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 1% fraudulent cases. The model predicts 'not fraudulent' for all transactions and achieves 99% accuracy. Which metric would best reveal the model's poor performance on fraud detection?
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 would be 0/0 (undefined) because no frauds were predicted (no true positives or false positives). Even if defined, it would be 0, but recall is more direct.
B
Recall
Recall = true positives / (true positives + false negatives). Since no frauds are caught, recall = 0, exposing the model's failure.
C
F1 score
Why wrong: F1 score is a harmonic mean of precision and recall. With recall = 0, F1 = 0; however, recall itself is the primary indicator in this class-imbalance case.
D
Accuracy
Why wrong: Accuracy is 99% because the model correctly classifies all non-fraudulent transactions, but it completely misses the fraudulent ones. Accuracy hides the problem.
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 (fraudulent transactions) correctly identified by the model. With 1% fraud, a model that predicts 'not fraudulent' for all transactions will have a recall of 0% because it fails to catch any true positives, despite 99% accuracy. This makes recall the best metric to reveal the model's inability to detect 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 0/0 (undefined) because no frauds were predicted (no true positives or false positives). Even if defined, it would be 0, but recall is more direct.
✓
Recall
Why this is correct
Recall = true positives / (true positives + false negatives). Since no frauds are caught, recall = 0, exposing the model's failure.
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 a harmonic mean of precision and recall. With recall = 0, F1 = 0; however, recall itself is the primary indicator in this class-imbalance case.
✗
Accuracy
Why it's wrong here
Accuracy is 99% because the model correctly classifies all non-fraudulent transactions, but it completely misses the fraudulent ones. Accuracy hides the problem.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates see 99% accuracy and assume the model is performing well, failing to recognize that accuracy is a poor metric for imbalanced datasets where the minority class (fraud) is the focus.
Detailed technical explanation
How to think about this question
Recall is defined as TP / (TP + FN), where FN are fraudulent transactions misclassified as legitimate. In this scenario, TP = 0 and FN = all actual fraud cases, yielding recall = 0. This metric is critical in fraud detection because the cost of missing a fraudulent transaction (false negative) is typically much higher than a false alarm (false positive). Azure Machine Learning's automated ML and model evaluation tools emphasize recall for imbalanced classification tasks, often using techniques like SMOTE or class weighting to improve minority class detection.
KKey Concepts to Remember
Recall measures the proportion of actual positive cases correctly identified.
Recall is crucial when the cost of false negatives is high.
A recall of 0 indicates the model failed to detect any positive instances.
Recall is calculated as True Positives / (True Positives + False Negatives).
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 (fraudulent transactions) correctly identified by the model. With 1% fraud, a model that predicts 'not fraudulent' for all transactions will have a recall of 0% because it fails to catch any true positives, despite 99% accuracy. This makes recall the best metric to reveal the model's inability to detect 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.
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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