- A
The proportion of actual positives that the model correctly identified
Why wrong: That is recall (sensitivity) — precision is the proportion of predicted positives that are actually positive.
- B
The proportion of positive predictions that are actually correct
Precision = TP / (TP + FP). It measures how reliable the model's positive predictions are — minimizing false alarms.
- C
The overall proportion of all predictions that are correct
Why wrong: Overall proportion correct is accuracy — precision specifically measures the reliability of positive predictions.
- D
The number of decimal places in the model's confidence score
Why wrong: Decimal places in scores are numerical precision — classification precision is a model evaluation metric about positive prediction correctness.
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. Match the stated requirement to the specific cloud service, access model, or configuration option — many options are valid in isolation but not for this scenario. 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 precision in the context of binary classification model evaluation?
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
The proportion of positive predictions that are actually correct
Precision measures the accuracy of positive predictions: it is the ratio of true positives to the sum of true positives and false positives. Option B correctly defines this as 'the proportion of positive predictions that are actually correct,' which is the standard definition used in Azure Machine Learning's classification metrics.
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.
- ✗
The proportion of actual positives that the model correctly identified
Why it's wrong here
That is recall (sensitivity) — precision is the proportion of predicted positives that are actually positive.
- ✓
The proportion of positive predictions that are actually correct
Why this is correct
Precision = TP / (TP + FP). It measures how reliable the model's positive predictions are — minimizing false alarms.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
The overall proportion of all predictions that are correct
Why it's wrong here
Overall proportion correct is accuracy — precision specifically measures the reliability of positive predictions.
- ✗
The number of decimal places in the model's confidence score
Why it's wrong here
Decimal places in scores are numerical precision — classification precision is a model evaluation metric about positive prediction correctness.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse precision with recall (Option A) because both involve true positives, but precision focuses on the correctness of positive predictions while recall focuses on capturing all actual positives.
Detailed technical explanation
How to think about this question
Precision is critical when the cost of false positives is high, such as in spam detection where incorrectly labeling legitimate email as spam (false positive) is more harmful than missing some spam. Under the hood, precision is calculated as TP / (TP + FP), and Azure Machine Learning's automated ML reports this metric alongside recall and F1-score to help balance trade-offs. A subtle behavior: precision can be misleadingly high if the model predicts very few positives, so it must be interpreted alongside recall.
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
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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: The proportion of positive predictions that are actually correct — Precision measures the accuracy of positive predictions: it is the ratio of true positives to the sum of true positives and false positives. Option B correctly defines this as 'the proportion of positive predictions that are actually correct,' which is the standard definition used in Azure Machine Learning's classification metrics.
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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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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