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 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?
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.
Why wrong: Underfitting occurs when the model has high error on both training and test data, not just on new data.
B
Overfitting
Overfitting means the model captures noise and specifics of the training data, leading to poor generalization to new data, especially from a different distribution.
C
Data leakage
Why wrong: Data leakage involves the model using information from outside the training set to make predictions, which is not indicated here.
D
Bias-variance tradeoff
Why wrong: While overfitting relates to high variance, the bias-variance tradeoff is a broader concept; overfitting is the most direct explanation for the described behavior.
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 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.
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.
✗
Underfitting
Why it's wrong here
Underfitting occurs when the model has high error on both training and test data, not just on new data.
✓
Overfitting
Why this is correct
Overfitting means the model captures noise and specifics of the training data, leading to poor generalization to new data, especially from a different distribution.
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.
✗
Data leakage
Why it's wrong here
Data leakage involves the model using information from outside the training set to make predictions, which is not indicated here.
✗
Bias-variance tradeoff
Why it's wrong here
While overfitting relates to high variance, the bias-variance tradeoff is a broader concept; overfitting is the most direct explanation for the described behavior.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates may confuse overfitting with the bias-variance tradeoff, but the question specifically asks for the concept that best explains the poor performance on new data, which is overfitting, not the general tradeoff.
Detailed technical explanation
How to think about this question
Overfitting often results from a model with too many parameters relative to the amount of training data, causing it to fit the training noise. In regression, this can be detected by comparing training and validation errors; a large gap indicates overfitting. Regularization techniques like L1 (Lasso) or L2 (Ridge) penalize large coefficients to reduce overfitting, and cross-validation helps ensure the model generalizes to unseen data distributions.
KKey Concepts to Remember
Overfitting means a model performs well on training data but poorly on new, unseen data.
It occurs when the model learns noise and specific patterns unique to the training set.
Overfit models have high variance, meaning they are sensitive to small changes in the training data.
Techniques like regularization, cross-validation, and using more diverse data can mitigate overfitting.
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 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.
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.
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