The answer is that both stemming and stop word removal are inappropriate for the domain. The core issue lies in how these preprocessing techniques destroy semantic nuance in specialized vocabulary: stemming aggressively conflates distinct terms like 'therapy' and 'therapist' into the same root, while stop word removal discards words like 'not' or 'up' that carry critical meaning in medical negation or maintenance contexts. On the AWS Certified Machine Learning Engineer Associate MLA-C01 exam, this question tests your understanding that domain-specific NLP requires careful evaluation of preprocessing trade-offs—a common trap is assuming generic text cleaning always improves generalization, when in reality it can strip away the very signals the model needs. Remember the mnemonic "Don't Stem the Domain" to recall that aggressive root reduction and blanket stop word lists often backfire on specialized corpora.
MLA-C01 Data Preparation for Machine Learning Practice Question
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 data scientist is preparing text data for natural language processing (NLP). The corpus contains many rare words and typos. To reduce dimensionality and improve generalization, they decide to apply stemming and remove stop words. However, after training, the model performs poorly on domain-specific terms. What is the most likely cause?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue: "most likely"
Why it matters: Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Both stemming and stop word removal are inappropriate for the domain
Option B is correct because both stemming and stop word removal are inappropriate for this domain. Stemming aggressively reduces words to their root forms, which can conflate distinct domain-specific terms (e.g., 'therapy' and 'therapist' both stem to 'therap'), losing critical semantic nuance. Stop word removal can discard words that carry domain-specific meaning (e.g., 'not' in medical negation or 'up' in 'tune-up' for maintenance), leading to poor generalization on specialized vocabulary.
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.
✗
The corpus should be lemmatized instead
Why it's wrong here
Lemmatization may help but does not address the stop word issue.
✓
Both stemming and stop word removal are inappropriate for the domain
Why this is correct
In specialized domains, stemming can distort meaning and stop words can carry essential context.
Clue confirmation
The clue word "most likely" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
✗
Stemming is too aggressive for the domain
Why it's wrong here
While stemming could be aggressive, removing stop words could also be harmful; the combination is the issue.
✗
Stop word removal removed important context words
Why it's wrong here
Stop word removal might remove domain-specific context, but stemming is also problematic; both together are likely the cause.
Common exam traps
Common exam trap: answer the scenario, not the keyword
AWS often tests the misconception that lemmatization is always superior to stemming, but the trap here is that the root cause is the inappropriate application of both preprocessing techniques to domain-specific text, not the choice between stemming and lemmatization.
Detailed technical explanation
How to think about this question
Under the hood, stemming uses heuristic rules (e.g., Porter Stemmer) to chop off suffixes, which can produce non-dictionary roots (e.g., 'studies' -> 'studi'), while lemmatization uses a vocabulary and morphological analysis to return base dictionary forms (e.g., 'studies' -> 'study'). In domain-specific NLP, such as medical or legal text, rare terms and typos often carry critical meaning; aggressive normalization can map distinct concepts to the same token, reducing model discriminability. A real-world scenario is clinical NLP where 'cancer' and 'cancerous' should remain distinct for diagnosis classification, but stemming would collapse them, degrading performance.
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.
Data Preparation for Machine Learning — This question tests Data Preparation for Machine Learning — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Both stemming and stop word removal are inappropriate for the domain — Option B is correct because both stemming and stop word removal are inappropriate for this domain. Stemming aggressively reduces words to their root forms, which can conflate distinct domain-specific terms (e.g., 'therapy' and 'therapist' both stem to 'therap'), losing critical semantic nuance. Stop word removal can discard words that carry domain-specific meaning (e.g., 'not' in medical negation or 'up' in 'tune-up' for maintenance), leading to poor generalization on specialized vocabulary.
What should I do if I get this MLA-C01 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Are there clue words in this question I should notice?
Yes — watch for: "most likely". Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
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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