- A
Oversample the minority class (failure examples)
Correct. Oversampling increases the number of minority class examples in the training set, helping the model learn to identify failures better.
- B
Undersample the majority class (normal examples)
Why wrong: Incorrect. Undersampling reduces the number of majority class examples, which can help but discards potentially useful data and may still not provide enough minority examples.
- C
Use precision as the optimization metric
Why wrong: Incorrect. Changing the evaluation metric does not directly address the data imbalance; the model still needs balanced data to learn from.
- D
Reduce the complexity of the model
Why wrong: Incorrect. Reducing model complexity typically increases bias and underfitting, which would likely worsen performance on the minority class.
Quick Answer
The answer is to oversample the minority class, specifically by duplicating or synthetically generating more failure examples. This data-level technique directly addresses severe class imbalance by balancing the training dataset, allowing the model to learn the patterns of rare failures rather than defaulting to the majority class. On the Microsoft Azure AI Fundamentals AI-900 exam, this scenario tests your understanding of how to handle imbalanced data for binary classification, a common challenge in predictive maintenance and anomaly detection. A frequent trap is assuming that high accuracy (like 99.5%) means a good model, but when the minority class is critical, accuracy is misleading—recall and precision matter more. Remember the memory tip: “When failures are few, oversample to make them new”—this helps you recall that oversampling creates more minority instances, directly enabling the model to meet business requirements like catching 80% of failures.
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. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. A key principle to apply: oversampling increases minority class examples in the training set.. 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 is training a binary classification model to detect rare equipment failures from sensor data. The dataset contains 99.5% normal operation readings and only 0.5% failure readings. The model currently predicts all readings as 'normal' and achieves 99.5% accuracy on the test set. The business requires the model to identify at least 80% of actual failures. Which data-level technique should the data scientist use to most directly address the class imbalance?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue:
"least"Why it matters: You want the option with minimum overhead, fewest steps, or lowest impact — not the most feature-rich or comprehensive answer.
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
Oversample the minority class (failure examples)
Oversampling the minority class (failure examples) directly addresses the severe class imbalance by creating synthetic copies or duplicates of the rare failure instances. This balances the training dataset, allowing the model to learn patterns associated with failures rather than always predicting the majority class. With a balanced dataset, the model can be trained to meet the business requirement of identifying at least 80% of actual failures, even though overall accuracy may decrease.
Key principle: Oversampling increases minority class examples in the training set.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Oversample the minority class (failure examples)
Why this is correct
Correct. Oversampling increases the number of minority class examples in the training set, helping the model learn to identify failures better.
Clue confirmation
The clue word "least" in the question point toward this answer.
Related concept
Oversampling increases minority class examples in the training set.
- ✗
Undersample the majority class (normal examples)
Why it's wrong here
Incorrect. Undersampling reduces the number of majority class examples, which can help but discards potentially useful data and may still not provide enough minority examples.
- ✗
Use precision as the optimization metric
Why it's wrong here
Incorrect. Changing the evaluation metric does not directly address the data imbalance; the model still needs balanced data to learn from.
- ✗
Reduce the complexity of the model
Why it's wrong here
Incorrect. Reducing model complexity typically increases bias and underfitting, which would likely worsen performance on the minority class.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates may think high accuracy (99.5%) is always good, but in imbalanced datasets, accuracy is misleading; the question tests whether you recognize that data-level techniques like oversampling are needed to force the model to learn the minority class, not just optimize metrics or simplify the model.
Detailed technical explanation
How to think about this question
Oversampling techniques like SMOTE (Synthetic Minority Oversampling Technique) create synthetic examples by interpolating between existing minority class instances, which can help the model learn more robust decision boundaries without simply duplicating data. In Azure Machine Learning, the SMOTE module is available in the data transformation pipeline, allowing seamless integration into training workflows. A real-world scenario where this matters is predictive maintenance for jet engines, where failure events are extremely rare but missing them can be catastrophic.
KKey Concepts to Remember
- Oversampling increases minority class examples in the training set.
- It helps models learn patterns for rare events in imbalanced datasets.
- SMOTE is a common oversampling technique that generates synthetic samples.
- Oversampling directly addresses the data distribution problem for better recall.
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
Oversampling increases minority class examples in the training set.
Real-world example
How this comes up in practice
A company's IT admin needs to give a contractor read-only access to production logs without sharing account credentials. Using role-based access control (RBAC) and temporary scoped permissions — not a permanent shared password — is the correct pattern. Questions like this test whether you can apply least-privilege access across cloud identity services.
What to study next
Got this wrong? Here's your next step.
Review oversampling increases minority class examples in the training set., then practise related AI-900 questions on the same topic to reinforce the concept.
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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 — Oversampling increases minority class examples in the training set..
What is the correct answer to this question?
The correct answer is: Oversample the minority class (failure examples) — Oversampling the minority class (failure examples) directly addresses the severe class imbalance by creating synthetic copies or duplicates of the rare failure instances. This balances the training dataset, allowing the model to learn patterns associated with failures rather than always predicting the majority class. With a balanced dataset, the model can be trained to meet the business requirement of identifying at least 80% of actual failures, even though overall accuracy may decrease.
What should I do if I get this AI-900 question wrong?
Review oversampling increases minority class examples in the training set., 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: "least". You want the option with minimum overhead, fewest steps, or lowest impact — not the most feature-rich or comprehensive answer.
What is the key concept behind this question?
Oversampling increases minority class examples in the training set.
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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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