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AIF-C01 practice question: ML lifecycle, MLOps and metrics (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 hospital is testing a model that screens scans for a serious but treatable condition. In a validation set of 1,000 scans, 40 show the condition.

The model flags 36 of those 40 and 60 healthy scans for follow-up. Missing a patient is far worse than a follow-up check.

Which metric should the team focus on MOST?

Choose one answer.

  1. A. Precision, because it shows how many flagged scans truly have the condition.
  2. B. Accuracy, because it counts every correct prediction equally.
  3. C. Recall, because it shows what share of patients with the condition the model finds.
  4. D. Root mean squared error, because it penalises large mistakes.
Show the answer and explanation

Answer: C

  • A. Wrong: Precision here is 36 out of 96 flagged scans, or 37.5 per cent. It matters most when false alarms are costly, which the hospital says is not the main risk.
  • B. Wrong: Accuracy here is 87.6 per cent, but most scans are healthy, so the total can look good while missing real cases. It hides the four missed patients.
  • C. Right: Correct. Recall is 36 out of 40, or 90 per cent. The team can lower the decision threshold to find more of the 40 patients, at the cost of more follow-up checks, which the hospital accepts. Learn more: What is machine learning? (opens in a new tab)
  • D. Wrong: RMSE measures the size of errors in numeric predictions, such as prices. A yes-or-no screen is a classification problem, so RMSE does not fit.

Explanation

Recall measures the share of real cases that the model finds, so it is the metric to prioritize when a missed case is the costly error. The figures show precision at 37.5 per cent and accuracy at 87.6 per cent, which both hide the four missed patients differently. This question tests choosing the metric from the business cost of each type of error.

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