AI-900 Practice Question: Describe fundamental principles of machine learning on Azure
A data scientist trains a machine learning model to predict house prices based on features like square footage, number of bedrooms, and location. The model achieves a very low error on the training data but performs poorly on a held-out test set. Which term best describes this situation?
⚠ Common exam trap
Test-takers frequently confuse 'high variance' (the cause) with 'overfitting' (the observed behavior), but the question asks for the term that best describes the situation, not the underlying statistical property.
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 but poorly on test data, which is the classic symptom of overfitting. Overfitting occurs when the model learns noise and specific patterns in the training set rather than generalizing to unseen data. In Azure Machine Learning, this can be detected by monitoring the gap between training and validation metrics, and mitigated using techniques like regularization or early stopping.
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 is too simplistic to learn the underlying relationships in the training data, so it produces substantial errors on both the training set and the test set. In the described scenario, training metrics are excellent and only test performance is poor, which is the opposite of underfitting's generalized failure. Therefore, underfitting cannot explain the observed train-test gap.
- ✓
Overfitting
Why this is correct
Overfitting means the model has effectively memorized the training examples, including their random noise and idiosyncrasies, rather than learning a generalizable pattern. As a result, it achieves near-perfect training accuracy but performs poorly on unseen test data because the test set does not contain those same noise patterns. This direct training-versus-test performance gap is the classic signature of overfitting.
- ✗
High bias
Why it's wrong here
High bias arises from overly restrictive modeling assumptions, such as using a linear function when the true relationship is nonlinear, and typically leads to underfitting. A model with high bias shows high error on the training set and high error on the test set because it cannot represent the data's structure. The scenario's excellent training performance rules out high bias as the primary cause.
- ✗
High variance
Why it's wrong here
High variance describes how much a model's predictions fluctuate when it is retrained on different samples from the same underlying distribution, and it is conceptually related to overfitting. However, the question asks for the direct label for the symptom where training accuracy is high but test accuracy drops, and that label is overfitting. High variance is the statistical property that can cause overfitting, not the specific name for the observed performance gap.
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
Machine learning
Machine learning is a branch of artificial intelligence where computers learn patterns from data to make decisions or predictions without being explicitly programmed for every task.
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