- A
One-hot encoding of all words
Why wrong: One-hot encoding all words creates an extremely large sparse matrix and is not recommended for NLP.
- B
Image resizing
Why wrong: Image resizing is a technique for image data, not text.
- C
Tokenization
Tokenization splits text into tokens (words or subwords), a fundamental step in NLP preprocessing.
- D
Principal component analysis (PCA)
Why wrong: PCA is a dimensionality reduction technique for numerical data, not typical for text preprocessing.
- E
Stop word removal
Removing common stop words helps reduce noise and improve model performance.
Question 220 of 1,000
mediumMultiple SelectObjective-mapped
MLA-C01 Practice Question: A data scientist is preparing text data for a…
This MLA-C01 practice question tests your understanding of mla-c01 exam topics. 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 a sentiment analysis model using Amazon SageMaker. Which two data preprocessing techniques are commonly used when working with text data for natural language processing? (Choose two.)
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Tokenization
Tokenization is correct because it is a fundamental preprocessing step that splits raw text into smaller units (tokens), such as words or subwords, which are necessary for converting text into a structured format that machine learning models can process. Stop word removal is correct because it filters out common words (e.g., 'the', 'and', 'is') that carry little semantic meaning, reducing noise and improving model performance in sentiment analysis.
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.
- ✗
One-hot encoding of all words
Why it's wrong here
One-hot encoding all words creates an extremely large sparse matrix and is not recommended for NLP.
- ✗
Image resizing
Why it's wrong here
Image resizing is a technique for image data, not text.
- ✓
Tokenization
Why this is correct
Tokenization splits text into tokens (words or subwords), a fundamental step in NLP preprocessing.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Principal component analysis (PCA)
Why it's wrong here
PCA is a dimensionality reduction technique for numerical data, not typical for text preprocessing.
- ✓
Stop word removal
Why this is correct
Removing common stop words helps reduce noise and improve model performance.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates may confuse one-hot encoding as a preprocessing technique for raw text, when it is actually a feature engineering step applied after tokenization, and they may overlook that stop word removal is a standard preprocessing step despite its potential to remove sentiment-bearing words in certain contexts.
Detailed technical explanation
How to think about this question
Tokenization can be performed at different granularities, such as word-level, subword-level (e.g., Byte-Pair Encoding), or character-level, each affecting model vocabulary size and handling of out-of-vocabulary words. Stop word removal must be applied carefully in sentiment analysis because words like 'not' or 'very' can carry significant sentiment polarity; domain-specific stop word lists are often curated to avoid losing critical context. Under the hood, these steps are typically implemented using libraries like NLTK or spaCy, which use rule-based or statistical methods to split text and filter tokens.
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.
Related practice questions
Related MLA-C01 practice-question pages
Use these pages to review the topic behind this question. This is how one missed question becomes focused revision.
ML Model Development practice questions
Practise MLA-C01 questions linked to ML Model Development.
Data Preparation for Machine Learning practice questions
Practise MLA-C01 questions linked to Data Preparation for Machine Learning.
Deployment and Orchestration of ML Workflows practice questions
Practise MLA-C01 questions linked to Deployment and Orchestration of ML Workflows.
ML Solution Monitoring, Maintenance, and Security practice questions
Practise MLA-C01 questions linked to ML Solution Monitoring, Maintenance, and Security.
ML Solution Monitoring, Maintenance and Security practice questions
Practise MLA-C01 questions linked to ML Solution Monitoring, Maintenance and Security.
MLA-C01 fundamentals practice questions
Practise MLA-C01 questions linked to MLA-C01 fundamentals.
MLA-C01 scenario practice questions
Practise MLA-C01 questions linked to MLA-C01 scenario.
MLA-C01 troubleshooting practice questions
Practise MLA-C01 questions linked to MLA-C01 troubleshooting.
Practice this exam
Start a free MLA-C01 practice session
Short sessions build daily habit. Longer sessions build exam-day stamina. Try a timed session to simulate real conditions.
FAQ
Questions learners often ask
What does this MLA-C01 question test?
Read the scenario before looking for a memorised answer.
What is the correct answer to this question?
The correct answer is: Tokenization — Tokenization is correct because it is a fundamental preprocessing step that splits raw text into smaller units (tokens), such as words or subwords, which are necessary for converting text into a structured format that machine learning models can process. Stop word removal is correct because it filters out common words (e.g., 'the', 'and', 'is') that carry little semantic meaning, reducing noise and improving model performance in sentiment analysis.
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.
About these practice questions
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
Keep practising
More MLA-C01 practice questions
- A team is using SageMaker Pipelines to train a model. The pipeline has multiple steps: data processing, training, evalua…
- A machine learning team deploys a custom container image for an Amazon SageMaker training job. The container needs to ac…
- A machine learning engineer sees the above error in Amazon CloudWatch Logs for a SageMaker endpoint. What is the most li…
- A data scientist has trained a model that achieves 95% accuracy on the training set but only 70% on the test set. Which…
- Refer to the exhibit. A data scientist reviews the output of a SageMaker training job. The model has 95% training accura…
- A team is using Amazon SageMaker to train a neural network. They want to minimize training time while effectively explor…
Last reviewed: Jul 4, 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.
Question Discussion
Share a tip, memory trick, or ask about the reasoning behind this question. Do not post real exam questions, leaked content, braindumps, or copyrighted exam material. Comments are moderated and may be removed without notice.
Sign in to join the discussion.