- A
Remove all punctuation and URLs
Punctuation and URLs are typically not useful for sentiment and add noise.
- B
Apply stemming to reduce words to root forms
Why wrong: Stemming can help but may also lose meaning; it's not always necessary.
- C
Remove common stop words
Stop words add little meaning and can be removed to reduce dimensionality.
- D
Convert all text to lowercase
Why wrong: Lowercasing is standard but the question asks for two; other steps are more impactful.
- E
Tokenize the text into individual words
Why wrong: Tokenization is fundamental but is a step, not specifically for improving performance.
Question 39 of 1,000
NLP Text Preprocessing: Stop Words and Punctuation Removal
This AI0-001 practice question tests your understanding of ai models and data engineering. Compare every option against the stated constraints before choosing — the best answer satisfies all requirements, not just the most obvious one. 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 natural language processing (NLP) team is building a sentiment analysis model. The raw text data contains punctuation, stop words, and URLs. Which TWO preprocessing steps are most appropriate to improve model performance? (Choose two.)
Quick Answer
The correct answer is to remove common stop words and remove punctuation and URLs. These two preprocessing steps directly reduce noise in raw text by eliminating frequent but uninformative words like “the” or “and,” and by stripping out non-alphanumeric characters and web addresses that add no semantic value to a sentiment analysis model. On the CompTIA AI+ AI0-001 exam, this question tests your understanding of foundational NLP text preprocessing stop words punctuation removal—a core step before feature extraction. A common trap is confusing tokenization or stemming with noise reduction; while tokenization is necessary, it does not improve performance by itself, and stemming may alter word meaning. For a quick memory tip, think “Noise First”: always strip stop words and punctuation before applying any linguistic transformations.
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
✓
Remove all punctuation and URLs
Removing punctuation and URLs eliminates noise that does not contribute to sentiment (e.g., 'http://...' or '!!!'), allowing the model to focus on meaningful words. This step reduces vocabulary size and prevents the model from learning spurious correlations tied to formatting artifacts.
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.
- ✓
Remove all punctuation and URLs
Why this is correct
Punctuation and URLs are typically not useful for sentiment and add noise.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Apply stemming to reduce words to root forms
Why it's wrong here
Stemming can help but may also lose meaning; it's not always necessary.
- ✓
Remove common stop words
Why this is correct
Stop words add little meaning and can be removed to reduce dimensionality.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Convert all text to lowercase
Why it's wrong here
Lowercasing is standard but the question asks for two; other steps are more impactful.
- ✗
Tokenize the text into individual words
Why it's wrong here
Tokenization is fundamental but is a step, not specifically for improving performance.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Cisco often tests the distinction between mandatory preprocessing steps (like tokenization) and steps that specifically improve performance by reducing noise, leading candidates to select tokenization or lowercasing instead of the more impactful noise-removal steps.
Detailed technical explanation
How to think about this question
In sentiment analysis, punctuation like '!!!' can carry sentiment signal (e.g., excitement), but URLs are pure noise. Removing them prevents the model from wasting capacity on irrelevant tokens. Stop words (e.g., 'the', 'and') are high-frequency but low-information; their removal reduces feature dimensionality and can improve model focus on sentiment-bearing terms like 'amazing' or 'terrible'.
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 practitioner preparing for the AI0-001 exam encounters this exact type of scenario on the job. The correct answer here is not the most general option — it is the best answer for the specific constraint described. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Real exam questions reward reading the full scenario before eliminating options, because the constraint defines which answer fits.
What to study next
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FAQ
Questions learners often ask
What does this AI0-001 question test?
AI Models and Data Engineering — This question tests AI Models and Data Engineering — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Remove all punctuation and URLs — Removing punctuation and URLs eliminates noise that does not contribute to sentiment (e.g., 'http://...' or '!!!'), allowing the model to focus on meaningful words. This step reduces vocabulary size and prevents the model from learning spurious correlations tied to formatting artifacts.
What should I do if I get this AI0-001 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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Last reviewed: Jul 4, 2026
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