- A
Downsample the majority class to balance the dataset
Why wrong: Downsampling discards data, which can be suboptimal.
- B
Use Synthetic Minority Over-sampling Technique (SMOTE)
SMOTE generates synthetic samples for the minority class.
- C
Use class weights in the loss function to penalize misclassifications of the minority class
Class weights focus the model on the minority class.
- D
Use accuracy as the evaluation metric
Why wrong: Accuracy is misleading for imbalanced data.
- E
Increase the model complexity by adding more layers
Why wrong: Increased complexity may lead to overfitting.
Question 1,309 of 1,755
MLS-C01 Modeling Practice Question
This MLS-C01 practice question tests your understanding of modeling. 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 building a binary classifier to predict customer churn. The dataset is highly imbalanced (5% churn). Which TWO techniques can help improve the model's ability to detect churn?
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 Synthetic Minority Over-sampling Technique (SMOTE)
Option B is correct because SMOTE generates synthetic samples for the minority class by interpolating between existing minority instances, effectively balancing the dataset and providing the model with more diverse churn examples to learn from. This directly addresses the class imbalance problem without losing information from the majority 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.
- ✗
Downsample the majority class to balance the dataset
Why it's wrong here
Downsampling discards data, which can be suboptimal.
- ✓
Use Synthetic Minority Over-sampling Technique (SMOTE)
Why this is correct
SMOTE generates synthetic samples for the minority class.
Related concept
Read the scenario before looking for a memorised answer.
- ✓
Use class weights in the loss function to penalize misclassifications of the minority class
Why this is correct
Class weights focus the model on the minority class.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Use accuracy as the evaluation metric
Why it's wrong here
Accuracy is misleading for imbalanced data.
- ✗
Increase the model complexity by adding more layers
Why it's wrong here
Increased complexity may lead to overfitting.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often assume downsampling (Option A) is always beneficial for imbalance, but it can discard critical majority class patterns, whereas SMOTE and class weights (Options B and C) preserve data while directly targeting the minority class.
Detailed technical explanation
How to think about this question
SMOTE works by selecting a minority class sample, finding its k-nearest neighbors (typically k=5), and creating synthetic samples along the line segments connecting the sample to its neighbors. This technique avoids the overfitting problem of simple oversampling (which duplicates minority samples) and is particularly effective when the minority class is sparse but not completely isolated. In real-world churn prediction, SMOTE is often combined with ensemble methods like Random Forest or XGBoost to further improve recall on the minority 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
Got this wrong? Here's your next step.
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FAQ
Questions learners often ask
What does this MLS-C01 question test?
Modeling — This question tests Modeling — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Use Synthetic Minority Over-sampling Technique (SMOTE) — Option B is correct because SMOTE generates synthetic samples for the minority class by interpolating between existing minority instances, effectively balancing the dataset and providing the model with more diverse churn examples to learn from. This directly addresses the class imbalance problem without losing information from the majority class.
What should I do if I get this MLS-C01 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: Jul 4, 2026
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