This MLS-C01 practice question tests your understanding of modeling. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. 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 where the positive class represents only 1% of the data. The model achieves 99% accuracy but only identifies 5% of the actual positives. Which metric should the data scientist use to evaluate model performance?
Mean squared error
Why wrong: MSE is for regression problems.
Accuracy
Why wrong: Accuracy is misleading for imbalanced datasets.
Recall
Recall measures the proportion of actual positives correctly identified.
Precision
Why wrong: Precision measures the proportion of predicted positives that are actual 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 by the model. With only 5% of positives detected, recall is 0.05, which directly reveals the model's failure to capture the minority class despite high accuracy. In imbalanced datasets, accuracy is misleading because the model can achieve 99% accuracy by simply predicting the majority class (negative) for all instances.
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.
Mean squared error
Why it's wrong here
MSE is for regression problems.
Accuracy
Why it's wrong here
Accuracy is misleading for imbalanced datasets.
Recall
Why this is correct
Recall measures the proportion of actual positives correctly identified.
Related concept
Read the scenario before looking for a memorised answer.
Precision
Why it's wrong here
Precision measures the proportion of predicted positives that are actual positives.
Common exam traps
Cisco often tests the trap that high accuracy implies good performance on imbalanced datasets, leading candidates to choose accuracy without considering class distribution or the specific failure mode (low recall).
Detailed technical explanation
Recall is calculated as TP / (TP + FN). In this scenario, with only 1% positives, even a model that predicts all negatives yields TP=0, FN=all positives, recall=0. The confusion matrix reveals that high accuracy masks poor recall; for example, with 10,000 samples (100 positives), the model might correctly identify 5 positives (TP=5) and miss 95 (FN=95), giving recall=0.05. In production, low recall can be catastrophic in domains like fraud detection or disease screening where missing positives has high cost.
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 by the model. With only 5% of positives detected, recall is 0.05, which directly reveals the model's failure to capture the minority class despite high accuracy. In imbalanced datasets, accuracy is misleading because the model can achieve 99% accuracy by simply predicting the majority class (negative) for all instances.
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
This MLS-C01 practice question is part of Courseiva's free Amazon Web Services certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the MLS-C01 exam.
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