- A
SMOTE (Synthetic Minority Over-sampling Technique)
Generates synthetic samples for the minority class.
- B
Random undersampling of the majority class
Why wrong: Removes potentially valuable data.
- C
Random oversampling of the minority class
Why wrong: Duplicates existing samples, risk of overfitting.
- D
Apply class weights during model training
Why wrong: Affects loss function, not data preparation.
Question 664 of 1,000
MLA-C01 Data Preparation for Machine Learning Practice Question
This MLA-C01 practice question tests your understanding of data preparation for machine learning. 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 preparing a large dataset for training a binary classification model. The dataset has a severe class imbalance (95% negative, 5% positive). Which data preparation technique should the scientist use to address this imbalance without losing too much data?
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
✓
SMOTE (Synthetic Minority Over-sampling Technique)
SMOTE (Synthetic Minority Over-sampling Technique) is the best choice because it generates synthetic examples for the minority class by interpolating between existing minority instances and their k-nearest neighbors, rather than simply duplicating data. This addresses the severe 95:5 class imbalance without losing data (as undersampling would) and without the overfitting risk of naive random oversampling. The synthetic samples help the model learn a more general decision boundary for the positive 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.
- ✓
SMOTE (Synthetic Minority Over-sampling Technique)
Why this is correct
Generates synthetic samples for the minority class.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Random undersampling of the majority class
Why it's wrong here
Removes potentially valuable data.
- ✗
Random oversampling of the minority class
Why it's wrong here
Duplicates existing samples, risk of overfitting.
- ✗
Apply class weights during model training
Why it's wrong here
Affects loss function, not data preparation.
Common exam traps
Common exam trap: answer the scenario, not the keyword
AWS often tests the distinction between data-level techniques (like SMOTE, oversampling, undersampling) and algorithm-level techniques (like class weights), and the trap here is that candidates confuse class weighting as a data preparation method when it is actually a model training adjustment, not a data transformation step.
Detailed technical explanation
How to think about this question
SMOTE works by selecting a minority class instance, finding its k-nearest neighbors (typically k=5), and creating a synthetic sample along the line segment connecting the instance to a randomly chosen neighbor. This interpolation occurs in feature space, so the synthetic points are plausible but not exact copies, which helps the model generalize better. In real-world scenarios like fraud detection or rare disease diagnosis, SMOTE can be combined with undersampling (e.g., SMOTEENN) to clean noisy majority examples, but pure SMOTE is preferred when data loss is unacceptable.
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 MLA-C01 question test?
Data Preparation for Machine Learning — This question tests Data Preparation for Machine Learning — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: SMOTE (Synthetic Minority Over-sampling Technique) — SMOTE (Synthetic Minority Over-sampling Technique) is the best choice because it generates synthetic examples for the minority class by interpolating between existing minority instances and their k-nearest neighbors, rather than simply duplicating data. This addresses the severe 95:5 class imbalance without losing data (as undersampling would) and without the overfitting risk of naive random oversampling. The synthetic samples help the model learn a more general decision boundary for the positive class.
What should I do if I get this MLA-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: Jun 30, 2026
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