- A
Overfitting
Overfitting occurs when the model learns the training data too well, capturing noise and making it perform poorly on new, unseen data, as shown by the large gap between training and validation performance.
- B
Underfitting
Why wrong: Underfitting would result in poor performance on both training and validation sets, not a large gap where training is near perfect.
- C
High bias
Why wrong: High bias typically leads to underfitting, where the model cannot capture the underlying patterns, causing low accuracy on both training and validation data.
- D
Insufficient training data
Why wrong: While insufficient data can cause overfitting, the description — very high training performance and much lower validation performance — is a direct symptom of overfitting, not just 'insufficient data' in general.
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 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 machine learning model to predict housing prices. On the training data, the model achieves an R-squared value of 0.99, but on a separate validation dataset it achieves an R-squared of only 0.65. What is the most likely issue with this model?
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 exceptionally well on the training data (R² = 0.99) but poorly on the validation data (R² = 0.65), which is a classic symptom of overfitting. Overfitting occurs when the model learns noise and specific patterns in the training set that do not generalize to unseen data, often due to excessive complexity (e.g., too many features or deep decision trees). In Azure Machine Learning, this can be detected by comparing training and validation metrics in automated ML runs or by using regularization techniques like L1/L2 penalties.
Key principle: Overfitting means a model performs well on training data but poorly on 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
Overfitting occurs when the model learns the training data too well, capturing noise and making it perform poorly on new, unseen data, as shown by the large gap between training and validation performance.
Clue confirmation
The clue word "most likely" in the question point toward this answer.
Related concept
Overfitting means a model performs well on training data but poorly on unseen data.
- ✗
Underfitting
Why it's wrong here
Underfitting would result in poor performance on both training and validation sets, not a large gap where training is near perfect.
- ✗
High bias
Why it's wrong here
High bias typically leads to underfitting, where the model cannot capture the underlying patterns, causing low accuracy on both training and validation data.
- ✗
Insufficient training data
Why it's wrong here
While insufficient data can cause overfitting, the description — very high training performance and much lower validation performance — is a direct symptom of overfitting, not just 'insufficient data' in general.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates may confuse high training accuracy with a good model, overlooking the validation gap, or incorrectly attribute the issue to underfitting or high bias because they focus on the low validation score without considering the training performance.
Detailed technical explanation
How to think about this question
R-squared measures the proportion of variance in the target variable explained by the model; a value of 0.99 on training data suggests the model has memorized the training set almost perfectly, often due to high variance. In Azure ML, automated machine learning uses cross-validation and model explainability to flag such discrepancies, and practitioners can apply techniques like early stopping, pruning, or reducing model complexity to mitigate overfitting. A real-world scenario is predicting house prices with too many polynomial features, where the model fits outliers in the training data but fails on new listings.
KKey Concepts to Remember
- Overfitting means a model performs well on training data but poorly on unseen data.
- It occurs when a model learns noise and specific patterns from the training set.
- A large gap between training and validation accuracy is a key indicator of overfitting.
- Techniques like cross-validation, regularization, and more 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 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 unseen data. 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.
Review overfitting means a model performs well on training data but poorly on unseen data., then practise related AI-900 questions on the same topic to reinforce the concept.
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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 — Overfitting means a model performs well on training data but poorly on unseen data..
What is the correct answer to this question?
The correct answer is: Overfitting — The model performs exceptionally well on the training data (R² = 0.99) but poorly on the validation data (R² = 0.65), which is a classic symptom of overfitting. Overfitting occurs when the model learns noise and specific patterns in the training set that do not generalize to unseen data, often due to excessive complexity (e.g., too many features or deep decision trees). In Azure Machine Learning, this can be detected by comparing training and validation metrics in automated ML runs or by using regularization techniques like L1/L2 penalties.
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 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: "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?
Overfitting means a model performs well on training data but poorly on unseen data.
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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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