The answer is 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—such as disease present—but the actual ground truth is negative, meaning the patient is healthy. This is technically a Type I error, and in medical screening, it represents a healthy individual incorrectly flagged as having the disease, leading to unnecessary follow-up tests and anxiety. On the Microsoft Azure AI Fundamentals AI-900 exam, this concept tests your understanding of classification model evaluation, often appearing in scenario-based questions about healthcare or fraud detection. A common trap is confusing false positives with false negatives; remember that “false” refers to the prediction being wrong, and “positive” refers to what the model predicted. A useful memory tip: a false positive is a “false alarm”—the model cried wolf when there was none.
AI-900 Practice Question: Describe fundamental principles of machine learning on Azure
This AI-900 practice question tests your understanding of describe fundamental principles of machine learning on azure. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
What is a confusion matrix's 'false positive' in medical screening?
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.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
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
That is a true positive — a false positive is predicted positive when actually negative.
✓
A patient predicted to have a disease who is actually healthy
Why this is correct
False positive: model says 'has disease' but patient is healthy — leads to unnecessary anxiety and follow-up procedures.
Related concept
Read the scenario before looking for a memorised answer.
✗
A patient who tests negative but actually has the disease
Why it's wrong here
That is a false negative — a false negative misses an actual case, while a false positive incorrectly flags a healthy patient.
✗
A patient correctly identified as healthy by the model
Why it's wrong here
Correctly identifying a healthy patient is a true negative — a false positive incorrectly predicts disease in a healthy person.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is confusing '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.
Detailed technical explanation
How to think about this question
The confusion matrix is a 2x2 table comparing predicted vs. actual labels: rows represent actual classes, columns represent predicted classes. In Azure Machine Learning, the confusion matrix is computed from the scored probabilities and a chosen threshold (e.g., 0.5); adjusting this threshold changes the trade-off between false positives and false negatives. In medical screening, a high false positive rate can overwhelm healthcare resources with unnecessary confirmatory tests, while a low false positive rate may increase false negatives, risking missed diagnoses.
KKey Concepts to Remember
Read the scenario before looking for a memorised answer.
Find the constraint that changes the correct option.
Eliminate answers that are true in general but not in this case.
TExam Day Tips
→Watch for words such as best, first, most likely and least administrative effort.
→Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
Related glossary terms
Concepts from this question explained
These glossary pages explain the core terms tested in this AI-900 question in full detail.
Describe fundamental principles of machine learning on Azure — This question tests Describe fundamental principles of machine learning on Azure — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: 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.
What should I do if I get this AI-900 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Question Discussion
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