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 regression model to predict housing prices. The model uses polynomial features up to degree 5. It achieves an R-squared of 0.95 on the training set but only 0.60 on the test set. 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.
Why wrong: Incorrect: Underfitting would show low performance on both training and test sets, not high training performance.
B
Overfitting
Correct: The model performs well on training data but poorly on new data, indicating overfitting.
C
Data leakage
Why wrong: Incorrect: Data leakage would likely show high performance on both sets if test data leaked, but here test performance is low.
D
Multicollinearity
Why wrong: Incorrect: Multicollinearity affects interpretability of coefficients but does not necessarily cause such a large train-test performance gap.
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 exceptionally well on the training data (R-squared 0.95) but poorly on the test data (R-squared 0.60), which is the classic symptom of overfitting. Using polynomial features up to degree 5 introduces high model complexity, causing the model to learn noise and specific patterns in the training set that do not generalize to 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
Incorrect: Underfitting would show low performance on both training and test sets, not high training performance.
✓
Overfitting
Why this is correct
Correct: The model performs well on training data but poorly on new data, indicating overfitting.
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.
✗
Data leakage
Why it's wrong here
Incorrect: Data leakage would likely show high performance on both sets if test data leaked, but here test performance is low.
✗
Multicollinearity
Why it's wrong here
Incorrect: Multicollinearity affects interpretability of coefficients but does not necessarily cause such a large train-test performance gap.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates may confuse overfitting with underfitting, but the key indicator is the large gap between high training performance and low test performance, not uniformly low performance.
Trap categories for this question
Command / output trap
Incorrect: Underfitting would show low performance on both training and test sets, not high training performance.
Detailed technical explanation
How to think about this question
Overfitting occurs when a model's capacity (e.g., polynomial degree) is too high relative to the amount of training data, causing it to fit the training noise. Regularization techniques like Lasso (L1) or Ridge (L2) can penalize large coefficients and reduce overfitting. In Azure Machine Learning, you can use automated hyperparameter tuning with regularization parameters to find the optimal model complexity.
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.
Related glossary terms
Concepts from this question explained
These glossary pages explain the core terms tested in this AI-900 question in full detail.
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 exceptionally well on the training data (R-squared 0.95) but poorly on the test data (R-squared 0.60), which is the classic symptom of overfitting. Using polynomial features up to degree 5 introduces high model complexity, causing the model to learn noise and specific patterns in the training set that do not generalize to 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: "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.
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Question Discussion
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