The correct action is to remove the stop words from the text before creating the bag-of-words representation. This improves feature representation for sentiment analysis because stop words like 'the', 'and', and 'a' carry little to no semantic weight regarding sentiment, and their high frequency can drown out the signal from content words that actually express positive or negative emotion. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this concept tests your understanding of text preprocessing for feature engineering, often appearing in scenario-based questions where you must choose the step that reduces noise in a bag-of-words or TF-IDF model. A common trap is confusing stop word removal with stemming or n-grams—stemming only reduces word roots without filtering common words, and n-grams still include stop words unless explicitly removed. Remember the memory tip: “Stop words stop the signal—remove them to let sentiment speak.”
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.
A data scientist is working with a dataset containing text reviews. The goal is to classify sentiment. During EDA, they compute the word frequency distribution. They notice that the most frequent words are common stop words like 'the', 'and', 'a'. Which action should they take to improve the feature representation for modeling?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Remove the stop words from the text before creating the bag-of-words representation.
Option B is correct because removing stop words focuses on content words that carry sentiment. Option A is wrong because adding more stop words would remove even more potentially useful words. Option C is wrong because stemming reduces words to root forms but does not address stop words. Option D is wrong because n-grams capture phrases but still include stop words.
Key principle: NAT direction and interface roles matter as much as the IP address mapping. Inside/outside designation controls which traffic is translated.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
✗
Use n-grams instead of unigrams to capture phrase patterns.
Why it's wrong here
N-grams include stop words in phrases, still noisy.
✗
Add more stop words to the default list to remove even more common words.
Why it's wrong here
Removing too many words may discard sentiment-bearing words like 'not'.
✓
Remove the stop words from the text before creating the bag-of-words representation.
Why this is correct
Stop words are usually not informative for sentiment; removing them reduces noise.
Related concept
Static NAT maps one inside address to one outside address.
✗
Apply stemming to reduce words to their root forms.
Why it's wrong here
Stemming does not remove stop words.
Common exam traps
Common exam trap: NAT rules depend on direction and matching traffic
NAT is not only about the public address. The inside/outside interface roles and the ACL or rule that matches traffic are just as important.
Trap categories for this question
Keyword trap
N-grams include stop words in phrases, still noisy.
Detailed technical explanation
How to think about this question
NAT questions usually test address translation, overload/PAT behaviour, static mappings and whether the right traffic is being translated. Read the interface direction and address terms carefully.
KKey Concepts to Remember
Static NAT maps one inside address to one outside address.
PAT allows many inside hosts to share one public address using ports.
Inside local and inside global describe the private and translated addresses.
NAT ACLs identify traffic for translation, not always security filtering.
TExam Day Tips
→Identify inside and outside interfaces first.
→Check whether the scenario needs static NAT, dynamic NAT or PAT.
→Do not confuse NAT matching ACLs with normal packet-filtering intent.
Key takeaway
NAT direction and interface roles matter as much as the IP address mapping. Inside/outside designation controls which traffic is translated.
Real-world example
How this comes up in practice
An e-commerce site experiences heavy traffic on Black Friday and near-zero traffic during off-peak weeks. Rather than provisioning permanent large VMs, the team uses auto-scaling groups that add capacity automatically under load and reduce it overnight. Questions like this test whether you understand elasticity, availability zones, and cloud compute scaling patterns.
What to study next
Got this wrong? Here's your next step.
Review the four NAT address types (inside local, inside global, outside local, outside global), PAT port overload, and static vs dynamic NAT use cases. Then practise related MLS-C01 NAT questions on configuration and troubleshooting.
Exploratory Data Analysis — This question tests Exploratory Data Analysis — Static NAT maps one inside address to one outside address..
What is the correct answer to this question?
The correct answer is: Remove the stop words from the text before creating the bag-of-words representation. — Option B is correct because removing stop words focuses on content words that carry sentiment. Option A is wrong because adding more stop words would remove even more potentially useful words. Option C is wrong because stemming reduces words to root forms but does not address stop words. Option D is wrong because n-grams capture phrases but still include stop words.
What should I do if I get this MLS-C01 question wrong?
Review the four NAT address types (inside local, inside global, outside local, outside global), PAT port overload, and static vs dynamic NAT use cases. Then practise related MLS-C01 NAT questions on configuration and troubleshooting.
What is the key concept behind this question?
Static NAT maps one inside address to one outside address.
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. A data scientist is performing EDA on a dataset containing text reviews. To understand the most common words, the data scientist generates a word cloud. Which preprocessing step is most important to ensure the word cloud reflects meaningful content?
hard
✓ A.Stop word removal
B.Part-of-speech tagging
C.Stemming
D.Tokenization
Why A: Option C is correct because removing stop words (common words like 'the', 'and') ensures that the word cloud highlights meaningful words. Stemming (A) may not be necessary for a word cloud. Tokenization (B) is fundamental but not the most critical for meaningfulness. POS tagging (D) is overkill.
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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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