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AIF-C01 practice question: AI, ML and deep learning basics (2 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 gym chain holds membership and visit records for 200,000 customers, but nobody has labeled them with customer types.

Marketing wants the customers split into groups of similar people so it can design a campaign for each group. It has no outcome to predict.

Which machine learning approach fits?

Choose one answer.

  1. A. Supervised classification trained on labeled customer types.
  2. B. Reinforcement learning that rewards the model for each new sign-up.
  3. C. Regression that predicts each customer's monthly spend.
  4. D. Unsupervised clustering that groups customers by similarity.
Show the answer and explanation

Answer: D

  • A. Wrong: Classification needs labeled examples of each type. The gym has no labels, so the model cannot be trained this way.
  • B. Wrong: Reinforcement learning learns by acting and receiving rewards. Counting sign-ups does not split existing customers into segments.
  • C. Wrong: Regression predicts a number for each customer. It does not divide the customers into groups of similar people for campaigns.
  • D. Right: Correct. Clustering finds groups of similar records without labels, which is what the gym needs. Each group can then receive its own campaign. Learn more: K-Means Algorithm in Amazon SageMaker AI (opens in a new tab)

Explanation

Clustering is unsupervised learning, so it works on data with no labels and no target to predict. It groups records by how similar they are. This question tests choosing clustering when the business needs segments rather than a prediction.

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