- A
Calculating the vocabulary size
Why wrong: Vocabulary size is not critical for the initial EDA.
- B
Creating a word cloud
Why wrong: Word cloud is a visualization, not a critical EDA step.
- C
Removing stop words
Why wrong: Stop word removal is preprocessing, not EDA.
- D
Checking the distribution of sentiment labels
Class imbalance can significantly impact model performance.
Question 804 of 1,755
EDA for Sentiment Analysis: Critical Step of Checking Class Distribution
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. A key principle to apply: class Imbalance. 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 working with a dataset containing text reviews. The goal is to build a sentiment analysis model. Which EDA step is most critical before feature extraction?
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Checking the distribution of sentiment labels
Checking the distribution of sentiment labels is critical before feature extraction because it reveals class imbalance, which can bias the model towards the majority class and affect evaluation metrics. This EDA step enables informed decisions about resampling or weighting techniques. Option A (vocabulary size) is not a critical first step; option B (word cloud) is a visualization tool, not essential; option C (removing stop words) is a preprocessing step, not part of EDA.
Key principle: Class Imbalance
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Calculating the vocabulary size
Why it's wrong here
Vocabulary size is not critical for the initial EDA.
- ✗
Creating a word cloud
Why it's wrong here
Word cloud is a visualization, not a critical EDA step.
- ✗
Removing stop words
Why it's wrong here
Stop word removal is preprocessing, not EDA.
- ✓
Checking the distribution of sentiment labels
Why this is correct
Class imbalance can significantly impact model performance.
Related concept
Class Imbalance
Common exam traps
Common exam trap: answer the scenario, not the keyword
A common pitfall is jumping directly into text preprocessing (stop word removal, tokenization) without first examining the label distribution, which can lead to biased models and misleading accuracy metrics.
Detailed technical explanation
How to think about this question
Treat this as a scenario question. Identify the problem, the constraint, and the best action. Then compare each option against those facts.
KKey Concepts to Remember
- Class Imbalance
- Exploratory Data Analysis (EDA)
- Sentiment Analysis
- Feature Extraction
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
Class Imbalance
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. Class Imbalance 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.
Review class Imbalance, then practise related MLS-C01 questions on the same topic to reinforce the concept.
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Exploratory Data Analysis — study guide chapter
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FAQ
Questions learners often ask
What does this MLS-C01 question test?
Exploratory Data Analysis — This question tests Exploratory Data Analysis — Class Imbalance.
What is the correct answer to this question?
The correct answer is: Checking the distribution of sentiment labels — Checking the distribution of sentiment labels is critical before feature extraction because it reveals class imbalance, which can bias the model towards the majority class and affect evaluation metrics. This EDA step enables informed decisions about resampling or weighting techniques. Option A (vocabulary size) is not a critical first step; option B (word cloud) is a visualization tool, not essential; option C (removing stop words) is a preprocessing step, not part of EDA.
What should I do if I get this MLS-C01 question wrong?
Review class Imbalance, then practise related MLS-C01 questions on the same topic to reinforce the concept.
What is the key concept behind this question?
Class Imbalance
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Last reviewed: Jun 20, 2026
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