- A
Underfitting
Why wrong: Underfitting would result in low accuracy on both training and test datasets, not high training accuracy and lower test accuracy.
- B
Overfitting
Overfitting occurs when the model performs very well on training data but poorly on test data due to memorizing training examples instead of learning general patterns.
- C
High bias
Why wrong: High bias typically causes underfitting, meaning the model oversimplifies and fails to capture even training data patterns, leading to low training accuracy.
- D
High variance
Why wrong: High variance is related to overfitting, but the question asks for the most likely issue. Overfitting is the direct term describing the symptom, while high variance is a cause. In exam context, overfitting is the correct answer.
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 historical sales data to predict future sales volume. The model achieves 99% accuracy on the training dataset but only 75% accuracy on a separate test dataset. What is the most likely issue 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.
Answer choices
Why each option matters
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 99% accuracy on the training set versus 75% on the test set indicates it has memorized the training data, including noise and outliers, rather than learning generalizable patterns. This classic symptom of overfitting occurs when the model is too complex relative to the amount or variability of the training data, causing poor performance on unseen data.
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 datasets, not high training accuracy and lower test accuracy.
- ✓
Overfitting
Why this is correct
Overfitting occurs when the model performs very well on training data but poorly on test data due to memorizing training examples instead of learning general patterns.
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.
- ✗
High bias
Why it's wrong here
High bias typically causes underfitting, meaning the model oversimplifies and fails to capture even training data patterns, leading to low training accuracy.
- ✗
High variance
Why it's wrong here
High variance is related to overfitting, but the question asks for the most likely issue. Overfitting is the direct term describing the symptom, while high variance is a cause. In exam context, overfitting is the correct answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates confuse 'high variance' with 'overfitting' as separate concepts, when in fact high variance is the statistical cause of overfitting, but the exam expects 'overfitting' as the direct answer describing the model's behavior.
Detailed technical explanation
How to think about this question
Overfitting occurs when a model learns the training data's noise and random fluctuations, often due to excessive model capacity (e.g., too many parameters or deep neural network layers). In Azure Machine Learning, automated machine learning (AutoML) mitigates this by applying regularization techniques like L1/L2 penalties, early stopping, and cross-validation to ensure the model generalizes well to new data.
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.
What to study next
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
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FAQ
Questions learners often ask
What does this AI-900 question test?
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 99% accuracy on the training set versus 75% on the test set indicates it has memorized the training data, including noise and outliers, rather than learning generalizable patterns. This classic symptom of overfitting occurs when the model is too complex relative to the amount or variability of the training data, causing poor performance on unseen data.
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.
About these practice questions
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Last reviewed: Jun 11, 2026
This AI-900 practice question is part of Courseiva's free Microsoft certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the AI-900 exam.
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