AI-102 Plan and manage an Azure AI solution Practice Question
You are deploying an Azure AI Language service custom text classification model. After training, the model achieves 95% accuracy on the test set but only 60% on a held-out validation set. What is the most likely cause?
⚠ Common exam trap
It's easy for candidates to confuse overfitting with data leakage or label imbalance, but the key diagnostic is the large gap between high test accuracy and low validation accuracy, which uniquely points to overfitting.
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 to the training data
A 95% accuracy on the test set but only 60% on a held-out validation set is a classic sign of overfitting. The model has memorized patterns specific to the training and test sets (which may share distribution or preprocessing artifacts) but fails to generalize to unseen data. In Azure AI Language custom text classification, overfitting often occurs when the model is too complex relative to the amount of training data or when hyperparameters like learning rate or number of epochs are not tuned properly.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Overfitting to the training data
Why this is correct
Overfitting causes high accuracy on training/test sets but poor generalization.
- ✗
Data leakage between training and test sets
Why it's wrong here
Data leakage would likely inflate accuracy on the test set, not cause a large gap.
- ✗
Insufficient training data
Why it's wrong here
Insufficient data would generally result in low accuracy on both sets.
- ✗
Label imbalance in the training data
Why it's wrong here
Label imbalance would affect both sets similarly, not create a large gap.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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