The answer is overfitting, because the model’s high R-squared on the test set masked its inability to generalize to a new city with different property characteristics. Overfitting occurs when a regression model captures noise or dataset-specific patterns—like city-specific price trends—rather than the true underlying relationship, so it fails when faced with data from a different distribution. On the Microsoft Azure AI Fundamentals AI-900 exam, this scenario tests your understanding of the overfitting vs generalization trade-off, often appearing in questions about model evaluation and deployment pitfalls. A common trap is assuming a high R-squared always means a good model; in reality, it can signal overfitting if the test set is too similar to the training data. Remember the mnemonic: “High R-squared, low real-world—overfitting unfurled.”
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. A key principle to apply: overfitting means a model performs well on training data but poorly on new, unseen data.. 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 house prices using features like bedrooms, square footage, and location. The model achieves an R-squared of 0.95 on the test set. However, when deployed to predict prices in a new city with different property characteristics, the predictions are very inaccurate. Which concept best explains this poor performance?
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.
The model performs well on the original test set but fails on data from a different distribution (new city), which is a classic sign of overfitting.
B
Underfitting
Why wrong: Underfitting would cause poor performance on both training and test sets, which is not the case here.
C
High bias
Why wrong: High bias is associated with underfitting, where the model is too simple to capture patterns. This model shows high variance (overfitting).
D
Data drift
Why wrong: Data drift refers to a gradual change in data distribution over time, but the issue here is immediate poor generalization to a different city, not a temporal change.
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 achieved an R-squared of 0.95 on the test set, indicating excellent performance on data from the same distribution. However, when deployed to a new city with different property characteristics, the predictions were very inaccurate. This is a classic symptom of overfitting, where the model has learned noise and patterns specific to the training data (e.g., city-specific price trends) that do not generalize to unseen data from a different distribution.
Key principle: Overfitting means a model performs well on training data but poorly on new, 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.
✓
Overfitting
Why this is correct
The model performs well on the original test set but fails on data from a different distribution (new city), which is a classic sign of overfitting.
Clue confirmation
The clue word "best" in the question point toward this answer.
Related concept
Overfitting means a model performs well on training data but poorly on new, unseen data.
✗
Underfitting
Why it's wrong here
Underfitting would cause poor performance on both training and test sets, which is not the case here.
✗
High bias
Why it's wrong here
High bias is associated with underfitting, where the model is too simple to capture patterns. This model shows high variance (overfitting).
✗
Data drift
Why it's wrong here
Data drift refers to a gradual change in data distribution over time, but the issue here is immediate poor generalization to a different city, not a temporal change.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates confuse high test-set accuracy with model generalization, failing to recognize that a model can overfit to the test set's distribution and still fail on data from a different domain, which is a core concept of overfitting versus data drift.
Trap categories for this question
Command / output trap
High bias is associated with underfitting, where the model is too simple to capture patterns. This model shows high variance (overfitting).
Detailed technical explanation
How to think about this question
Overfitting occurs when a model captures variance (noise) rather than the underlying signal, often due to excessive model complexity relative to the amount of training data. In regression, this can manifest as high R-squared on training data but poor generalization to out-of-sample data, especially when the new data comes from a different distribution (covariate shift). Techniques like regularization (e.g., L1/L2), cross-validation, and feature selection are used to mitigate overfitting.
KKey Concepts to Remember
Overfitting means a model performs well on training data but poorly on new, unseen data.
It occurs when a model learns noise and specific patterns from the training set.
Overfit models have high variance and low bias.
Symptoms include excellent performance on the test set from the original distribution, but poor generalization.
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
Overfitting means a model performs well on training data but poorly on new, unseen data.
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. Overfitting means a model performs well on training data but poorly on new, unseen data. 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.
Review overfitting means a model performs well on training data but poorly on new, unseen data., then practise related AI-900 questions on the same topic to reinforce the concept.
Describe fundamental principles of machine learning on Azure — This question tests Describe fundamental principles of machine learning on Azure — Overfitting means a model performs well on training data but poorly on new, unseen data..
What is the correct answer to this question?
The correct answer is: Overfitting — The model achieved an R-squared of 0.95 on the test set, indicating excellent performance on data from the same distribution. However, when deployed to a new city with different property characteristics, the predictions were very inaccurate. This is a classic symptom of overfitting, where the model has learned noise and patterns specific to the training data (e.g., city-specific price trends) that do not generalize to unseen data from a different distribution.
What should I do if I get this AI-900 question wrong?
Review overfitting means a model performs well on training data but poorly on new, unseen data., then practise related AI-900 questions on the same topic to reinforce the concept.
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?
Overfitting means a model performs well on training data but poorly on new, unseen data.
About these practice questions
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. A data scientist trains a regression model to predict house prices using features like bedrooms, square footage, and location. The model achieves a low error on the training data but performs significantly worse when used to predict prices in a new city with different property characteristics. Which concept best explains this poor performance?
medium
A.Underfitting
✓ B.Overfitting
C.Data leakage
D.Bias-variance tradeoff
Why B: The model performs well on training data but poorly on new data from a different city, which is the classic symptom of overfitting. Overfitting occurs when a model learns noise and specific patterns in the training data that do not generalize to unseen data, especially when the new data has different characteristics (e.g., different property market dynamics). In this case, the model has memorized the training city's price patterns rather than learning generalizable relationships.
Last reviewed: Jun 11, 2026
Question Discussion
Share a tip, memory trick, or ask about the reasoning behind this question. Do not post real exam questions, leaked content, braindumps, or copyrighted exam material. Comments are moderated and may be removed without notice.
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.
Question Discussion
Share a tip, memory trick, or ask about the reasoning behind this question. Do not post real exam questions, leaked content, braindumps, or copyrighted exam material. Comments are moderated and may be removed without notice.
Sign in to join the discussion.