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 a binary classification model. The dataset has 10,000 records with 100 features. The target variable is imbalanced, with 95% negative class and 5% positive class. Which data preparation step should the data scientist take to address the 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
✓
Apply SMOTE to oversample the positive class
Option D is correct because SMOTE (Synthetic Minority Oversampling Technique) generates synthetic samples for the minority class (positive class, 5%) by interpolating between existing minority instances. This addresses the severe class imbalance (95:5) without discarding data, allowing the model to learn decision boundaries for the minority class more effectively than simple duplication.
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.
✗
Normalize all features to a 0-1 range
Why it's wrong here
Normalization does not address class imbalance; it scales features.
✗
Use cross-validation to handle imbalance
Why it's wrong here
Cross-validation is a model evaluation technique, not a data preparation step for imbalance.
✗
Remove enough instances of the negative class to achieve balance
Why it's wrong here
Removing data leads to loss of potentially valuable information and is not recommended.
✓
Apply SMOTE to oversample the positive class
Why this is correct
SMOTE generates synthetic samples for the minority class, effectively balancing the dataset.
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 misconception that any data preprocessing step (like normalization or cross-validation) can fix class imbalance, when in fact only resampling techniques (oversampling, undersampling, or synthetic generation) directly alter the class distribution.
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) among minority samples, and creating a synthetic sample along the line segment connecting the sample to a randomly chosen neighbor. This avoids the overfitting problem of simple oversampling (duplicating existing samples) and is particularly effective when the imbalance is severe, as in this 95:5 ratio. In real-world scenarios like fraud detection or rare disease diagnosis, SMOTE helps the model generalize to unseen minority patterns rather than memorizing duplicates.
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 oversample the positive class — Option D is correct because SMOTE (Synthetic Minority Oversampling Technique) generates synthetic samples for the minority class (positive class, 5%) by interpolating between existing minority instances. This addresses the severe class imbalance (95:5) without discarding data, allowing the model to learn decision boundaries for the minority class more effectively than simple duplication.
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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