AI-900 Practice Question: Describe fundamental principles of machine learning on Azure
What is a confusion matrix's 'false positive' in medical screening?
⚠ Common exam trap
It's easy for candidates to confuse 'false positive' with 'false negative' — candidates often mix up which axis (predicted vs. actual) defines the error, especially when the question uses medical screening terminology instead of standard ML terms.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
A patient predicted to have a disease who is actually healthy
In a confusion matrix, a false positive occurs when the model predicts a positive outcome (e.g., disease present) but the actual ground truth is negative (healthy). This is a Type I error, and in medical screening it represents a healthy patient incorrectly flagged as having the disease, leading to unnecessary follow-up tests and anxiety.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
A patient who tests positive and actually has the disease
Why it's wrong here
In this situation the predicted outcome and the actual outcome are both positive—the patient truly has the disease and the model correctly flags it—making it a true positive rather than a false positive. The word 'positive' in false positive refers only to the predicted label, but the term becomes false only when the actual label is negative. Thus, a correctly detected diseased patient is not a false positive.
- ✓
A patient predicted to have a disease who is actually healthy
Why this is correct
Here the model outputs 'disease present' (a positive prediction) for a person who is actually healthy (negative actual state), so the prediction is false relative to reality. This is precisely a false positive, or type I error, and in healthcare it triggers unnecessary worry, extra tests, and possibly invasive follow-up procedures. The existing explanation correctly identifies this as the right answer.
- ✗
A patient who tests negative but actually has the disease
Why it's wrong here
This case, where the prediction is negative but the true condition is positive, is a false negative: the model fails to detect a real disease. It is a missed diagnosis, and in clinical terms it can delay treatment and put the patient at risk. It contrasts with a false positive because the error is a missed disease, not an incorrect disease call.
- ✗
A patient correctly identified as healthy by the model
Why it's wrong here
When the model classifies a patient as healthy and that patient is in fact disease-free, the prediction agrees with ground truth, so it is a true negative. A false positive specifically requires the model to output the positive (disease) class while the actual condition is negative (healthy). This option therefore describes a correct absence-of-disease verdict, not an erroneous positive alert.
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