AI-900 Practice Question: Describe fundamental principles of machine learning on Azure
What is precision in the context of binary classification model evaluation?
⚠ Common exam trap
Watch out — 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.
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.
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
This describes recall (sensitivity), not precision. Recall = true positives / (true positives + false negatives), and it measures the fraction of actual positive cases the model found. Precision instead divides true positives by all positive predictions, so its denominator is predicted positives rather than actual positives.
- ✓
The proportion of positive predictions that are actually correct
Why this is correct
Precision = true positives / (true positives + false positives). It measures how many of the model's positive predictions were actually correct, making it a direct check on false alarms. High precision means that when the model says a result is positive, it is usually right.
- ✗
The overall proportion of all predictions that are correct
Why it's wrong here
This describes accuracy, which equals (true positives + true negatives) / total predictions and reflects overall correctness across all classes. Precision ignores true negatives and only considers positive predictions. Accuracy can be high even when precision is poor, especially with imbalanced classes.
- ✗
The number of decimal places in the model's confidence score
Why it's wrong here
The number of decimal places in a confidence score is numerical precision, a property of how a number is stored or displayed. That has nothing to do with classification precision, which evaluates whether positive predictions are reliable. In model evaluation, precision is a ratio derived from the confusion matrix, not a formatting detail.
Go deeper
Related to this question
Learn chapter
Machine Learning Core Concepts
Key term
Classification
Classification is a supervised machine learning technique used to predict a category or class label for new data based on patterns learned from labeled training data.
Key term
Azure Machine Learning
Azure Machine Learning is a cloud service for building, training, and deploying machine learning models at scale.
About these practice questions
This AI-900 question is part of Courseiva's 985-question bank — original exam-style content with full explanations and wrong-answer analysis, never real exam questions or exam dumps. Learn why practice questions differ from exam dumps →
JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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.