- A
Precision
Why wrong: Precision measures how many of the predicted fraudulent transactions are actually fraudulent. Since the model predicts zero fraud, precision is undefined (or 0/0), making it uninformative.
- B
Recall
Recall measures the fraction of actual fraudulent transactions that are correctly detected. The model catches none, so recall is 0, clearly showing the model's failure.
- C
F1 score
Why wrong: The F1 score is the harmonic mean of precision and recall. With recall at 0, the F1 score would also be 0, but recall is the more direct indicator because it specifically measures the model's ability to find positive cases.
- D
Mean Absolute Error (MAE)
Why wrong: MAE is a metric for regression tasks (predicting continuous values), not for binary classification, so it is not applicable here.
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 credit card transactions. The dataset contains 99.5% legitimate transactions and 0.5% fraudulent transactions. The model predicts every transaction as legitimate and achieves 99.5% accuracy on the test set. Which metric would best reveal that the model is failing to identify any fraudulent transactions?
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.
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
Recall
Recall (also known as sensitivity) measures the proportion of actual positive cases correctly identified by the model. In this scenario, the model predicts all transactions as legitimate, so it correctly identifies zero fraudulent transactions, giving a recall of 0%. Accuracy alone is misleading because the dataset is highly imbalanced (99.5% legitimate, 0.5% fraudulent), and a 99.5% accuracy can be achieved by simply predicting the majority class. Recall directly reveals the model's failure to detect 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 measures how many of the predicted fraudulent transactions are actually fraudulent. Since the model predicts zero fraud, precision is undefined (or 0/0), making it uninformative.
- ✓
Recall
Why this is correct
Recall measures the fraction of actual fraudulent transactions that are correctly detected. The model catches none, so recall is 0, clearly showing 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
The F1 score is the harmonic mean of precision and recall. With recall at 0, the F1 score would also be 0, but recall is the more direct indicator because it specifically measures the model's ability to find positive cases.
- ✗
Mean Absolute Error (MAE)
Why it's wrong here
MAE is a metric for regression tasks (predicting continuous values), not for binary classification, so it is not applicable here.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates see 99.5% accuracy and assume the model is performing well, failing to recognize that accuracy is a poor metric for imbalanced datasets and that recall specifically measures the model's ability to find positive cases (fraud).
Detailed technical explanation
How to think about this question
Recall is calculated as TP / (TP + FN), where TP is true positives and FN is false negatives. In this imbalanced dataset, the model's false negative rate is 100% (all 0.5% fraud cases are missed), making recall 0. In Azure Machine Learning, when evaluating classification models on imbalanced data, the 'Recall' metric is critical; the 'accuracy' metric can be misleading because a model that always predicts the majority class can achieve high accuracy. Real-world fraud detection systems often use recall as a key performance indicator to ensure that as many fraudulent transactions as possible are flagged, even at the cost of some false positives.
KKey Concepts to Remember
- Recall measures the proportion of actual positive cases correctly identified.
- High recall is crucial when the cost of false negatives (missing actual positives) is high.
- Recall is calculated as True Positives / (True Positives + False Negatives).
- In imbalanced datasets, recall for the minority class is a strong indicator of model performance.
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.
What to study next
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FAQ
Questions learners often ask
What does this AI-900 question test?
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) measures the proportion of actual positive cases correctly identified by the model. In this scenario, the model predicts all transactions as legitimate, so it correctly identifies zero fraudulent transactions, giving a recall of 0%. Accuracy alone is misleading because the dataset is highly imbalanced (99.5% legitimate, 0.5% fraudulent), and a 99.5% accuracy can be achieved by simply predicting the majority class. Recall directly reveals the model's failure to detect 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.
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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Last reviewed: Jun 11, 2026
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