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 dataset for binary classification has a severe class imbalance (5% positive class). Which two data preparation techniques can help address this imbalance? (Choose two.)
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Undersample the majority class
Option D is correct because undersampling the majority class reduces the number of instances from the dominant class, helping to balance the dataset and prevent the model from being biased toward the majority class. This technique is straightforward and can be effective when the majority class has redundant or noisy samples, though it risks losing valuable information.
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.
✗
Remove outliers from the minority class
Why it's wrong here
This would further reduce the minority class, worsening imbalance.
✗
Apply PCA to reduce dimensionality
Why it's wrong here
PCA does not address class imbalance.
✗
Use stratified splitting for train/test sets
Why it's wrong here
Stratified splitting maintains class proportions but does not balance the data itself.
✓
Undersample the majority class
Why this is correct
Reduces majority class size to balance with minority class.
Related concept
Read the scenario before looking for a memorised answer.
✓
Oversample the minority class using SMOTE
Why this is correct
Generates synthetic samples for the minority class.
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 techniques that change the dataset distribution (like undersampling and oversampling) versus those that only affect model training or evaluation (like stratified splitting), leading candidates to mistakenly select stratified splitting as a balancing technique.
Detailed technical explanation
How to think about this question
SMOTE (Synthetic Minority Oversampling Technique) works by interpolating between existing minority class samples to create synthetic instances, rather than simply duplicating them, which helps avoid overfitting. Undersampling can be performed randomly or using methods like Tomek links or Edited Nearest Neighbors to remove majority samples near the decision boundary, improving class separation. In real-world scenarios like fraud detection, combining SMOTE with undersampling (e.g., SMOTEENN) often yields better results than either technique alone.
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: Undersample the majority class — Option D is correct because undersampling the majority class reduces the number of instances from the dominant class, helping to balance the dataset and prevent the model from being biased toward the majority class. This technique is straightforward and can be effective when the majority class has redundant or noisy samples, though it risks losing valuable information.
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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