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. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. 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 fraudulent transactions. The dataset contains only 2% fraudulent transactions. The model achieves 98% overall accuracy, but it fails to detect any fraudulent transactions, classifying all transactions as legitimate. Which metric would most clearly reveal this failure?
Why wrong: Precision would be low or undefined because the model predicts no positive cases, but recall more directly shows the inability to find fraud.
B
Recall
Recall (true positive rate) is 0 when no fraudulent transactions are identified, exposing the model's failure.
C
F1 score
Why wrong: F1 score is the harmonic mean of precision and recall; it would be 0 but does not pinpoint the issue as clearly as recall alone.
D
Specificity
Why wrong: Specificity measures true negative rate, which would be high (98%) and thus hide the failure to detect fraud.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Recall
Recall (also known as sensitivity or true positive rate) measures the proportion of actual positive cases (fraudulent transactions) that were correctly identified by the model. In this scenario, the model classifies all transactions as legitimate, so it detects zero fraudulent transactions, yielding a recall of 0%. Despite 98% overall accuracy, the recall metric clearly exposes the model's complete failure to identify any fraud.
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 would be low or undefined because the model predicts no positive cases, but recall more directly shows the inability to find fraud.
✓
Recall
Why this is correct
Recall (true positive rate) is 0 when no fraudulent transactions are identified, exposing the model's failure.
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 but does not pinpoint the issue as clearly as recall alone.
✗
Specificity
Why it's wrong here
Specificity measures true negative rate, which would be high (98%) and thus hide the failure to detect fraud.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often assume high overall accuracy (98%) implies good model performance, failing to recognize that accuracy is a poor metric for imbalanced datasets and that recall is the metric that directly exposes the model's inability to detect the minority class.
Trap categories for this question
Command / output trap
Precision would be low or undefined because the model predicts no positive cases, but recall more directly shows the inability to find fraud.
Detailed technical explanation
How to think about this question
Recall is calculated as TP / (TP + FN). In this imbalanced dataset with only 2% fraud, a naive model that always predicts 'legitimate' achieves high accuracy but has a recall of 0 (TP = 0). Under the hood, accuracy is misleading for rare-event detection because it is dominated by the majority class; recall focuses exclusively on the minority class performance. In real-world fraud detection, a recall of 0% means every fraudulent transaction is missed, leading to catastrophic financial losses, which is why recall is the primary metric for such use cases.
KKey Concepts to Remember
Recall measures the proportion of actual positive cases correctly identified.
It is calculated as True Positives / (True Positives + False Negatives).
Low recall indicates the model misses many actual positive instances.
Recall is crucial for imbalanced datasets where the positive class is rare.
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 (also known as sensitivity or true positive rate) measures the proportion of actual positive cases (fraudulent transactions) that were correctly identified by the model. In this scenario, the model classifies all transactions as legitimate, so it detects zero fraudulent transactions, yielding a recall of 0%. Despite 98% overall accuracy, the recall metric clearly exposes the model's complete failure to identify any fraud.
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.
What is the key concept behind this question?
Recall measures the proportion of actual positive cases correctly identified.
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Question Discussion
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