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: underfitting occurs when a model is too simple to capture data patterns.. 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 performs poorly on both the training data and the test data, showing high error in both sets. Which concept 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.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Underfitting
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.
Key principle: Underfitting occurs when a model is too simple to capture data patterns.
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 it's wrong here
Incorrect. Overfitting would show low error on training data but high error on test data, not high error on both.
✓
Underfitting
Why this is correct
Correct. Underfitting means the model is too simplistic to learn the data patterns, causing poor performance on both training and test sets.
Clue confirmation
The clue word "best" in the question point toward this answer.
Related concept
Underfitting occurs when a model is too simple to capture data patterns.
✗
Data leakage
Why it's wrong here
Incorrect. Data leakage typically causes unrealistically high performance on test data, not poor performance on both sets.
✗
Feature scaling
Why it's wrong here
Incorrect. Feature scaling is a preprocessing technique that helps model convergence but is not a cause of uniformly high error.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates confuse underfitting with overfitting because both involve poor performance, but the key distinction is that underfitting shows high error on both training and test sets, while overfitting shows low training error and high test error.
Trap categories for this question
Command / output trap
Incorrect. Overfitting would show low error on training data but high error on test data, not high error on both.
Detailed technical explanation
How to think about this question
Underfitting often stems from a model with insufficient capacity, such as a linear regression applied to highly nonlinear data, or from overly aggressive regularization that forces coefficients toward zero. In Azure Machine Learning, this can be diagnosed by comparing training and validation metrics; if both are poor, increasing model complexity (e.g., adding polynomial features or using a more flexible algorithm like Random Forest) is a common remedy.
KKey Concepts to Remember
Underfitting occurs when a model is too simple to capture data patterns.
It results in high error on both training and test datasets.
Common causes include insufficient model complexity or insufficient training.
Solutions involve increasing model complexity or adding more relevant features.
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
Underfitting occurs when a model is too simple to capture data patterns.
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. Underfitting occurs when a model is too simple to capture data patterns. 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 underfitting occurs when a model is too simple to capture data patterns., 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 — Underfitting occurs when a model is too simple to capture data patterns..
What is the correct answer to this question?
The correct answer is: Underfitting — 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.
What should I do if I get this AI-900 question wrong?
Review underfitting occurs when a model is too simple to capture data patterns., 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?
Underfitting occurs when a model is too simple to capture data patterns.
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