The answer is performing stemming or lemmatization, along with converting text to lowercase and removing HTML tags, URLs, and special characters. These three steps are essential for standard text preprocessing in NLP because they normalize the data, reduce vocabulary size, and eliminate noise that would confuse machine learning models. Stemming or lemmatization reduces words to their base or root form, ensuring that variations like “running” and “ran” are treated as the same feature, which is critical for tasks like sentiment analysis or topic modeling. On the AWS Certified Machine Learning Engineer Associate MLA-C01 exam, this question tests your understanding of the foundational data cleaning pipeline before vectorization, often appearing in scenario-based questions where raw text contains web artifacts. A common trap is to skip normalization steps like lowercasing, assuming the model will handle case variations, but this inflates feature space unnecessarily. Remember the mnemonic “CLS” for Clean, Lowercase, and Stem to recall the core preprocessing sequence.
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 cleaning a text dataset for natural language processing. The raw data contains HTML tags, URLs, and special characters. Which THREE steps should be taken to preprocess the text data? (Choose 3.)
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Convert all text to lowercase
Converting all text to lowercase (Option A) is a standard text normalization step in NLP preprocessing. It reduces the vocabulary size by treating words like 'Apple' and 'apple' as the same token, which helps downstream models avoid treating case variations as distinct features. This is typically done early in the pipeline before tokenization or vectorization.
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.
✓
Convert all text to lowercase
Why this is correct
Lowercasing standardizes text and reduces vocabulary size.
Related concept
Read the scenario before looking for a memorised answer.
✗
Encode the text using one-hot encoding
Why it's wrong here
One-hot encoding is a feature extraction step, not a cleaning step.
✓
Remove HTML tags using a regular expression
Why this is correct
HTML tags are noise and should be removed before analysis.
Related concept
Read the scenario before looking for a memorised answer.
✓
Perform stemming or lemmatization
Why this is correct
Stemming/lemmatization reduces words to root forms, aiding generalization.
Related concept
Read the scenario before looking for a memorised answer.
✗
Remove stop words
Why it's wrong here
Stop word removal is optional and may not be appropriate for all NLP tasks (e.g., sentiment analysis).
Common exam traps
Common exam trap: answer the scenario, not the keyword
AWS often tests the distinction between preprocessing steps that clean raw data (like removing HTML tags and normalizing case) versus later feature engineering steps (like encoding or stop word removal), causing candidates to mistakenly select stop word removal as a cleaning step when it is actually a filtering step applied after tokenization.
Detailed technical explanation
How to think about this question
HTML tag removal via regular expressions (e.g., re.sub(r'<[^>]+>', '', text)) strips markup without affecting the textual content, which is critical when scraping web data. Stemming or lemmatization reduces words to their base forms (e.g., 'running' to 'run'), collapsing inflected forms and improving feature space efficiency; lemmatization uses a dictionary (e.g., WordNet) for more accurate root forms compared to the heuristic-based stemming. These steps are typically applied after tokenization and before vectorization.
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: Convert all text to lowercase — Converting all text to lowercase (Option A) is a standard text normalization step in NLP preprocessing. It reduces the vocabulary size by treating words like 'Apple' and 'apple' as the same token, which helps downstream models avoid treating case variations as distinct features. This is typically done early in the pipeline before tokenization or vectorization.
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.
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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