- A
TF-IDF
Why wrong: TF-IDF also ignores word order.
- B
N-grams with frequency counts
N-grams represent contiguous sequences of n words, thus preserving local word order.
- C
Word2Vec
Why wrong: Word2Vec embeddings capture context but do not preserve the sequential order of words in a sentence.
- D
Bag-of-words
Why wrong: Bag-of-words disregards word order and only considers frequency.
Question 264 of 1,000
Text Feature Extraction: N-grams Preserving Word Order
This AI0-001 practice question tests your understanding of ai models and data engineering. 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 engineer is preprocessing text data for sentiment analysis. Which technique preserves word order while converting text to numeric features?
Quick Answer
The answer is n-grams with frequency counts, as this technique uniquely preserves word order while converting text into numeric features. By sliding a window of size n across the text, n-grams capture contiguous sequences of words—such as bigrams or trigrams—so that the order of tokens within each window is retained in the feature representation. On the CompTIA AI+ AI0-001 exam, this question tests your understanding of how different feature extraction methods handle sequential information; a common trap is confusing word embeddings like word2vec, which encode semantic relationships but discard exact positional order, with n-grams that explicitly model local word sequences. Remember that bag-of-words and TF-IDF both ignore order entirely, making n-grams the only option here that preserves it. A helpful memory tip: think of n-grams as “word chains”—each link in the chain keeps its neighbors in sequence, unlike a bag where all words are jumbled together.
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
✓
N-grams with frequency counts
Option B (N-grams with frequency counts) is correct because n-grams capture sequences of words, preserving local order. TF-IDF and Bag-of-words ignore word order entirely, treating text as an unordered set of tokens. Word2Vec produces dense embeddings that encode semantic similarity based on context, but it does not explicitly preserve exact word order; it learns from surrounding words but focuses on meaning rather than sequence.
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.
- ✗
TF-IDF
Why it's wrong here
TF-IDF also ignores word order.
- ✓
N-grams with frequency counts
Why this is correct
N-grams represent contiguous sequences of n words, thus preserving local word order.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Word2Vec
Why it's wrong here
Word2Vec embeddings capture context but do not preserve the sequential order of words in a sentence.
- ✗
Bag-of-words
Why it's wrong here
Bag-of-words disregards word order and only considers frequency.
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 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
Got this wrong? Here's your next step.
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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: N-grams with frequency counts — Option B (N-grams with frequency counts) is correct because n-grams capture sequences of words, preserving local order. TF-IDF and Bag-of-words ignore word order entirely, treating text as an unordered set of tokens. Word2Vec produces dense embeddings that encode semantic similarity based on context, but it does not explicitly preserve exact word order; it learns from surrounding words but focuses on meaning rather than sequence.
What should I do if I get this AI0-001 question wrong?
Identify which AI0-001 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.
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Last reviewed: Jun 23, 2026
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