MLA-C01 Data Preparation for Machine Learning Practice Question
This MLA-C01 practice question tests your understanding of data preparation for machine learning. Compare every option against the stated constraints before choosing — the best answer satisfies all requirements, not just the most obvious one. 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 dataset for binary classification using SageMaker. The dataset has 100 features and 10,000 rows, but the target variable is highly imbalanced (95% negative, 5% positive). Which technique should the data scientist apply during data preparation to address the imbalance?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Apply SMOTE to generate synthetic samples for the minority class
SMOTE (Synthetic Minority Oversampling Technique) is the most appropriate technique because it generates synthetic samples for the minority class by interpolating between existing minority instances, which avoids the overfitting risk of simple duplication (oversampling) and the information loss from undersampling. In SageMaker, SMOTE can be applied during data preparation using libraries like imbalanced-learn before training, or via SageMaker Data Wrangler's built-in transform, making it a robust choice for handling class imbalance without discarding data.
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.
✗
Oversampling the minority class by duplicating examples
Why it's wrong here
Simple duplication can cause overfitting and does not introduce diversity.
✗
Collect more data to match the number of samples in both classes
Why it's wrong here
While collecting more data helps, it is not always practical and does not directly address the existing imbalance.
✗
Random undersampling of the majority class
Why it's wrong here
Undersampling discards potentially useful data and may lead to loss of information.
✓
Apply SMOTE to generate synthetic samples for the minority class
Why this is correct
SMOTE creates synthetic examples along the line segments of minority class nearest neighbors, addressing imbalance.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
AWS often tests the distinction between oversampling by duplication and synthetic oversampling (SMOTE), where candidates mistakenly choose simple duplication (Option A) because they think 'more data is always better,' failing to recognize that SMOTE generates diverse synthetic samples to reduce overfitting.
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, effectively generating new, plausible data points in feature space. In SageMaker, you can implement SMOTE using the imbalanced-learn library within a processing job or a custom training script, but be cautious: SMOTE can introduce noise if the minority class has outliers, and it assumes continuous features, so categorical variables may require encoding or alternative methods like SMOTE-NC.
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.
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: Apply SMOTE to generate synthetic samples for the minority class — SMOTE (Synthetic Minority Oversampling Technique) is the most appropriate technique because it generates synthetic samples for the minority class by interpolating between existing minority instances, which avoids the overfitting risk of simple duplication (oversampling) and the information loss from undersampling. In SageMaker, SMOTE can be applied during data preparation using libraries like imbalanced-learn before training, or via SageMaker Data Wrangler's built-in transform, making it a robust choice for handling class imbalance without discarding data.
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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Question Discussion
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