AI-900 Practice Question: Describe fundamental principles of machine learning on Azure
A data scientist trains a machine learning model on historical sales data to predict future sales volume. The model achieves 99% accuracy on the training dataset but only 75% accuracy on a separate test dataset. What is the most likely issue with this model?
⚠ Common exam trap
Many exam-takers confuse 'high variance' with 'overfitting' as separate concepts, when in fact high variance is the statistical cause of overfitting, but the exam expects 'overfitting' as the direct answer describing the model's behavior.
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 99% accuracy on the training set versus 75% on the test set indicates it has memorized the training data, including noise and outliers, rather than learning generalizable patterns. This classic symptom of overfitting occurs when the model is too complex relative to the amount or variability of the training data, causing 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 a model is too simplistic to learn the underlying structure in the training data, resulting in low accuracy on both the training set and the test set. Because the scenario reports high training accuracy, the model has clearly fit the training data well, which contradicts the defining symptom of underfitting. Therefore, underfitting cannot explain the observed gap.
- ✓
Overfitting
Why this is correct
Overfitting is the classic failure mode in which a model learns the training data too thoroughly, including its noise and random fluctuations, rather than the generalizable underlying patterns. This yields near-perfect performance on the training set but significantly worse performance on unseen test data, exactly matching the described symptom of high training accuracy and lower test accuracy.
- ✗
High bias
Why it's wrong here
High bias refers to simplifying assumptions embedded in the model that restrict its ability to represent complex relationships, typically leading to systematic prediction errors on the training data itself. In the bias-variance tradeoff, high bias drives underfitting, not overfitting, so it would produce low training accuracy rather than the high training accuracy described. This makes high bias an unlikely explanation for the observed train-test gap.
- ✗
High variance
Why it's wrong here
High variance is related to overfitting, but the question asks for the most likely issue. Overfitting is the direct term describing the symptom, while high variance is a cause. In exam context, overfitting is the correct answer.
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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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.