The answer is stop word removal and tokenization. These two text preprocessing techniques for NLP in SageMaker are foundational because they transform raw, unstructured text into a clean, analyzable format: tokenization splits sentences into individual words or tokens, while stop word removal eliminates common, low-information words like "the" or "and" that add noise to sentiment analysis. On the AWS Certified Machine Learning Engineer Associate MLA-C01 exam, this question tests your understanding of the standard data preparation pipeline before feature engineering—a common trap is confusing one-hot encoding of all words (which creates a sparse, high-dimensional matrix) with these essential preprocessing steps. Remember that tokenization and stop word removal reduce dimensionality and improve model focus, whereas techniques like PCA or image resizing belong to other domains. A useful memory tip: think "Toke-n-stop" as the first two gears in any NLP pipeline before you ever touch a vectorizer.
MLA-C01 ML Model Development Practice Question
This MLA-C01 practice question tests your understanding of ml model development. 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 the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Tokenization
Stop word removal and tokenization are standard text preprocessing steps. One-hot encoding of all words leads to high dimensionality and is rarely used directly. Image resizing is for images, and PCA is for numerical dimensionality reduction.
Key principle: NAT direction and interface roles matter as much as the IP address mapping. Inside/outside designation controls which traffic is translated.
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
Static NAT maps one inside address to one outside address.
✗
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
Static NAT maps one inside address to one outside address.
Common exam traps
Common exam trap: NAT rules depend on direction and matching traffic
NAT is not only about the public address. The inside/outside interface roles and the ACL or rule that matches traffic are just as important.
Detailed technical explanation
How to think about this question
NAT questions usually test address translation, overload/PAT behaviour, static mappings and whether the right traffic is being translated. Read the interface direction and address terms carefully.
KKey Concepts to Remember
Static NAT maps one inside address to one outside address.
PAT allows many inside hosts to share one public address using ports.
Inside local and inside global describe the private and translated addresses.
NAT ACLs identify traffic for translation, not always security filtering.
TExam Day Tips
→Identify inside and outside interfaces first.
→Check whether the scenario needs static NAT, dynamic NAT or PAT.
→Do not confuse NAT matching ACLs with normal packet-filtering intent.
Key takeaway
NAT direction and interface roles matter as much as the IP address mapping. Inside/outside designation controls which traffic is translated.
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. NAT direction and interface roles matter as much as the IP address mapping. Inside/outside designation controls which traffic is translated. 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.
Review the four NAT address types (inside local, inside global, outside local, outside global), PAT port overload, and static vs dynamic NAT use cases. Then practise related MLA-C01 NAT questions on configuration and troubleshooting.
ML Model Development — This question tests ML Model Development — Static NAT maps one inside address to one outside address..
What is the correct answer to this question?
The correct answer is: Tokenization — Stop word removal and tokenization are standard text preprocessing steps. One-hot encoding of all words leads to high dimensionality and is rarely used directly. Image resizing is for images, and PCA is for numerical dimensionality reduction.
What should I do if I get this MLA-C01 question wrong?
Review the four NAT address types (inside local, inside global, outside local, outside global), PAT port overload, and static vs dynamic NAT use cases. Then practise related MLA-C01 NAT questions on configuration and troubleshooting.
What is the key concept behind this question?
Static NAT maps one inside address to one outside address.
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 →
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.
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.