The answer is overfitting. This scenario describes overfitting because the regression model has memorized the training data, including its noise and outliers, achieving very low error on that set, but fails to generalize to unseen data, as shown by the high error on the test set. In the context of overfitting vs underfitting regression, this is the classic hallmark: a large gap between training and validation metrics. On the Microsoft Azure AI Fundamentals AI-900 exam, this concept tests your understanding of model evaluation and generalization, often appearing in questions about regression performance or regularization techniques. A common trap is confusing this with underfitting, but remember: underfitting shows high error on both training and test sets. A useful memory tip is to think of overfitting as the model being "too smart for its own good"—it knows the training data by heart but can't handle new examples.
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. Match the stated requirement to the specific cloud service, access model, or configuration option — many options are valid in isolation but not for this scenario. 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 house prices. The model achieves very low error on the training data but significantly higher error on a held-out test set. Which problem does this scenario best describe?
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.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Overfitting
The scenario describes overfitting, where the model learns the training data too well, including noise and outliers, resulting in very low training error but poor generalization to new data. In Azure Machine Learning, this is often detected by comparing training and validation metrics; a large gap indicates overfitting. The correct answer is B.
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 would show high error on both the training and test sets because the model is too simple to capture underlying patterns.
✓
Overfitting
Why this is correct
Correct. Overfitting is characterized by excellent performance on training data but poor performance on new data due to memorization of noise.
Clue confirmation
The clue word "best" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
✗
High bias
Why it's wrong here
High bias typically leads to underfitting, not the pattern of very low training error and high test error.
✗
High variance
Why it's wrong here
High variance is related to overfitting, but the term 'overfitting' is the direct description of the observed performance gap.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates confuse 'high variance' (a statistical property) with the specific problem name 'overfitting', but the question explicitly asks for the problem description, not the underlying cause.
Trap categories for this question
Command / output trap
Underfitting would show high error on both the training and test sets because the model is too simple to capture underlying patterns.
Detailed technical explanation
How to think about this question
Overfitting happens when a model has too many parameters relative to the number of training samples, capturing random fluctuations rather than the underlying distribution. In Azure ML, regularization techniques like L1 (Lasso) or L2 (Ridge) can be applied to penalize large coefficients and reduce overfitting. A real-world example is predicting house prices with a polynomial regression of degree 10 on a small dataset, which fits training points perfectly but fails on unseen homes.
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 scenario describes overfitting, where the model learns the training data too well, including noise and outliers, resulting in very low training error but poor generalization to new data. In Azure Machine Learning, this is often detected by comparing training and validation metrics; a large gap indicates overfitting. The correct answer is B.
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: "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?
Read the scenario before looking for a memorised answer.
About these practice questions
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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. The model performs poorly on both the training data and the test data, showing high error in both sets. Which concept best describes this situation?
medium
A.Overfitting
✓ B.Underfitting
C.Data leakage
D.Feature scaling
Why B: Underfitting occurs when a model is too simple to capture the underlying patterns in the data, resulting in high error on both the training and test sets. In this regression scenario, the model fails to learn the relationship between features and house prices, leading to poor performance across all data splits.
Variation 2. 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?
easy
A.Underfitting
✓ B.Overfitting
C.Multicollinearity
D.Class imbalance
Why B: 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.
Variation 3. 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?
easy
✓ A.Overfitting
B.Underfitting
C.High bias
D.Insufficient training data
Why A: 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.
Variation 4. 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?
hard
A.Underfitting
✓ B.Overfitting
C.Data leakage
D.Multicollinearity
Why B: 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.
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