The correct answer is to apply a TF-IDF transformation, because this technique directly addresses the need to reduce the impact of very common words like 'the' while preserving the significance of rarer, more meaningful terms like 'excellent'. TF-IDF works by combining term frequency with inverse document frequency, which naturally downweights words that appear across many documents, making it the ideal method for this scenario. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this question tests your understanding of feature engineering for text data, often appearing alongside traps like bag-of-words (which gives equal weight to all terms) or simple stopword removal (which only eliminates a fixed list). A common mistake is confusing TF-IDF with word embeddings like word2vec, but remember that TF-IDF is purely a frequency-based weighting scheme, not a context-based representation. Memory tip: think of TF-IDF as a spotlight that dims the common crowd and highlights the rare gems.
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 machine learning engineer is examining a dataset containing text reviews. They want to convert the text into numerical features for a model. During EDA, they notice that the word 'the' appears in almost every review, while words like 'excellent' appear rarely. Which of the following techniques should they use to reduce the impact of very common words?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Apply TF-IDF transformation.
Option C is correct because TF-IDF downweights common words. Option A is wrong because bag-of-words does not weight. Option B is wrong because removing stopwords helps but does not adjust for frequency beyond that. Option D is wrong because word2vec focuses on context, not frequency weighting.
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 this is correct
TF-IDF reduces the weight of terms that appear frequently across documents.
Related concept
Read the scenario before looking for a memorised answer.
✗
Remove stopwords from the text.
Why it's wrong here
Removing stopwords helps but still other common words may dominate.
✗
Use word2vec embeddings.
Why it's wrong here
Word2vec captures semantic meaning but does not automatically downweight frequent words.
✗
Use a bag-of-words representation.
Why it's wrong here
Bag-of-words counts frequencies, so common words dominate.
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 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 MLS-C01 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.
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 TF-IDF transformation. — Option C is correct because TF-IDF downweights common words. Option A is wrong because bag-of-words does not weight. Option B is wrong because removing stopwords helps but does not adjust for frequency beyond that. Option D is wrong because word2vec focuses on context, not frequency weighting.
What should I do if I get this MLS-C01 question wrong?
Identify which MLS-C01 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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Question Discussion
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