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 cases 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 trains a binary classification model to detect spam emails. The dataset contains 95% legitimate emails (negative class) and 5% spam (positive class). The model predicts all emails as legitimate. The accuracy is 95%, but the model is useless. Which metric would best indicate the model's failure?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue: "best"
Why it matters: Signals that multiple options may be partially correct. Choose the option that most directly solves the exact problem described, not the one that sounds most complete.
Why wrong: Precision for the positive class would be undefined (0/0) or 0 if any spam is predicted, but since no positive predictions are made, precision is not meaningful alone.
B
Recall
Recall (sensitivity) for the positive class is 0 because no spam emails are detected, highlighting the model's complete failure to identify the minority class.
C
F1 score
Why wrong: F1 score is the harmonic mean of precision and recall; it would be 0 as well, but recall is the most direct indicator when the model predicts only the majority class.
D
Specificity
Why wrong: Specificity measures true negative rate; it would be 1 (100%), which incorrectly suggests good performance because it ignores the positive class entirely.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Recall
Recall (sensitivity) measures the proportion of actual positive cases correctly identified. With 5% spam and the model predicting all as legitimate, recall is 0% because no spam emails are detected. This directly exposes the model's failure to identify the positive class despite high accuracy.
Key principle: Recall measures the proportion of actual positive cases 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.
✗
Precision
Why it's wrong here
Precision for the positive class would be undefined (0/0) or 0 if any spam is predicted, but since no positive predictions are made, precision is not meaningful alone.
✓
Recall
Why this is correct
Recall (sensitivity) for the positive class is 0 because no spam emails are detected, highlighting the model's complete failure to identify the minority class.
Clue confirmation
The clue word "best" in the question point toward this answer.
Related concept
Recall measures the proportion of actual positive cases correctly identified.
✗
F1 score
Why it's wrong here
F1 score is the harmonic mean of precision and recall; it would be 0 as well, but recall is the most direct indicator when the model predicts only the majority class.
✗
Specificity
Why it's wrong here
Specificity measures true negative rate; it would be 1 (100%), which incorrectly suggests good performance because it ignores the positive class entirely.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates see 95% accuracy and assume the model is good, failing to recognize that accuracy is meaningless for imbalanced classes without evaluating per-class metrics like recall.
Detailed technical explanation
How to think about this question
Recall is defined as TP / (TP + FN). In this scenario, TP = 0 and FN = all actual spam, so recall = 0. Accuracy alone is misleading for imbalanced datasets because it can be high even when the model is trivial (e.g., always predicting the majority class). In Azure Machine Learning, the 'Recall' metric is critical for fraud detection or medical diagnosis where missing a positive case is costly.
KKey Concepts to Remember
Recall measures the proportion of actual positive cases correctly identified.
It is also known as sensitivity or True Positive Rate.
Recall is crucial for imbalanced datasets, especially for the minority class.
A recall of 0 for the positive class indicates the model missed all actual positive instances.
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 cases 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 cases 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 cases 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 cases correctly identified..
What is the correct answer to this question?
The correct answer is: Recall — Recall (sensitivity) measures the proportion of actual positive cases correctly identified. With 5% spam and the model predicting all as legitimate, recall is 0% because no spam emails are detected. This directly exposes the model's failure to identify the positive class despite high accuracy.
What should I do if I get this AI-900 question wrong?
Review recall measures the proportion of actual positive cases correctly identified., then practise related AI-900 questions on the same topic to reinforce the concept.
Are there clue words in this question I should notice?
Yes — watch for: "best". Signals that multiple options may be partially correct. Choose the option that most directly solves the exact problem described, not the one that sounds most complete.
What is the key concept behind this question?
Recall measures the proportion of actual positive cases correctly identified.
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