- A
A technique for collecting more real minority class examples from external data sources
Why wrong: External data acquisition is data collection — SMOTE generates synthetic minority examples from the existing ones through interpolation.
- B
Generating synthetic minority class examples by interpolating between existing examples
SMOTE creates plausible synthetic minority examples — helping classifiers learn from rare classes without just duplicating existing ones.
- C
Removing majority class examples until all classes have equal representation
Why wrong: Removing majority examples is undersampling — SMOTE is an oversampling technique that adds synthetic minority examples.
- D
Setting model confidence thresholds to classify more examples as the minority class
Why wrong: Threshold adjustment is post-processing — SMOTE is a data-level technique generating new training examples.
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. 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.
What is 'imbalanced classification' handling using 'SMOTE'?
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
Generating synthetic minority class examples by interpolating between existing examples
SMOTE (Synthetic Minority Over-sampling Technique) is a data augmentation method that creates synthetic examples for the minority class by interpolating between existing minority class instances. It selects a minority example, finds its k-nearest neighbors from the same class, and generates new samples along the line segments connecting the example to those neighbors. This balances the class distribution without duplicating existing data or discarding majority class examples.
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.
- ✗
A technique for collecting more real minority class examples from external data sources
Why it's wrong here
External data acquisition is data collection — SMOTE generates synthetic minority examples from the existing ones through interpolation.
- ✓
Generating synthetic minority class examples by interpolating between existing examples
Why this is correct
SMOTE creates plausible synthetic minority examples — helping classifiers learn from rare classes without just duplicating existing ones.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Removing majority class examples until all classes have equal representation
Why it's wrong here
Removing majority examples is undersampling — SMOTE is an oversampling technique that adds synthetic minority examples.
- ✗
Setting model confidence thresholds to classify more examples as the minority class
Why it's wrong here
Threshold adjustment is post-processing — SMOTE is a data-level technique generating new training examples.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates confuse SMOTE with undersampling or threshold tuning, but SMOTE is specifically a synthetic oversampling technique that creates new data points, not a method for removing data or adjusting model parameters.
Detailed technical explanation
How to think about this question
SMOTE works by first selecting a minority class sample, then identifying its k nearest neighbors (typically k=5) from the same class. A new synthetic sample is created by taking the difference between the feature vector of the original sample and one of its neighbors, multiplying this difference by a random number between 0 and 1, and adding the result to the original feature vector. This interpolation produces plausible new examples that expand the decision region of the minority class, helping models like logistic regression or neural networks avoid bias toward the majority class.
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
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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: Generating synthetic minority class examples by interpolating between existing examples — SMOTE (Synthetic Minority Over-sampling Technique) is a data augmentation method that creates synthetic examples for the minority class by interpolating between existing minority class instances. It selects a minority example, finds its k-nearest neighbors from the same class, and generates new samples along the line segments connecting the example to those neighbors. This balances the class distribution without duplicating existing data or discarding majority class examples.
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.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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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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