DA0-002 Data Analysis Practice Question
A data analyst trains a complex model that achieves 99% accuracy on training data but only 65% on new data. What is the most likely issue?
⚠ Common exam trap
CompTIA often tests the distinction between overfitting and underfitting by presenting a large gap between training and test accuracy, tempting candidates to choose high bias or multicollinearity due to confusion about bias-variance tradeoff or correlation issues.
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
✓
Overfitting
The model performs exceptionally well on training data (99% accuracy) but poorly on new data (65% accuracy), which is the classic symptom of overfitting. Overfitting occurs when the model learns noise and specific patterns in the training data rather than generalizing to unseen data, often due to excessive complexity (e.g., too many parameters or deep layers). This results in high variance and poor performance on validation or test sets.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Underfitting
Why it's wrong here
Underfitting would show low accuracy on both training and test data.
- ✓
Overfitting
Why this is correct
The model performs well on training but poorly on test data, a classic sign of overfitting.
- ✗
Multicollinearity
Why it's wrong here
Multicollinearity affects interpretation but does not directly cause poor generalization.
- ✗
High bias
Why it's wrong here
High bias leads to underfitting, not a large gap between training and test accuracy.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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