AI-900 Practice Question: Describe fundamental principles of machine learning on Azure
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?
⚠ Common exam trap
Test-takers frequently 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.
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 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.
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 is the ratio of true positives to all positive predictions (TP/(TP+FP)). With no positive predictions at all, TP and FP are both zero, so precision is either undefined (0/0) or reported as zero, not simply low. It answers 'how many flagged cases are actually fraud?', not 'how many fraud cases did we catch?'. Recall directly exposes the complete failure to find fraud, while precision's degenerate value obscures it.
- ✓
Recall
Why this is correct
Recall, or true positive rate, is TP/(TP+FN). Since the model never predicts fraud, TP=0 while FN equals the total number of actual fraud cases, making recall exactly zero. This metric measures the model's ability to find positive examples, so it clearly and unambiguously exposes the model's failure to detect any fraudulent transactions.
- ✗
F1 score
Why it's wrong here
F1 score is the harmonic mean of precision and recall, so it will equal zero if recall is zero. This tells the data scientist only that performance is poor, not why it is poor; low F1 could also result from many false positives or an unbalanced threshold. Recall isolates the central problem—all fraud is missed—making it a more direct diagnostic for this specific failure.
- ✗
Specificity
Why it's wrong here
Specificity is TN/(TN+FP), the true negative rate. Because the model predicts no fraud, every legitimate transaction is correctly classified as non-fraud, yielding high specificity (around 98%). That high value makes the model appear effective even though it completely misses all fraud; specificity only measures how well negative cases are identified and therefore hides the total lack of positive detections.
Go deeper
Related to this question
Learn chapter
Regression and Classification
Key term
Classification
Classification is a supervised machine learning technique used to predict a category or class label for new data based on patterns learned from labeled training data.
Key term
Model
In IT and AI, a model is a trained mathematical representation that learns patterns from data to make predictions or decisions.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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