- A
Underfitting
Why wrong: 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.
- B
Overfitting
Overfitting means the model learns the training data too well, including noise, leading to poor generalization. The large gap between 99% training and 80% test accuracy is a hallmark of overfitting.
- C
Bias-variance tradeoff
Why wrong: 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.
- D
Cross-validation
Why wrong: Cross-validation is a technique to evaluate model performance by splitting data multiple times. It does not explain why training accuracy is higher than test accuracy; it is a method to assess generalization.
AI-900 Practice Question: Describe fundamental principles of machine learning on Azure
This AI-900 practice question tests your understanding of describe fundamental principles of machine learning on azure. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
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?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue:
"best"Why it matters: Signals that multiple options may be partially correct. Choose the option that most directly solves the exact problem described, not the one that sounds most complete.
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.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
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
Overfitting means the model learns the training data too well, including noise, leading to poor generalization. The large gap between 99% training and 80% test accuracy is a hallmark of overfitting.
Clue confirmation
The clue word "best" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
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 technique to evaluate model performance by splitting data multiple times. It does not explain why training accuracy is higher than test accuracy; it is a method to assess generalization.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates may 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.
Trap categories for this question
Scenario analysis trap
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.
Detailed technical explanation
How to think about this question
Decision trees are prone to overfitting when grown too deep, as they can create leaf nodes that correspond to individual training examples. Pruning techniques (e.g., cost-complexity pruning) or setting a minimum number of samples per leaf are common ways to reduce variance. In Azure Machine Learning, the 'Max depth' and 'Minimum leaf samples' hyperparameters directly control this behavior.
KKey Concepts to Remember
- Read the scenario before looking for a memorised answer.
- Find the constraint that changes the correct option.
- Eliminate answers that are true in general but not in this case.
TExam Day Tips
- Watch for words such as best, first, most likely and least administrative effort.
- Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
What to study next
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
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FAQ
Questions learners often ask
What does this AI-900 question test?
Describe fundamental principles of machine learning on Azure — This question tests Describe fundamental principles of machine learning on Azure — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: 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.
What should I do if I get this AI-900 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Are there clue words in this question I should notice?
Yes — watch for: "best". Signals that multiple options may be partially correct. Choose the option that most directly solves the exact problem described, not the one that sounds most complete.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 11, 2026
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.
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