- A
Increase the learning rate
Why wrong: Learning rate controls the step size during gradient descent. It does not address class imbalance and may even cause the model to diverge if set too high.
- B
Add more features to the model
Why wrong: Adding features does not fix imbalanced data. It may introduce noise and does not change the class distribution.
- C
Use a resampling technique like SMOTE or random oversampling of the minority class
Resampling techniques balance the class distribution by creating synthetic samples (SMOTE) or duplicating existing minority samples (oversampling). This gives the minority class more influence during training, improving model recall for that class.
- D
Use L1 regularization (Lasso)
Why wrong: L1 regularization penalizes the absolute size of coefficients, which helps with feature selection and reduces overfitting. It does not address class imbalance.
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.
A data scientist is training a model to classify customer reviews as positive, negative, or neutral. The dataset contains 10,000 reviews, but only 500 of them are negative. The data scientist wants to ensure the model performs well on the minority class (negative reviews). Which technique should the data scientist consider to address the class imbalance?
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
Use a resampling technique like SMOTE or random oversampling of the minority class
Option C is correct because resampling techniques like SMOTE (Synthetic Minority Oversampling Technique) or random oversampling directly address class imbalance by generating synthetic samples or duplicating existing samples from the minority class (negative reviews). This balances the training dataset, preventing the model from being biased toward the majority class (positive/neutral reviews) and improving recall for the minority class.
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.
- ✗
Increase the learning rate
Why it's wrong here
Learning rate controls the step size during gradient descent. It does not address class imbalance and may even cause the model to diverge if set too high.
- ✗
Add more features to the model
Why it's wrong here
Adding features does not fix imbalanced data. It may introduce noise and does not change the class distribution.
- ✓
Use a resampling technique like SMOTE or random oversampling of the minority class
Why this is correct
Resampling techniques balance the class distribution by creating synthetic samples (SMOTE) or duplicating existing minority samples (oversampling). This gives the minority class more influence during training, improving model recall for that class.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Use L1 regularization (Lasso)
Why it's wrong here
L1 regularization penalizes the absolute size of coefficients, which helps with feature selection and reduces overfitting. It does not address class imbalance.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates may confuse regularization or feature engineering techniques with data-level imbalance solutions, or assume that simply increasing the learning rate can compensate for a skewed dataset.
Detailed technical explanation
How to think about this question
SMOTE works by interpolating between existing minority class samples in feature space, creating synthetic examples that are plausible but not exact duplicates, which helps the model generalize better than simple oversampling. In Azure Machine Learning, the SMOTE module is available in the designer, and for custom training, libraries like imbalanced-learn can be integrated. A real-world scenario is fraud detection, where fraudulent transactions are rare (e.g., 0.1% of data), and resampling is critical to avoid a model that always predicts 'non-fraud' with high accuracy but zero recall.
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 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
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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: Use a resampling technique like SMOTE or random oversampling of the minority class — Option C is correct because resampling techniques like SMOTE (Synthetic Minority Oversampling Technique) or random oversampling directly address class imbalance by generating synthetic samples or duplicating existing samples from the minority class (negative reviews). This balances the training dataset, preventing the model from being biased toward the majority class (positive/neutral reviews) and improving recall for the minority class.
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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