This MLS-C01 practice question tests your understanding of modeling. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. 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 is training a binary classification model on a highly imbalanced dataset (99% negative class, 1% positive class). The model currently achieves 99% accuracy but only identifies 0.5% of true positives. Which metric should the data scientist focus on to improve model performance?
Precision
Why wrong: Precision focuses on false positives, not false negatives; recall is more important here.
Root Mean Squared Error (RMSE)
Why wrong: RMSE is for regression, not classification.
Recall
Recall measures the ability to find all positive samples, which is crucial for imbalanced data.
Accuracy
Why wrong: Accuracy is not suitable for imbalanced datasets as it can be high even if the model fails to predict positives.
Answer choices
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, which is critical when the dataset is highly imbalanced (99% negative, 1% positive) and the model fails to detect most positives (only 0.5% true positives). Improving recall directly addresses the model's inability to capture the minority class, even if it reduces precision or accuracy. In binary classification with severe class imbalance, accuracy is misleading because a model can achieve 99% accuracy by simply predicting the majority class, as seen here.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
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 focuses on false positives, not false negatives; recall is more important here.
Root Mean Squared Error (RMSE)
Why it's wrong here
RMSE is for regression, not classification.
Recall
Why this is correct
Recall measures the ability to find all positive samples, which is crucial for imbalanced data.
Related concept
Read the scenario before looking for a memorised answer.
Accuracy
Why it's wrong here
Accuracy is not suitable for imbalanced datasets as it can be high even if the model fails to predict positives.
Common exam traps
The trap here is that candidates see 99% accuracy and assume the model is performing well, failing to recognize that accuracy is a deceptive metric in imbalanced datasets, while recall directly measures the model's ability to find the rare positive class.
Detailed technical explanation
Recall is calculated as TP / (TP + FN), and in this scenario, with only 0.5% true positives detected, the false negative rate is extremely high (99.5% of positives missed). Under the hood, many classifiers (e.g., logistic regression, SVM) use a decision threshold (default 0.5) that can be tuned to increase recall at the cost of precision, often visualized via a precision-recall curve. In real-world applications like fraud detection or disease screening, a high recall is prioritized to minimize missed positives, even if it means more false alarms.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
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. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. 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
Modeling — This question tests Modeling — Read the scenario before looking for a memorised answer..
The correct answer is: Recall — Recall (sensitivity) measures the proportion of actual positives correctly identified, which is critical when the dataset is highly imbalanced (99% negative, 1% positive) and the model fails to detect most positives (only 0.5% true positives). Improving recall directly addresses the model's inability to capture the minority class, even if it reduces precision or accuracy. In binary classification with severe class imbalance, accuracy is misleading because a model can achieve 99% accuracy by simply predicting the majority class, as seen here.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 24, 2026
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