Quick Answer
The correct technique is word embeddings, such as Word2Vec or GloVe, because they convert text into dense numerical vectors that preserve semantic meaning by capturing word relationships based on context. Unlike sparse methods like bag-of-words or TF-IDF, which treat words as independent tokens, embeddings ensure that semantically similar words—like “good” and “excellent”—have similar vector representations, which is critical for sentiment analysis where nuance matters. On the AWS Certified Machine Learning Engineer Associate MLA-C01 exam, this question tests your understanding of text preprocessing trade-offs, often appearing in pipeline design scenarios where you must choose between sparse and dense representations. A common trap is selecting count-based methods for speed, but the key is that embeddings retain contextual similarity. Memory tip: think “dense for sense”—dense vectors preserve semantic sense, while sparse methods lose it.
This MLA-C01 practice question tests your understanding of data preparation for machine learning. 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 machine learning engineer is building a pipeline to preprocess text data for a sentiment analysis model. The data consists of customer reviews. The engineer wants to convert the text into numerical features while preserving the semantic meaning of words. Which technique should be used?
One-hot encoding of each word
Why wrong: One-hot encoding creates sparse vectors without semantic similarity.
Bag-of-words with TF-IDF
Why wrong: TF-IDF captures term frequency but not semantic meaning.
Hashing vectorizer
Why wrong: Hashing vectorizer reduces dimensionality but loses semantic information.
Word embeddings (e.g., Word2Vec or GloVe)
Word embeddings represent words in dense vector spaces that preserve semantic relationships.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Word embeddings (e.g., Word2Vec or GloVe)
Word embeddings (like Word2Vec or GloVe) are dense vector representations that capture semantic relationships between words based on their context in a large corpus. For sentiment analysis, preserving semantic meaning (e.g., 'good' and 'excellent' having similar vectors) is critical, and embeddings directly encode this, unlike sparse or count-based methods.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
For each option: why learners choose it and why it is or isn't the right answer here.
One-hot encoding of each word
Why it's wrong here
One-hot encoding creates sparse vectors without semantic similarity.
Bag-of-words with TF-IDF
Why it's wrong here
TF-IDF captures term frequency but not semantic meaning.
Hashing vectorizer
Why it's wrong here
Hashing vectorizer reduces dimensionality but loses semantic information.
Word embeddings (e.g., Word2Vec or GloVe)
Why this is correct
Word embeddings represent words in dense vector spaces that preserve semantic relationships.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
The trap here is that candidates often choose TF-IDF (Option B) because it is a common text preprocessing technique, but they overlook the explicit requirement to 'preserve semantic meaning,' which only dense embeddings can achieve.
Trap categories for this question
Similar concept trap
One-hot encoding creates sparse vectors without semantic similarity.
Detailed technical explanation
Word embeddings are learned by predicting context words (CBOW) or target words (Skip-gram) in a neural network, resulting in dense vectors (e.g., 300 dimensions) where cosine similarity reflects semantic closeness. In sentiment analysis, this allows the model to generalize—e.g., 'amazing' and 'fantastic' map to nearby regions in vector space, improving performance on unseen but similar words. A subtle behavior is that embeddings can capture analogies (e.g., 'king' - 'man' + 'woman' ≈ 'queen'), which is impossible with TF-IDF or one-hot encoding.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
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.
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FAQ
Data Preparation for Machine Learning — This question tests Data Preparation for Machine Learning — Read the scenario before looking for a memorised answer..
The correct answer is: Word embeddings (e.g., Word2Vec or GloVe) — Word embeddings (like Word2Vec or GloVe) are dense vector representations that capture semantic relationships between words based on their context in a large corpus. For sentiment analysis, preserving semantic meaning (e.g., 'good' and 'excellent' having similar vectors) is critical, and embeddings directly encode this, unlike sparse or count-based methods.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 24, 2026
This MLA-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 MLA-C01 exam.
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