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AIF-C01 practice question: Use cases and ML techniques (3 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 telecom operator wants to predict which mobile customers will cancel their contracts next month.

It has five years of structured account data in one table, with a column recording who actually canceled. A developer proposes sending each customer record to a large foundation model with a prompt asking whether the customer will leave.

What is the MOST appropriate approach?

Choose one answer.

  1. A. Send each record to a foundation model, because larger models are more accurate on any table.
  2. B. Train a supervised classification model on the labeled table, then score each customer.
  3. C. Use a diffusion model, because it can generate likely future customer behavior.
  4. D. Use unsupervised clustering, because the cancellation column is not needed at all.
Show the answer and explanation

Answer: B

  • A. Wrong: A foundation model is built for text and open-ended generation. Sending every row through a prompt is slow and costly, and it does not learn from the recorded cancellations.
  • B. Right: Correct. The table already records the outcome for each customer, so a supervised classifier can learn the cancellation pattern and score new customers. It is cheap to run and its accuracy can be measured. Learn more: Built-in SageMaker AI algorithms for tabular data (opens in a new tab)
  • C. Wrong: Diffusion models generate images and similar media from noise. They do not predict whether a specific customer will cancel.
  • D. Wrong: Clustering works without labels, so it discards the cancellation column that makes this a supervised task. It could group customers but would not predict who will cancel.

Explanation

When historical data includes the known outcome, supervised learning is the natural fit, and a classifier on a structured table is efficient. Foundation models and diffusion models solve different problems, and clustering ignores the labels that would make the prediction accurate. This question tests matching the data and outcome to the right ML approach.

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