AI-900 Practice Question: Describe fundamental principles of machine learning on Azure
A data scientist is evaluating a binary classification model that predicts whether a transaction is fraudulent. The test set contains 1,000 transactions: 990 legitimate and 10 fraudulent. The model's predictions are shown in the confusion matrix below. Confusion matrix: Predicted Legitimate Predicted Fraudulent Actual Legitimate 942 48 Actual Fraudulent 2 8 Which metric should the data scientist prioritize if the business goal is to minimize the number of fraudulent transactions that are missed (false negatives)?
⚠ Common exam trap
The trap here is that candidates often pick Accuracy because it seems intuitive, but the severe class imbalance (99% legitimate) makes accuracy a poor metric, while Recall directly addresses the business requirement of minimizing missed fraud.
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 (sensitivity) measures the proportion of actual positives correctly identified, calculated as TP/(TP+FN). With 2 false negatives (missed fraudulent transactions), recall is 8/(8+2)=0.80. Minimizing missed fraud directly corresponds to maximizing recall, making it the correct priority for this business goal.
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 = TP/(TP+FP) measures the proportion of predicted fraud cases that are truly fraudulent. It emphasizes minimizing false positives, not false negatives. A model could achieve very high precision by flagging only a few highly suspicious transactions, yet still miss many real frauds, which directly contradicts the goal of catching all fraudulent activity.
- ✓
Recall
Why this is correct
Recall = TP/(TP+FN) measures the proportion of actual fraud cases that are correctly identified. It directly quantifies how many fraudulent transactions the model misses (false negatives). Since the stated goal is to minimize false negatives, recall is the most appropriate evaluation metric because a high recall ensures that few real frauds slip through undetected.
- ✗
Accuracy
Why it's wrong here
Accuracy = (TP+TN)/Total provides an overall percentage of correct predictions but is heavily skewed in imbalanced datasets where the majority class (legitimate transactions) dominates. For example, a model that simply predicts 'not fraud' for every transaction could achieve extremely high accuracy while flagging zero frauds, making it misleading when the real priority is catching all rare positive cases.
- ✗
Specificity
Why it's wrong here
Specificity = TN/(TN+FP) measures the true negative rate, i.e., how accurately the model identifies legitimate transactions. It focuses on minimizing false positives, not false negatives. In fact, a model that never flags anything would have perfect specificity (100%) but would miss all frauds, so it is unsuitable for the goal of capturing fraudulent transactions.
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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