AI-900 Practice Question: Describe fundamental principles of machine learning on Azure
A data scientist trains a regression model to predict house prices. The model achieves very low error on the training data but significantly higher error on a held-out test set. Which problem does this scenario best describe?
⚠ Common exam trap
Many exam-takers confuse 'high variance' (a statistical property) with the specific problem name 'overfitting', but the question explicitly asks for the problem description, not the underlying cause.
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 scenario describes overfitting, where the model learns the training data too well, including noise and outliers, resulting in very low training error but poor generalization to new data. In Azure Machine Learning, this is often detected by comparing training and validation metrics; a large gap indicates overfitting. The correct answer is B.
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 happens when the model is too simple to capture the underlying patterns in the data, resulting in high error on both the training set and the test set. Because the scenario explicitly states very low error on the training data, underfitting is ruled out—an underfit model would struggle to fit even the examples it was trained on.
- ✓
Overfitting
Why this is correct
Overfitting occurs when a regression model memorizes the training data, including its noise and random fluctuations, rather than learning the underlying relationship. This leads to very low training error but substantially higher error on new, unseen data, which matches the described scenario exactly. The large gap between training and test performance is the hallmark of overfitting.
- ✗
High bias
Why it's wrong here
High bias refers to systematic errors introduced by overly simplistic model assumptions, which typically cause both training and test error to be high (underfitting). The observed pattern of low training error contradicts high bias, since bias would prevent the model from closely fitting the training data in the first place.
- ✗
High variance
Why it's wrong here
High variance describes a model whose predictions are highly sensitive to the particular training sample, meaning small changes in the data lead to very different models. While overfitting is a symptom of high variance, the question asks for the direct description of the performance gap—and "overfitting" is the standard, precise term for low training error combined with high test error, making it the better answer.
Go deeper
Related to this question
Learn chapter
Machine Learning Core Concepts
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.
Key term
Azure Machine Learning
Azure Machine Learning is a cloud service for building, training, and deploying machine learning models at scale.
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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.