The answer is underfitting. This is correct because underfitting occurs when a model is too simple to capture the underlying patterns in the data, leading to high training error and similarly high test error. In this linear regression scenario, the model fails to learn the relationship between features and house prices, resulting in poor performance on both sets. On the Microsoft Azure AI Fundamentals AI-900 exam, this concept tests your ability to diagnose model performance issues—a common trap is confusing underfitting with overfitting, where training error is low but test error is high. Remember that underfitting means the model is not learning enough, often due to insufficient complexity or too few features. A helpful memory tip: think of underfitting as the model being “under-trained” or “too simple to fit the data,” so both errors stay high.
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 linear regression model to predict house prices. The model's training error is very high, and its test error is nearly as high. 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.
Underfitting is characterized by a model that does not learn the training data well, leading to high error on both training and test sets.
B
Overfitting
Why wrong: Overfitting would show low training error with high test error, not high error on both.
C
High bias
Why wrong: While underfitting often involves high bias, 'high bias' itself describes a cause rather than the observed condition. The question asks for the term describing the situation, which is underfitting.
D
High variance
Why wrong: High variance is associated with overfitting, where the model is too complex and captures noise, leading to low training error but high test error.
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 training error and similarly high test error. In this linear regression scenario, the model fails to learn the relationship between features and house prices, leading to poor performance on both training and test sets.
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 this is correct
Underfitting is characterized by a model that does not learn the training data well, leading to high error on both training and test sets.
Clue confirmation
The clue word "best" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
✗
Overfitting
Why it's wrong here
Overfitting would show low training error with high test error, not high error on both.
✗
High bias
Why it's wrong here
While underfitting often involves high bias, 'high bias' itself describes a cause rather than the observed condition. The question asks for the term describing the situation, which is underfitting.
✗
High variance
Why it's wrong here
High variance is associated with overfitting, where the model is too complex and captures noise, leading to low training error but high test error.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse 'high bias' with 'underfitting' as the best descriptor, but the question asks for the term that best describes the situation, and 'underfitting' is the direct behavioral term while 'high bias' is a contributing cause.
Trap categories for this question
Command / output trap
Overfitting would show low training error with high test error, not high error on both.
Scenario analysis trap
While underfitting often involves high bias, 'high bias' itself describes a cause rather than the observed condition. The question asks for the term describing the situation, which is underfitting.
Detailed technical explanation
How to think about this question
Underfitting in linear regression typically stems from insufficient model complexity, such as using only a linear term when the true relationship is polynomial. This leads to high bias, where the model's assumptions are too rigid, causing systematic errors in predictions. In Azure Machine Learning, this can be diagnosed by evaluating residuals or using learning curves to see if both training and validation errors plateau at high values.
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: Underfitting — Underfitting occurs when a model is too simple to capture the underlying patterns in the data, resulting in high training error and similarly high test error. In this linear regression scenario, the model fails to learn the relationship between features and house prices, leading to poor performance on both training and test sets.
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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