This DA0-001 practice question tests your understanding of analyzing and modeling data. 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 analyzing customer feedback sentiment. The dataset is highly imbalanced with 95% positive and 5% negative comments. Which technique should the analyst use to address class imbalance before modeling?
Use accuracy as the evaluation metric
Why wrong: Accuracy is misleading for imbalanced data; F1 or AUC are better.
Undersample the majority class
Why wrong: Undersampling can help but may discard useful data; SMOTE is more effective.
Oversample the majority class
Why wrong: This would increase imbalance further.
Use SMOTE
SMOTE generates synthetic minority samples to balance classes.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Use SMOTE
SMOTE (Synthetic Minority Oversampling Technique) is the correct choice because it generates synthetic samples for the minority class (negative comments) by interpolating between existing minority instances, rather than simply duplicating them. This addresses the 95:5 imbalance without the information loss of undersampling or the overfitting risk of naive oversampling.
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.
Use accuracy as the evaluation metric
Why it's wrong here
Accuracy is misleading for imbalanced data; F1 or AUC are better.
Undersample the majority class
Why it's wrong here
Undersampling can help but may discard useful data; SMOTE is more effective.
Oversample the majority class
Why it's wrong here
This would increase imbalance further.
Use SMOTE
Why this is correct
SMOTE generates synthetic minority samples to balance classes.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
The trap here is that candidates often confuse oversampling the minority class with oversampling the majority class, or they incorrectly assume that simply using a different evaluation metric (like accuracy) can fix the imbalance problem without modifying the dataset.
Detailed technical explanation
SMOTE works by selecting a minority class sample, finding its k-nearest neighbors (typically k=5), and creating synthetic samples along the line segments connecting the sample to its neighbors. This technique helps the model learn more generalizable decision boundaries for the minority class, but it can introduce noise if the minority class is highly clustered or if there are overlapping regions between classes.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
A practitioner preparing for the DA0-001 exam encounters this exact type of scenario on the job. The correct answer here is not the most general option — it is the best answer for the specific constraint described. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Real exam questions reward reading the full scenario before eliminating options, because the constraint defines which answer fits.
What to study next
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FAQ
Analyzing and Modeling Data — This question tests Analyzing and Modeling Data — Read the scenario before looking for a memorised answer..
The correct answer is: Use SMOTE — SMOTE (Synthetic Minority Oversampling Technique) is the correct choice because it generates synthetic samples for the minority class (negative comments) by interpolating between existing minority instances, rather than simply duplicating them. This addresses the 95:5 imbalance without the information loss of undersampling or the overfitting risk of naive oversampling.
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 11, 2026
This DA0-001 practice question is part of Courseiva's free CompTIA 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 DA0-001 exam.
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