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 to predict house prices based on features like square footage, number of bedrooms, and location. The model achieves a very low error on the training data but performs poorly on a held-out test set. Which term best describes this situation?
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 would result in high error on both training and test sets because the model is too simple to capture the underlying patterns.
B
Overfitting
Overfitting means the model has memorized the training data and does not generalize, leading to excellent training metrics but poor test performance.
C
High bias
Why wrong: High bias typically leads to underfitting, where the model is too simple and has high error on both training and test data, not just the test set.
D
High variance
Why wrong: High variance is related to overfitting, but the term 'overfitting' is the direct description of the training-test performance gap. High variance measures sensitivity to training data fluctuations.
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 training data but poorly on test data, which is the classic symptom of overfitting. Overfitting occurs when the model learns noise and specific patterns in the training set rather than generalizing to unseen data. In Azure Machine Learning, this can be detected by monitoring the gap between training and validation metrics, and mitigated using techniques like regularization or early stopping.
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 high error on both training and test sets because the model is too simple to capture the underlying patterns.
✓
Overfitting
Why this is correct
Overfitting means the model has memorized the training data and does not generalize, leading to excellent training metrics but poor test performance.
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, where the model is too simple and has high error on both training and test data, not just the test set.
✗
High variance
Why it's wrong here
High variance is related to overfitting, but the term 'overfitting' is the direct description of the training-test performance gap. High variance measures sensitivity to training data fluctuations.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates confuse 'high variance' (the cause) with 'overfitting' (the observed behavior), but the question asks for the term that best describes the situation, not the underlying statistical property.
Detailed technical explanation
How to think about this question
Overfitting often results from a model with too many parameters relative to the number of training samples, such as a deep neural network with many layers. In Azure Machine Learning, you can use automated machine learning (AutoML) to apply regularization (e.g., L1/L2 penalties) or cross-validation to detect and reduce overfitting. A real-world scenario is predicting house prices with a polynomial regression of degree 10 on a small dataset, which will memorize outliers and fail on new listings.
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 performs exceptionally well on training data but poorly on test data, which is the classic symptom of overfitting. Overfitting occurs when the model learns noise and specific patterns in the training set rather than generalizing to unseen data. In Azure Machine Learning, this can be detected by monitoring the gap between training and validation metrics, and mitigated using techniques like regularization or early stopping.
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.
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Question Discussion
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