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 company is building a binary classification model to predict customer churn. The dataset has 10,000 samples with 500 churners (positive class). The data scientist trains a logistic regression model and obtains an accuracy of 95%. However, the model predicts all customers as non-churn. Which metric should the data scientist use to evaluate the model's performance?
Why wrong: AUC-ROC would be 0.5 for a constant model, indicating no discriminative power, but it doesn't directly highlight the failure to detect positives.
B
F1-score
F1-score balances precision and recall; with all negatives predicted, recall is 0, so F1 is 0, clearly showing poor performance on churners.
C
Accuracy
Why wrong: Accuracy is high but misleading because the model predicts all non-churn, giving 95% accuracy while missing all churners.
D
Confusion matrix
Why wrong: A confusion matrix provides detailed breakdown but no single threshold-free metric; the question asks for a metric to evaluate performance.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
F1-score
The F1-score is the harmonic mean of precision and recall, making it robust to class imbalance. Since the model predicts all customers as non-churn (accuracy 95% due to 9500 non-churners), precision for the positive class is undefined (0 true positives) and recall is 0, so the F1-score correctly reveals the model's failure to identify any churners.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
✗
AUC-ROC
Why it's wrong here
AUC-ROC would be 0.5 for a constant model, indicating no discriminative power, but it doesn't directly highlight the failure to detect positives.
✓
F1-score
Why this is correct
F1-score balances precision and recall; with all negatives predicted, recall is 0, so F1 is 0, clearly showing poor performance on churners.
Related concept
Read the scenario before looking for a memorised answer.
✗
Accuracy
Why it's wrong here
Accuracy is high but misleading because the model predicts all non-churn, giving 95% accuracy while missing all churners.
✗
Confusion matrix
Why it's wrong here
A confusion matrix provides detailed breakdown but no single threshold-free metric; the question asks for a metric to evaluate performance.
Common exam traps
Common exam trap: answer the scenario, not the keyword
AWS often tests the trap that candidates choose accuracy because it is high (95%), failing to recognize that accuracy is meaningless in imbalanced datasets when the model predicts only the majority class.
Trap categories for this question
Similar concept trap
A confusion matrix provides detailed breakdown but no single threshold-free metric; the question asks for a metric to evaluate performance.
Detailed technical explanation
How to think about this question
The F1-score is defined as 2 * (precision * recall) / (precision + recall). When there are zero true positives, both precision and recall are 0, leading to an F1-score of 0 (or undefined in some implementations, but typically set to 0). In real-world churn prediction, the cost of missing a churner (false negative) is often high, so the F1-score directly penalizes the model's inability to detect any positive cases, unlike accuracy which is inflated by the majority class.
KKey Concepts to Remember
Read the scenario before looking for a memorised answer.
Find the constraint that changes the correct option.
Eliminate answers that are true in general but not in this case.
TExam Day Tips
→Watch for words such as best, first, most likely and least administrative effort.
→Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
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
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Modeling — This question tests Modeling — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: F1-score — The F1-score is the harmonic mean of precision and recall, making it robust to class imbalance. Since the model predicts all customers as non-churn (accuracy 95% due to 9500 non-churners), precision for the positive class is undefined (0 true positives) and recall is 0, so the F1-score correctly reveals the model's failure to identify any churners.
What should I do if I get this MLS-C01 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Question Discussion
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