AI-900 Practice Question: Describe fundamental principles of machine learning on Azure
A data scientist trains a decision tree model to predict customer churn. The model achieves 99% accuracy on the training data but only 80% on the test data. Which concept best explains this performance difference?
⚠ Common exam trap
Many candidates confuse overfitting with underfitting because they see a performance gap, but the key differentiator is that overfitting shows high training accuracy, while underfitting shows low accuracy on both sets.
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 training data (99%) but significantly lower accuracy on test data (80%) indicates that it has memorized the training data rather than learning generalizable patterns. This is the classic symptom of overfitting, where the decision tree captures noise and outliers in the training set, leading to poor performance on 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.
- ✗
Underfitting
Why it's wrong here
Underfitting occurs when the model performs poorly on both training and test data, often because it is too simple to capture the underlying pattern. Here, training accuracy is very high, so underfitting is not the case.
- ✓
Overfitting
Why this is correct
A decision tree that achieves 99% training accuracy but only 80% test accuracy has captured not only the true underlying pattern but also random noise and idiosyncrasies unique to the training set. This is the classic signature of overfitting: the model's hypothesis is too flexible (often because the tree has grown too deep without pruning), so it memorizes the training data instead of learning a generalizable mapping. As a result, its predictions on unseen data degrade substantially, and the gap between training and test performance becomes a direct indicator of this variance.
- ✗
Bias-variance tradeoff
Why it's wrong here
The bias-variance tradeoff describes the balance between underfitting (high bias) and overfitting (high variance). While this scenario is an example of high variance (overfitting), the question asks for the direct concept explaining the performance difference, which is overfitting itself.
- ✗
Cross-validation
Why it's wrong here
Cross-validation is a model evaluation and validation procedure, not an explanation for why training and test accuracies diverge. It systematically splits the dataset into multiple training and validation folds, computing an aggregate performance estimate that helps detect overfitting or guide hyperparameter tuning. In this scenario, applying k-fold cross-validation would likely expose the poor generalization, but the discrepancy itself arises from the model's learning behavior, not from the evaluation method.
Go deeper
Related to this question
Learn chapter
Machine Learning Core Concepts
Key term
Overfitting
Overfitting occurs when a machine learning model learns the training data too well, including its noise and outliers, causing it to perform poorly on new, unseen data.
Key term
Test data
Test data is a set of information used to verify that a software application or system works correctly under various conditions.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This AI-900 practice question is part of Courseiva's free Microsoft 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 AI-900 exam.