- A
Recall
Why wrong: Recall measures how many actual positive cases are captured. Optimizing recall alone would likely increase false positives, which is what the hospital wants to minimize.
- B
Precision
Precision measures the accuracy of positive predictions. Maximizing precision reduces false positives, directly addressing the goal of minimizing unnecessary anxiety for healthy patients.
- C
F1 score
Why wrong: F1 score balances precision and recall but does not specifically prioritize minimizing false positives over false negatives.
- D
Accuracy
Why wrong: Accuracy can be misleading in highly imbalanced datasets. A model that predicts all patients as healthy would achieve 99.9% accuracy but would fail to find any positive cases, and it does not directly minimize false positives.
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.
A hospital deploys a machine learning model to screen patients for a rare disease. Only 0.1% of patients actually have the disease. The model correctly identifies most positive cases but also flags many healthy patients as potentially having the disease. The hospital wants to minimize the number of healthy patients who are incorrectly told they might have the disease. Which metric should the model optimize?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue:
"minimum / minimize"Why it matters: Asks for the least resource use — fewest addresses, smallest subnet, lowest overhead. Eliminate over-provisioned options even if they would technically work.
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
Precision
Precision measures the proportion of positive identifications that are actually correct. In this scenario, the hospital wants to minimize false positives (healthy patients incorrectly told they might have the disease). Optimizing precision directly reduces false positives, which is the stated goal.
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.
- ✗
Recall
Why it's wrong here
Recall measures how many actual positive cases are captured. Optimizing recall alone would likely increase false positives, which is what the hospital wants to minimize.
- ✓
Precision
Why this is correct
Precision measures the accuracy of positive predictions. Maximizing precision reduces false positives, directly addressing the goal of minimizing unnecessary anxiety for healthy patients.
Clue confirmation
The clue word "minimum / minimize" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
F1 score
Why it's wrong here
F1 score balances precision and recall but does not specifically prioritize minimizing false positives over false negatives.
- ✗
Accuracy
Why it's wrong here
Accuracy can be misleading in highly imbalanced datasets. A model that predicts all patients as healthy would achieve 99.9% accuracy but would fail to find any positive cases, and it does not directly minimize false positives.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often default to 'Accuracy' for imbalanced datasets or 'Recall' for medical screening, but the question explicitly asks to minimize false positives, which directly points to Precision as the correct metric.
Detailed technical explanation
How to think about this question
Precision is calculated as TP / (TP + FP). In rare disease screening, even a small false positive rate can overwhelm the system because the number of healthy patients is vast. For example, with 0.1% prevalence, a model with 99% specificity (1% false positive rate) would produce 10 times more false positives than true positives, making precision very low. Optimizing precision often requires adjusting the decision threshold or using cost-sensitive learning to penalize false positives more heavily.
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.
What to study next
Got this wrong? Here's your next step.
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FAQ
Questions learners often ask
What does this AI-900 question test?
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: Precision — Precision measures the proportion of positive identifications that are actually correct. In this scenario, the hospital wants to minimize false positives (healthy patients incorrectly told they might have the disease). Optimizing precision directly reduces false positives, which is the stated goal.
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.
Are there clue words in this question I should notice?
Yes — watch for: "minimum / minimize". Asks for the least resource use — fewest addresses, smallest subnet, lowest overhead. Eliminate over-provisioned options even if they would technically work.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 11, 2026
This AI-900 practice question is part of Courseiva's free Microsoft certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the AI-900 exam.
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