- A
Underfitting
Why wrong: Underfitting occurs when the model is too simple and performs poorly on both training and test data, not when there is a large disparity between them.
- B
Overfitting
Overfitting happens when the model learns the training data too well, including noise, resulting in high training accuracy but poor test accuracy. The large MAE difference confirms this.
- C
Multicollinearity
Why wrong: Multicollinearity refers to high correlation between independent variables, which can inflate variance in coefficient estimates, but it does not directly cause a large train-test error gap.
- D
Class imbalance
Why wrong: Class imbalance is a problem in classification where one class is significantly more frequent than another, not applicable to regression tasks like predicting house prices.
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 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?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue:
"most likely"Why it matters: Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
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.
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 is too simple and performs poorly on both training and test data, not when there is a large disparity between them.
- ✓
Overfitting
Why this is correct
Overfitting happens when the model learns the training data too well, including noise, resulting in high training accuracy but poor test accuracy. The large MAE difference confirms this.
Clue confirmation
The clue word "most likely" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Multicollinearity
Why it's wrong here
Multicollinearity refers to high correlation between independent variables, which can inflate variance in coefficient estimates, but it does not directly cause a large train-test error gap.
- ✗
Class imbalance
Why it's wrong here
Class imbalance is a problem in classification where one class is significantly more frequent than another, not applicable to regression tasks like predicting house prices.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates 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.
Detailed technical explanation
How to think about this question
Overfitting often results from a model with too many parameters relative to the number of training samples, causing it to memorize noise rather than learn underlying patterns. In Azure Machine Learning, automated machine learning (AutoML) uses cross-validation and early stopping to detect overfitting, and you can apply L1 (Lasso) or L2 (Ridge) regularization to penalize large coefficients. A real-world scenario is using a high-degree polynomial regression on housing data with few samples, which fits training points perfectly but fails on new data due to extreme sensitivity to input variations.
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.
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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 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.
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: "most likely". Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
About these practice questions
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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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