DA0-002 Data Analysis Practice Question
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?
⚠ Common exam trap
Many exam-takers 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.
Answer choices
Why each option matters
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.
Answer analysis
Option-by-option breakdown
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 reports the proportion of all predictions that are correct, so a model predicting every comment as positive scores 95% while detecting zero negative sentiment. It is tempting because accuracy is the default metric for balanced classification, and would be the right choice when classes are roughly equal.
- ✗
Undersample the majority class
Why it's wrong here
Undersampling discards 90% of the majority-class records, leaving roughly 5% positive and 5% negative, which destroys training volume and the genuine 95:5 prior. It is tempting because it balances classes cheaply, and would suit a scenario where the majority class is enormous and redundant.
- ✗
Oversample the majority class
Why it's wrong here
Oversampling the majority class increases the already-dominant positive examples, pushing the ratio further from balance and worsening the bias toward predicting positive. It is tempting because resampling is the standard remedy for imbalance, and duplicating the minority class instead would be the correct direction.
- ✓
Use SMOTE
Why this is correct
SMOTE generates synthetic minority-class samples by interpolating between existing nearest neighbours, rebalancing the 95:5 split before training. This satisfies the stem's requirement to address class imbalance, letting the model learn negative-class patterns instead of defaulting to the majority class.
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JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This DA0-002 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 DA0-002 exam.