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 produces poor accuracy on both training and test data because the model is too simple to capture the underlying pattern. Here training accuracy is 99%, which contradicts underfitting. It is tempting because test performance is low, but that gap indicates variance, not bias.
- ✓
Overfitting
Why this is correct
Overfitting occurs when a model learns noise and idiosyncrasies in the training set rather than generalisable patterns, producing the 99% versus 65% gap. The stem's constraint — high training accuracy with poor unseen-data performance — is the defining signature of variance-dominated overfitting, so regularisation, pruning or more data would be required.
- ✗
Multicollinearity
Why it's wrong here
Multicollinearity is correlation among predictor variables that inflates coefficient variance; it does not by itself cause a large train-test accuracy gap. It is tempting because it degrades model reliability, and it would be the concern when interpreting coefficients of highly correlated predictors, not when diagnosing overfitting.
- ✗
High bias
Why it's wrong here
High bias means the model is too simple and underfits, giving low accuracy on training and test sets alike. A 99% training score rules this out. It is tempting because test accuracy is poor, but the large train-test gap points to high variance instead.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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