because each car either sells above the target or does not.
  3. C. Clustering, because similar vehicles can be grouped into segments.
  4. D. Anomaly detection, because some lease returns sell for unusual prices.
Show the answer and explanation

Answer: A

  • A. Right: Correct. The target is a number on a continuous scale, so this is regression. The training data already holds the final sale prices. Learn more: Linear Learner Algorithm in Amazon SageMaker AI (opens in a new tab)
  • B. Wrong: Binary classification predicts a yes-or-no label. Splitting prices at one target throws away the size of each price, which the company needs.
  • C. Wrong: Clustering groups vehicles without a target value. It does not produce an estimated resale price for a new car.
  • D. Wrong: Anomaly detection flags rare, unusual records. It does not predict the price of a typical car at lease end.

Explanation

When the output is a continuous number, such as a price, the problem is regression. Classification predicts categories, clustering groups data without a target, and anomaly detection looks for unusual records. This question tests matching the type of output to the ML problem.

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