Describe fundamental principles of machine learning on Azure →easyMultiple ChoiceObjective-mapped

AI-900 Practice Question: Describe fundamental principles of machine learning on Azure

A data scientist trains a model to predict house prices using features like number of bedrooms, square footage, and location. The model achieves a mean absolute error (MAE) of $5,000 on the training data but $25,000 on the test data. Which problem is the model most likely experiencing?

⚠ Common exam trap

Test-takers frequently confuse overfitting with underfitting because they see a low training error, but the key is the large gap between training and test error, which is the hallmark of overfitting, not underfitting.

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 well on training data (MAE $5,000) but poorly on test data (MAE $25,000), which is the classic symptom of overfitting. Overfitting occurs when the model learns noise and specific patterns in the training data too well, failing to generalize to unseen data. In Azure Machine Learning, this can be detected by comparing training vs. validation metrics and is often mitigated using regularization techniques or simplifying the model.

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 a model is too simplistic to capture the underlying patterns in the training data, leading to high error on both the training and test sets. In a house-price regression, an underfit model would show large mean absolute error (MAE) on training data as well, not just on test data. The described scenario of a large disparity between training and test MAE specifically indicates the model memorized training noise rather than failing to learn it.
✓
Overfitting
Why this is correct
Overfitting happens when a model fits the training data too closely, capturing random noise and idiosyncrasies that do not generalize to unseen data. For house-price prediction, an overfit model might achieve near-perfect MAE on the training set but produce a much higher MAE on the test set because it has effectively memorized the training examples rather than learning the true price determinants. This large train-test performance gap is the hallmark symptom of overfitting.
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Multicollinearity
Why it's wrong here
Multicollinearity is a statistical condition where independent variables in a regression model are highly correlated with each other, which can inflate the variance of coefficient estimates and make them unstable. However, multicollinearity does not inherently cause a large difference between training and test errors; it primarily affects interpretability and the reliability of individual predictors, not the model's ability to generalize. In fact, predictions can still be accurate even with multicollinearity, so it cannot explain the observed MAE gap in a house-price model.
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Class imbalance
Why it's wrong here
Class imbalance refers to a classification problem in which the target variable has a highly skewed distribution, such as 95% 'no purchase' and 5% 'purchase' examples. It does not apply to regression tasks like predicting house prices, where the target is a continuous numeric value rather than discrete classes. Even if a dataset contained mostly lower-priced homes, that would be an issue of target distribution, not class imbalance, and it would not directly produce a large training-versus-test MAE gap.

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AI-900 Practice Question: Describe fundamental principles of machine learning on Azure

Why each option matters

Option-by-option breakdown

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