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AIF-C01 Practice Question: A machine learning model achieves 99% accuracy on…
A machine learning model achieves 99% accuracy on the training set but only 65% on the test set. Which phenomenon is the model experiencing?
⚠ Common exam trap
Candidates often mistakenly select 'bias-variance tradeoff' because they associate the gap with variance, but the question explicitly asks for the phenomenon, not the underlying tradeoff concept.
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's high accuracy on the training set (99%) but significantly lower accuracy on the test set (65%) indicates that it has memorized the training data, including noise and outliers, rather than learning generalizable patterns. This is the classic symptom of overfitting, where the model performs well on seen data but fails to generalize to unseen data.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Bias-variance tradeoff
Why it's wrong here
The bias-variance tradeoff is a general design principle describing how model complexity affects error, not the specific diagnosis of a 34-point train-test gap. That gap indicates variance from overfitting. The tradeoff concept would be the answer if asked how to balance underfitting against overfitting when tuning model complexity.
- ✗
Data leakage
Why it's wrong here
Data leakage means training data contains information unavailable at prediction time, which can inflate training scores, but leakage is a data-preparation fault, not the named phenomenon describing a large generalisation gap. It would be the answer if the stem showed a feature derived from the target leaking into training.
- ✓
Overfitting
Why this is correct
A large gap between training accuracy (99%) and test accuracy (65%) is the signature of overfitting: the model has memorised training noise and fails to generalise. High variance across datasets, not high bias, explains the poor test performance.
- ✗
Underfitting
Why it's wrong here
Underfitting produces poor accuracy on both training and test sets, since the model is too simple to capture the underlying pattern. Here training accuracy is 99%, so the model has fitted the training data heavily. Underfitting would be the answer if both scores were low, for example 60% training and 58% test.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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