The answer is to use word embeddings to reduce dimensionality for bag-of-words text classification. This technique is correct because word embeddings like Word2Vec or GloVe map each of the 100,000 unique words into dense, low-dimensional vectors (typically 100–300 dimensions), directly transforming the sparse term-document matrix into a compact, semantically rich representation. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this question tests your understanding of feature engineering for high-cardinality text data, often appearing in scenarios where traditional bag-of-words leads to the curse of dimensionality. A common trap is choosing PCA or SVD, which work on dense matrices but fail to capture word semantics as effectively as embeddings. Remember the memory tip: “Embeddings embed meaning into fewer dimensions” — they shrink sparse word counts into dense semantic space while preserving context.
MLS-C01 Modeling Practice Question
This MLS-C01 practice question tests your understanding of modeling. 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 training a text classification model using a bag-of-words approach. The dataset contains 1 million documents and 100,000 unique words. The resulting feature matrix is very sparse. Which technique should the data scientist use to reduce the dimensionality of the feature space?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Use word embeddings to represent documents
Word embeddings (e.g., Word2Vec, GloVe) map words to dense, low-dimensional vectors that capture semantic relationships, effectively reducing the 100,000-dimensional sparse bag-of-words feature space to a much smaller dense representation (e.g., 100–300 dimensions). This directly addresses the sparsity and high dimensionality of the term-document matrix while preserving meaningful word context.
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.
✗
Apply TF-IDF transformation
Why it's wrong here
TF-IDF weights terms but does not reduce the number of features.
✓
Use word embeddings to represent documents
Why this is correct
Word embeddings create dense low-dimensional vectors, reducing sparsity and dimensionality.
Related concept
Read the scenario before looking for a memorised answer.
✗
Remove stop words from the vocabulary
Why it's wrong here
Removing stop words reduces dimensions but may not be sufficient and can remove meaningful words.
✗
Apply Principal Component Analysis (PCA) to the term-document matrix
Why it's wrong here
PCA can reduce dimensions but is less effective for sparse text data than embeddings.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates confuse TF-IDF (a reweighting technique) with dimensionality reduction, or assume PCA can be directly applied to sparse text matrices without considering computational cost and loss of interpretability.
Detailed technical explanation
How to think about this question
Word embeddings are typically learned via neural networks (e.g., skip-gram or CBOW) that predict context words, producing dense vectors where cosine similarity reflects semantic similarity. In practice, averaging or concatenating word embeddings for each document yields a fixed-length dense representation (e.g., 300D) that drastically reduces memory and improves model generalization, especially for large-scale text classification tasks.
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.
Modeling — This question tests Modeling — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Use word embeddings to represent documents — Word embeddings (e.g., Word2Vec, GloVe) map words to dense, low-dimensional vectors that capture semantic relationships, effectively reducing the 100,000-dimensional sparse bag-of-words feature space to a much smaller dense representation (e.g., 100–300 dimensions). This directly addresses the sparsity and high dimensionality of the term-document matrix while preserving meaningful word context.
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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Question Discussion
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