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 machine learning model on a dataset of housing prices. The model achieves 98% accuracy on the training data but only 72% accuracy on a separate test set. What is the most likely problem 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.
Why wrong: Underfitting would result in low accuracy on both training and test sets, not high training accuracy with low test accuracy.
B
Overfitting
Overfitting causes the model to memorize training data, leading to high training accuracy but poor generalization to new data.
C
Data leakage
Why wrong: Data leakage typically inflates test performance because the test data inadvertently contains information from the training data, so test accuracy would be higher, not lower.
D
Class imbalance
Why wrong: Class imbalance can affect accuracy, but it would likely cause poor performance on both training and test sets for the minority class, not a large gap between them.
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's high accuracy on training data (98%) but significantly lower accuracy on test data (72%) is a classic symptom of overfitting, where the model learns noise and specific patterns in the training set rather than generalizing to new, unseen data. In Azure Machine Learning, this often occurs when the model is too complex (e.g., deep decision trees or high-degree polynomial features) relative to the amount of training data, and regularization techniques like L1/L2 regularization or early stopping are not applied.
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 result in low accuracy on both training and test sets, not high training accuracy with low test accuracy.
✓
Overfitting
Why this is correct
Overfitting causes the model to memorize training data, leading to high training accuracy but poor generalization to new data.
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
Data leakage typically inflates test performance because the test data inadvertently contains information from the training data, so test accuracy would be higher, not lower.
✗
Class imbalance
Why it's wrong here
Class imbalance can affect accuracy, but it would likely cause poor performance on both training and test sets for the minority class, not a large gap between them.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse high training accuracy with a good model, overlooking the critical test accuracy drop that signals overfitting, and may incorrectly select underfitting because they focus only on the low test score.
Detailed technical explanation
How to think about this question
Overfitting occurs when a model's capacity (e.g., number of parameters or tree depth) exceeds what is necessary to capture the underlying data distribution, causing it to memorize training examples. In Azure ML, automated machine learning (AutoML) uses cross-validation and model complexity penalties to mitigate this, but manual training without such safeguards can easily overfit, especially with small datasets or high-dimensional features. A real-world scenario is using a 100-feature polynomial regression on 200 housing samples, which will fit training data perfectly but fail on new 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 model's high accuracy on training data (98%) but significantly lower accuracy on test data (72%) is a classic symptom of overfitting, where the model learns noise and specific patterns in the training set rather than generalizing to new, unseen data. In Azure Machine Learning, this often occurs when the model is too complex (e.g., deep decision trees or high-degree polynomial features) relative to the amount of training data, and regularization techniques like L1/L2 regularization or early stopping are not applied.
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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