AI0-001 AI Models and Data Engineering Practice Question
A credit risk model is being developed to predict loan defaults. The dataset has 95% non-default and 5% default instances. The data scientist trains a logistic regression model and obtains 95% accuracy, but the recall for defaults is only 10%. Which action is most appropriate to improve the model's ability to identify defaults?
⚠ Common exam trap
CompTIA often tests the misconception that undersampling the majority class is always better than oversampling the minority class, but in this scenario, undersampling would discard valuable non-default patterns and reduce model robustness, whereas SMOTE generates new, realistic default samples without data loss.
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 to oversample the default class
SMOTE (Synthetic Minority Oversampling Technique) is the most appropriate action because it generates synthetic samples for the minority class (defaults) rather than simply duplicating existing ones. This directly addresses the severe class imbalance (95% non-default vs. 5% default) that causes the logistic regression model to achieve high accuracy by predicting nearly all instances as non-default, while failing to identify actual defaults (recall of only 10%). By creating realistic synthetic default instances, SMOTE balances the training data and forces the model to learn decision boundaries that better capture the minority class.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Apply principal component analysis (PCA) to reduce dimensionality
Why it's wrong here
PCA does not address class imbalance.
- ✗
Collect more data from loan applicants to increase dataset size
Why it's wrong here
More data might maintain the same imbalance ratio unless targeted.
- ✗
Undersample the non-default class to match the number of defaults
Why it's wrong here
Undersampling discards many non-default samples, potentially losing useful information.
- ✓
Use SMOTE to oversample the default class
Why this is correct
SMOTE creates synthetic samples, balancing classes and improving recall.
About these practice questions
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JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This AI0-001 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 AI0-001 exam.