The correct answer includes removing common stop words like 'the', 'and', and 'is' as one of the three preprocessing steps for sentiment analysis. This step is essential because stop words carry little semantic meaning and add noise to the feature space, allowing the model to focus on content-bearing words that actually indicate sentiment. For the AWS Certified Machine Learning Specialty MLS-C01 exam, this question tests your understanding of fundamental NLP text preprocessing pipelines, where you must distinguish between cleaning steps (stop word removal, stemming, lowercasing) and feature extraction methods like TF-IDF or word embeddings. A common trap is confusing stemming with lemmatization—stemming chops words to root forms (e.g., 'running' to 'run') without regard for linguistic correctness, which is acceptable for reducing dimensionality in sentiment tasks. Remember the mnemonic "Clean, Stem, Remove Noise" to recall that preprocessing typically involves lowercasing, stemming or lemmatization, and stop word removal before any vectorization.
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 analyzing a dataset of customer reviews. The dataset contains a text column 'review' and a numerical rating from 1 to 5. The data scientist wants to create features for sentiment analysis. Which THREE preprocessing steps should be applied to the text data before feature extraction? (Choose THREE.)
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Apply stemming to reduce words to their root form.
Option B is correct because stemming reduces words to their root form (e.g., 'running' to 'run'), which consolidates variations of the same word and reduces feature dimensionality. This is a standard preprocessing step before feature extraction in NLP tasks like sentiment analysis, as it helps the model generalize across different word forms.
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.
✗
Standardize the text data using z-score normalization.
Why it's wrong here
Text data is categorical; standardization is not applicable.
✓
Apply stemming to reduce words to their root form.
Why this is correct
Stemming groups related words, reducing feature dimensionality.
Related concept
Read the scenario before looking for a memorised answer.
✗
Tokenize the text into individual words.
Why it's wrong here
Tokenization is a prerequisite but not typically considered a preprocessing step after tokenization; it is part of the pipeline.
✓
Convert all text to lowercase.
Why this is correct
Lowercasing ensures consistency and reduces vocabulary size.
Related concept
Read the scenario before looking for a memorised answer.
✓
Remove common stop words (e.g., 'the', 'and', 'is').
Why this is correct
Stop words are frequent but often irrelevant for sentiment analysis.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Cisco often tests the distinction between preprocessing steps that are specific to text (like stemming, lowercasing, stop word removal) versus those meant for numerical data (like normalization), and candidates may mistakenly apply scaling techniques to text or forget that tokenization is a prerequisite but not always listed as a separate 'correct' step in multi-select questions.
Detailed technical explanation
How to think about this question
Stemming typically uses algorithms like the Porter Stemmer or Snowball, which apply heuristic rules to chop off suffixes, often producing non-dictionary root forms (e.g., 'studies' becomes 'studi'). In contrast, lemmatization uses vocabulary and morphological analysis to return the base dictionary form (e.g., 'studies' becomes 'study'), but stemming is faster and more commonly used in high-volume text preprocessing for bag-of-words or TF-IDF pipelines.
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.
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: Apply stemming to reduce words to their root form. — Option B is correct because stemming reduces words to their root form (e.g., 'running' to 'run'), which consolidates variations of the same word and reduces feature dimensionality. This is a standard preprocessing step before feature extraction in NLP tasks like sentiment analysis, as it helps the model generalize across different word forms.
What should I do if I get this MLS-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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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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