AI-900 Practice Question: Describe fundamental principles of machine learning on Azure
A data scientist has trained a binary classification model to predict whether an email is spam (positive) or not spam (negative). On a test set, the model correctly identifies 90 out of 100 actual spam emails and 80 out of 100 actual non-spam emails. Which metric shows the proportion of actual spam emails that the model correctly predicted?
⚠ Common exam trap
Watch out — candidates often confuse recall with precision, mistakenly thinking that 'correctly predicted actual spam' refers to precision, when precision instead answers 'of all emails predicted as spam, how many were actually spam?'
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
✓
B. Recall
Recall (also known as sensitivity or true positive rate) measures the proportion of actual positive cases that were correctly predicted by the model. In this scenario, the model correctly identified 90 out of 100 actual spam emails, so the recall is 90/100 = 0.9 (90%). This metric directly answers the question about how well the model captures actual spam emails.
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. Precision
Why it's wrong here
Precision quantifies the reliability of positive predictions: it divides true positives by the sum of true positives and false positives, yielding 90/(90+20) = 0.818. It focuses on whether predicted spam is actually spam, not on whether actual spam was detected. The question asks about 'proportion of actual spam correctly identified,' which is the definition of recall, not precision.
When this WOULD be correct
Precision would be correct if the question asked: 'Which metric shows the proportion of emails predicted as spam that are actually spam?' or 'Which metric is most important when false positives are costly, such as in a spam filter that must not block important emails?'
- ✓
B. Recall
Why this is correct
Recall, also called sensitivity or true positive rate, directly measures the proportion of actual positive instances that the model successfully identifies. It is computed as true positives divided by the sum of true positives and false negatives, i.e., 90 out of 90+10, which equals 0.9. This precisely matches the question's definition of 'the proportion of actual spam correctly identified.'
- ✗
C. F1 Score
Why it's wrong here
F1 Score is the harmonic mean of precision and recall, and it collapses both metrics into a single composite number. Because it incorporates precision, it cannot isolate the proportion of actual positive cases that were caught; a model can have a high F1 score due to high precision even if its recall is lower. In this scenario, the F1 score would be approximately 0.857, not the direct 0.9 value needed.
When this WOULD be correct
F1 Score would be the correct answer when the question asks for a single metric that balances both precision and recall, especially in cases of imbalanced classes where both false positives and false negatives are equally important.
- ✗
D. Accuracy
Why it's wrong here
Accuracy measures the ratio of all correct predictions (both true positives and true negatives) to the total number of cases, here (90+80)/200 = 0.85. It fails to address the question because it dilutes the model's performance on the positive class with the true negatives, which are irrelevant to identifying actual spam. Even if the model missed 10 spam emails, accuracy can appear high if the negative class is large.
When this WOULD be correct
Accuracy would be the correct answer if the question asked for the overall percentage of correctly classified emails (both spam and non-spam) out of the total test set, without focusing on a specific class.
Option-by-option analysis
Why each answer is right or wrong
Understanding why wrong answers are wrong — and when they would be correct — is what separates a 750 score from a 900. The AI-900 exam frequently reuses these exact scenarios with slightly different constraints.
✓B. RecallCorrect answer▾
Why this is correct
Recall, also called sensitivity or true positive rate, directly measures the proportion of actual positive instances that the model successfully identifies. It is computed as true positives divided by the sum of true positives and false negatives, i.e., 90 out of 90+10, which equals 0.9. This precisely matches the question's definition of 'the proportion of actual spam correctly identified.'
✗A. PrecisionWrong answer — click to see why▾
Why this is wrong here
Precision measures the proportion of predicted spam emails that are actually spam, not the proportion of actual spam emails correctly identified. The question asks for recall (true positive rate).
★ When this WOULD be the correct answer
Precision would be correct if the question asked: 'Which metric shows the proportion of emails predicted as spam that are actually spam?' or 'Which metric is most important when false positives are costly, such as in a spam filter that must not block important emails?'
Why candidates choose this
Candidates often confuse precision and recall, especially when the question wording involves 'correctly predicted' without specifying the denominator (actual vs. predicted positives).
✗C. F1 ScoreWrong answer — click to see why▾
Why this is wrong here
The question asks for the proportion of actual spam emails correctly predicted, which is recall (true positive rate). F1 Score is the harmonic mean of precision and recall, not a direct measure of this proportion.
★ When this WOULD be the correct answer
F1 Score would be the correct answer when the question asks for a single metric that balances both precision and recall, especially in cases of imbalanced classes where both false positives and false negatives are equally important.
Why candidates choose this
Candidates may confuse F1 Score with recall because F1 incorporates recall, or they might think a combined metric is always better without reading the specific requirement for proportion of actual positives.
✗D. AccuracyWrong answer — click to see why▾
Why this is wrong here
Accuracy measures the overall proportion of correct predictions (both spam and non-spam) out of all predictions, not specifically the proportion of actual spam emails correctly identified.
★ When this WOULD be the correct answer
Accuracy would be the correct answer if the question asked for the overall percentage of correctly classified emails (both spam and non-spam) out of the total test set, without focusing on a specific class.
Why candidates choose this
Candidates often default to accuracy as a familiar metric without carefully reading that the question asks for the proportion of actual spam emails correctly predicted, which is recall.
Analysis generated from the official AI-900blueprint and verified against question context. The “when correct” sections are what AI assistants cite when candidates ask “what’s the difference between these options?”
Go deeper
Related to this question
Learn chapter
Regression and Classification
Key term
Model
In IT and AI, a model is a trained mathematical representation that learns patterns from data to make predictions or decisions.
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.
About these practice questions
Courseiva writes every AI-900 question from scratch — 985 in total, each with an explanation and a wrong-answer breakdown. None are copied from real exams or 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.