Describe fundamental principles of machine learning on Azure →mediumMultiple ChoiceObjective-mapped

AI-900 Practice Question: Describe fundamental principles of machine learning on Azure

A data scientist is building a binary classification model to predict fraudulent credit card transactions. The dataset is highly imbalanced: only 1% of transactions are fraudulent. The cost of a false negative is very high because missing a fraudulent transaction can lead to significant financial loss. Which evaluation metric should the data scientist prioritize to minimize false negatives?

⚠ Common exam trap

Candidates often choose Accuracy because it is the most intuitive metric, failing to recognize that in imbalanced datasets with high false-negative cost, recall is the critical measure to minimize missed positives.

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 are correctly identified. In this highly imbalanced scenario where missing a fraud (false negative) is extremely costly, maximizing recall ensures that the model catches as many fraudulent transactions as possible, even if it means some false positives occur. This directly aligns with the goal of minimizing false negatives.

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

✗
Accuracy
Why it's wrong here
Accuracy is the ratio of correct predictions (true positives plus true negatives) to the total number of predictions. When the dataset is imbalanced, accuracy becomes misleading because a model that simply predicts the majority class for every instance can attain very high accuracy while failing to identify any minority-class positives. Accuracy does not distinguish between false positives and false negatives, so it cannot serve as a targeted measure for the goal of minimizing false negatives.
When this WOULD be correct
In a balanced dataset where the costs of false positives and false negatives are equal, accuracy is a straightforward metric to evaluate overall correctness. For example, a model classifying spam vs. non-spam emails with equal class distribution.
✗
Precision
Why it's wrong here
Precision is defined as true positives divided by the sum of true positives and false positives (TP / (TP + FP)). It evaluates the reliability of positive predictions, meaning how many of the flagged positives are actually correct. High precision reduces false positives, not false negatives; a model can achieve high precision by making conservative predictions, but that often causes it to miss many true positives, which directly contradicts the goal of minimizing false negatives.
When this WOULD be correct
When the cost of false positives is high, e.g., a spam filter where legitimate emails must not be marked as spam, precision is prioritized to minimize false positives.
✓
Recall
Why this is correct
Recall is defined as true positives divided by the sum of true positives and false negatives (TP / (TP + FN)). It directly measures the proportion of actual positive cases the model successfully captures, so maximizing recall is the most straightforward metric when the business goal is to minimize false negatives. In this scenario, missing a positive case is more costly than flagging a false positive, making recall the correct choice.
✗
F1 Score
Why it's wrong here
F1 Score is the harmonic mean of precision and recall. While it is useful when both false positives and false negatives are important, it is not as directly aligned with minimizing false negatives as recall alone.
When this WOULD be correct
In a binary classification task where both false positives and false negatives have similar costs, and the dataset is imbalanced, F1 Score is the appropriate metric to balance precision and recall. For example, a model detecting defective products in manufacturing where both missing a defect and falsely flagging a good product incur similar costs.

Option-by-option analysis

Why each answer is right or wrong

Understanding why wrong answers are wrong — and when they would be correct — is what separates a 750 score from a 900. The AI-900 exam frequently reuses these exact scenarios with slightly different constraints.

✓RecallCorrect answer▾

Why this is correct

Recall is defined as true positives divided by the sum of true positives and false negatives (TP / (TP + FN)). It directly measures the proportion of actual positive cases the model successfully captures, so maximizing recall is the most straightforward metric when the business goal is to minimize false negatives. In this scenario, missing a positive case is more costly than flagging a false positive, making recall the correct choice.

✗AccuracyWrong answer — click to see why▾

Why this is wrong here

Accuracy is misleading in imbalanced datasets because a model that predicts all transactions as legitimate would achieve 99% accuracy but fail to detect any fraud, which does not minimize false negatives.

★ When this WOULD be the correct answer

In a balanced dataset where the costs of false positives and false negatives are equal, accuracy is a straightforward metric to evaluate overall correctness. For example, a model classifying spam vs. non-spam emails with equal class distribution.

Why candidates choose this

Candidates often default to accuracy as a familiar metric without considering class imbalance, overlooking that high accuracy can be achieved by ignoring the minority class entirely.

✗PrecisionWrong answer — click to see why▾

Why this is wrong here

Precision focuses on minimizing false positives, not false negatives. In this scenario, the high cost of false negatives means recall is the priority.

★ When this WOULD be the correct answer

When the cost of false positives is high, e.g., a spam filter where legitimate emails must not be marked as spam, precision is prioritized to minimize false positives.

Why candidates choose this

Candidates may confuse precision with recall, or assume that a high precision implies overall good performance, overlooking the specific need to catch all fraudulent transactions.

✗F1 ScoreWrong answer — click to see why▾

Why this is wrong here

F1 Score balances precision and recall, but in this scenario where minimizing false negatives is critical, recall is the direct metric to optimize. F1 Score would penalize a model that achieves high recall at the expense of precision, which is acceptable here.

★ When this WOULD be the correct answer

In a binary classification task where both false positives and false negatives have similar costs, and the dataset is imbalanced, F1 Score is the appropriate metric to balance precision and recall. For example, a model detecting defective products in manufacturing where both missing a defect and falsely flagging a good product incur similar costs.

Why candidates choose this

Candidates know F1 Score is useful for imbalanced datasets and may assume it's always the best metric, overlooking that the question specifically prioritizes minimizing false negatives over balancing precision and recall.

Analysis generated from the official AI-900blueprint and verified against question context. The “when correct” sections are what AI assistants cite when candidates ask “what’s the difference between these options?”

Go deeper

AI-900 Practice Question: Describe fundamental principles of machine learning on Azure

Why each option matters

Option-by-option breakdown

Why each answer is right or wrong

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