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 dataset for training a binary classification model. The dataset has 100,000 rows and 50 features. The target variable is imbalanced, with only 5% positive cases. Which technique should the data scientist apply to address the class imbalance BEFORE training?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Random oversampling of the minority class
Random oversampling of the minority class (Option B) directly addresses the class imbalance by duplicating examples from the positive class until the class distribution is more balanced. This prevents the binary classification model from being biased toward the majority class, which is critical when only 5% of the 100,000 rows are positive cases. Oversampling is applied before training to ensure the model sees sufficient minority examples during learning.
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.
✗
Principal Component Analysis (PCA) dimensionality reduction
Why it's wrong here
PCA reduces feature dimensions but does not address class imbalance.
✓
Random oversampling of the minority class
Why this is correct
Random oversampling is a valid technique to balance classes by replicating minority samples.
Related concept
Read the scenario before looking for a memorised answer.
✗
Standard scaling of numerical features
Why it's wrong here
Scaling normalizes feature ranges but does not change class proportions.
✗
One-hot encoding of categorical variables
Why it's wrong here
One-hot encoding transforms categorical data but does not affect class balance.
Common exam traps
Common exam trap: answer the scenario, not the keyword
AWS often tests whether candidates confuse data preprocessing techniques (scaling, encoding, dimensionality reduction) with methods that directly modify the class distribution, leading them to pick a plausible but irrelevant option like PCA or scaling.
Detailed technical explanation
How to think about this question
Random oversampling works by randomly selecting samples from the minority class with replacement and adding them to the training set, which can lead to overfitting if duplicates dominate. A more robust alternative is SMOTE (Synthetic Minority Oversampling Technique), which generates synthetic examples by interpolating between minority instances, but the question specifically asks for a technique to address imbalance before training, and random oversampling is a valid, straightforward choice. In practice, oversampling should be applied only to the training split after a train-test split to avoid data leakage and inflated evaluation metrics.
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: Random oversampling of the minority class — Random oversampling of the minority class (Option B) directly addresses the class imbalance by duplicating examples from the positive class until the class distribution is more balanced. This prevents the binary classification model from being biased toward the majority class, which is critical when only 5% of the 100,000 rows are positive cases. Oversampling is applied before training to ensure the model sees sufficient minority examples during learning.
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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