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 classifier using imbalanced data. Which TWO techniques can help improve model performance on the minority class? (Choose two.)
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Use the F1 score as the evaluation metric.
The F1 score is the harmonic mean of precision and recall, making it a robust evaluation metric for imbalanced datasets because it captures both false positives and false negatives. Unlike accuracy, which can be misleadingly high when the majority class dominates, the F1 score provides a balanced measure of model performance on the minority class.
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.
✗
Undersample the majority class randomly.
Why it's wrong here
Random undersampling may discard useful data.
✗
Use accuracy as the evaluation metric.
Why it's wrong here
Accuracy is misleading for imbalanced data.
✓
Use the F1 score as the evaluation metric.
Why this is correct
F1 score balances precision and recall.
Related concept
Read the scenario before looking for a memorised answer.
✓
Oversample the minority class using SMOTE.
Why this is correct
SMOTE generates synthetic samples for the minority class.
Related concept
Read the scenario before looking for a memorised answer.
✗
Apply L1 regularization to the model.
Why it's wrong here
Regularization does not address class imbalance.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Cisco often tests the misconception that random undersampling is always beneficial for imbalanced data, but candidates must recognize that it can discard useful majority class patterns and that SMOTE or other synthetic oversampling methods are preferred.
Detailed technical explanation
How to think about this question
SMOTE (Synthetic Minority Oversampling Technique) works by interpolating between existing minority class instances to create synthetic samples, rather than simply duplicating them, which helps the model learn more generalizable decision boundaries. The F1 score is particularly sensitive to the minority class because it requires both high precision (few false positives) and high recall (few false negatives), and a low F1 score immediately signals poor minority class performance even if accuracy is high.
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: Use the F1 score as the evaluation metric. — The F1 score is the harmonic mean of precision and recall, making it a robust evaluation metric for imbalanced datasets because it captures both false positives and false negatives. Unlike accuracy, which can be misleadingly high when the majority class dominates, the F1 score provides a balanced measure of model performance on the minority class.
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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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.
Question Discussion
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