The correct answer is to use SMOTE to generate synthetic samples for the minority class. SMOTE, or Synthetic Minority Oversampling Technique, addresses class imbalance by creating new, artificial data points for the minority class through interpolation between existing minority instances, rather than simply duplicating them. This approach avoids the information loss associated with random undersampling of the majority class and directly tackles the core problem of a skewed target distribution. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this scenario tests your understanding of data preprocessing for imbalanced datasets, a common pitfall where candidates might confuse feature scaling or dimensionality reduction with sampling techniques. A frequent trap is choosing undersampling, which can discard valuable data from the 99% majority class. Remember the memory tip: SMOTE "smooths over" the imbalance by synthesizing, not sacrificing.
MLS-C01 Exploratory Data Analysis Practice Question
This MLS-C01 practice question tests your understanding of exploratory data analysis. 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.
During exploratory data analysis on a dataset with 1 million rows, a data scientist notices that the distribution of the target variable is highly imbalanced (99% class A, 1% class B). Which technique should be applied to address this imbalance before model training?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Use SMOTE to generate synthetic samples for the minority class
Option D is correct because SMOTE (Synthetic Minority Oversampling Technique) generates synthetic samples for the minority class, balancing the dataset. Option A is wrong because random undersampling can discard important data. Option B is wrong because scaling does not address imbalance. Option C is wrong because PCA does not fix imbalance.
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.
✗
Randomly undersample the majority class to match the minority class size
Why it's wrong here
Undersampling may lose valuable information from majority class.
✗
Apply standard scaling to all features
Why it's wrong here
Scaling does not affect class imbalance.
✗
Use PCA to reduce dimensionality and oversample in principal component space
Why it's wrong here
PCA does not directly address class imbalance.
✓
Use SMOTE to generate synthetic samples for the minority class
Why this is correct
SMOTE creates synthetic examples to balance classes.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Many certification questions include familiar terms but test a specific constraint. Read the exact wording before choosing an answer that is generally true but wrong for this case.
Detailed technical explanation
How to think about this question
This question should be treated as a scenario, not a definition check. Identify the problem, the constraint and the best action. Then compare each option against those facts.
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.
Use explanations to understand the rule behind the answer.
TExam Day Tips
→Underline the problem statement mentally.
→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 MLS-C01 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
Exploratory Data Analysis — This question tests Exploratory Data Analysis — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Use SMOTE to generate synthetic samples for the minority class — Option D is correct because SMOTE (Synthetic Minority Oversampling Technique) generates synthetic samples for the minority class, balancing the dataset. Option A is wrong because random undersampling can discard important data. Option B is wrong because scaling does not address imbalance. Option C is wrong because PCA does not fix imbalance.
What should I do if I get this MLS-C01 question wrong?
Identify which MLS-C01 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
About these practice questions
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. During exploratory data analysis, a data scientist notices that the target variable is highly imbalanced. Which technique should be used to address this issue before training a classification model?
easy
A.Apply PCA to reduce dimensionality
B.Remove outliers from the majority class
C.Use cross-validation to evaluate the model
D.Apply feature scaling to all features
✓ E.Use SMOTE to generate synthetic samples for the minority class
Why E: SMOTE (Synthetic Minority Over-sampling Technique) is a popular method for handling imbalanced datasets by generating synthetic samples for the minority class. Option A is wrong because removing outliers does not address class imbalance. Option B is wrong because feature scaling does not affect imbalance. Option D is wrong because PCA is for dimensionality reduction, not imbalance. Option E is wrong because cross-validation is a model evaluation technique, not an imbalance solution.
Variation 2. A machine learning engineer is performing exploratory data analysis on a dataset containing customer transactions. They notice that the target variable is highly imbalanced: 99% of samples belong to class 0 and 1% to class 1. Which technique should they use to address this imbalance before training a classification model?
medium
A.Train the model on the raw data without any modification.
✓ B.Apply SMOTE to generate synthetic samples for the minority class.
C.Use accuracy as the evaluation metric and train on the raw data.
D.Under-sample the majority class to match the minority class size.
Why B: Option C is correct because SMOTE generates synthetic samples for the minority class, which is effective for imbalanced datasets. Option A is wrong because accuracy is not a good metric for imbalanced data. Option B is wrong because under-sampling discards majority class data. Option D is wrong because using raw data without handling imbalance typically leads to poor minority class performance.
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This MLS-C01 practice question is part of Courseiva's free Amazon Web Services certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the MLS-C01 exam.
Question Discussion
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