The answer is recall. Recall, also known as sensitivity or the true positive rate, measures the proportion of actual positives that the model correctly identifies, which directly answers the question about how well the model captures actual spam emails. In the given scenario, with 90 out of 100 actual spam emails correctly predicted, recall is 90%, while precision would instead measure how many of the predicted spam emails were actually spam. On the Microsoft Azure AI Fundamentals AI-900 exam, this distinction is a common trap: the question often describes a scenario where you must choose between recall and precision, and the key is to look for keywords like “actual positives” (recall) versus “predicted positives” (precision). A helpful memory tip is to think of “recall” as “retrieving all the real positives from the dataset”—if you can recall all the spam, you have high recall.
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. A key principle to apply: recall measures the proportion of actual positive instances correctly identified.. 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 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?
Why wrong: Precision is true positives / (true positives + false positives). It quantifies how many positive predictions were actually correct, not how many actual positives were caught.
B
B. Recall
Correct. Recall = true positives / (true positives + false negatives) = 90 / (90 + 10) = 0.9, exactly the proportion of actual spam correctly identified.
C
C. F1 Score
Why wrong: F1 Score is the harmonic mean of precision and recall, combining both into a single metric; it does not directly represent the proportion of actual positives correctly identified.
D
D. Accuracy
Why wrong: Accuracy = (true positives + true negatives) / total = (90 + 80) / 200 = 0.85. It measures overall correctness but does not isolate performance on the positive class.
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.
Key principle: Recall measures the proportion of actual positive instances correctly identified.
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 is true positives / (true positives + false positives). It quantifies how many positive predictions were actually correct, not how many actual positives were caught.
✓
B. Recall
Why this is correct
Correct. Recall = true positives / (true positives + false negatives) = 90 / (90 + 10) = 0.9, exactly the proportion of actual spam correctly identified.
Related concept
Recall measures the proportion of actual positive instances correctly identified.
✗
C. F1 Score
Why it's wrong here
F1 Score is the harmonic mean of precision and recall, combining both into a single metric; it does not directly represent the proportion of actual positives correctly identified.
✗
D. Accuracy
Why it's wrong here
Accuracy = (true positives + true negatives) / total = (90 + 80) / 200 = 0.85. It measures overall correctness but does not isolate performance on the positive class.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that 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?'
Detailed technical explanation
How to think about this question
Recall is defined as TP / (TP + FN), where TP = true positives (correctly predicted spam) and FN = false negatives (spam incorrectly predicted as non-spam). In this test set, FN = 10 (100 actual spam - 90 correctly identified). Under the hood, recall is critical in scenarios where missing a positive case has high cost, such as spam filtering (missing a spam email) or medical diagnosis (missing a disease). The trade-off between recall and precision is often managed via the F1 score or by adjusting the classification threshold.
KKey Concepts to Remember
Recall measures the proportion of actual positive instances correctly identified.
It is calculated as True Positives / (True Positives + False Negatives).
High recall indicates a low rate of false negatives (missed positives).
Recall is crucial when the cost of missing a positive instance is high.
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
Recall measures the proportion of actual positive instances correctly identified.
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. Recall measures the proportion of actual positive instances correctly identified. 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.
Review recall measures the proportion of actual positive instances correctly identified., then practise related AI-900 questions on the same topic to reinforce the concept.
Describe fundamental principles of machine learning on Azure — This question tests Describe fundamental principles of machine learning on Azure — Recall measures the proportion of actual positive instances correctly identified..
What is the correct answer to this question?
The correct answer is: 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.
What should I do if I get this AI-900 question wrong?
Review recall measures the proportion of actual positive instances correctly identified., then practise related AI-900 questions on the same topic to reinforce the concept.
What is the key concept behind this question?
Recall measures the proportion of actual positive instances correctly identified.
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