Quick Answer
The answer is Amazon Comprehend, as it is the only AWS AI service among the options designed to process unstructured text and can be used for checking text data quality with Comprehend by detecting syntax, entities, and unusual character patterns. While Comprehend does not directly flag spelling errors, its syntax analysis and entity recognition allow a data scientist to identify anomalies in review text during exploratory data analysis, such as malformed phrases or unexpected tokens, which is the closest fit for the task described. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this question tests your ability to match AWS services to specific data preparation tasks, with a common trap being to choose SageMaker Data Wrangler for its data-cleaning features—but remember that Data Wrangler is optimized for tabular, not textual, data. A useful memory tip: for text quality checks, think “Comprehend for content, Wrangler for columns.”
This MLS-C01 practice question tests your understanding of exploratory data analysis. Match the stated requirement to the specific cloud service, access model, or configuration option — many options are valid in isolation but not for this scenario. 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 working with a dataset that contains text reviews and a numeric rating (1-5). The goal is to predict the rating from the review text. During EDA, the scientist wants to check if there are any spelling errors or unusual characters. Which tool is BEST suited for this task?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue: "best"
Why it matters: Signals that multiple options may be partially correct. Choose the option that most directly solves the exact problem described, not the one that sounds most complete.
Amazon SageMaker Data Wrangler with a custom transform for text cleaning.
Why wrong: Data Wrangler is not designed for text analysis.
Amazon Athena with SQL queries to find anomalies.
Why wrong: Athena is not for text quality checks.
Amazon Comprehend to detect syntax and entities.
Comprehend can analyze text for structure.
Amazon QuickSight to create word clouds.
Why wrong: Word clouds are for visualization, not error detection.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Amazon Comprehend to detect syntax and entities.
Option C is correct because Amazon Comprehend can detect entities, key phrases, and syntax, but not spelling errors directly; however, it can be used to identify unusual patterns. Actually, for spelling errors, a custom solution may be needed. But among options, Comprehend is the only AWS AI service that processes text. Option A is wrong because SageMaker Data Wrangler is for tabular data. Option B is wrong because Athena is for SQL. Option D is wrong because QuickSight is for visualization.
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
For each option: why learners choose it and why it is or isn't the right answer here.
Amazon SageMaker Data Wrangler with a custom transform for text cleaning.
Why it's wrong here
Data Wrangler is not designed for text analysis.
Amazon Athena with SQL queries to find anomalies.
Why it's wrong here
Athena is not for text quality checks.
Amazon Comprehend to detect syntax and entities.
Why this is correct
Comprehend can analyze text for structure.
Clue confirmation
The clue word "best" in the question point toward this answer.
Related concept
Static NAT maps one inside address to one outside address.
Amazon QuickSight to create word clouds.
Why it's wrong here
Word clouds are for visualization, not error detection.
Common exam traps
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
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.
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
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
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 MLS-C01 NAT questions on configuration and troubleshooting.
Exploratory Data Analysis — study guide chapter
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FAQ
Exploratory Data Analysis — This question tests Exploratory Data Analysis — Static NAT maps one inside address to one outside address..
The correct answer is: Amazon Comprehend to detect syntax and entities. — Option C is correct because Amazon Comprehend can detect entities, key phrases, and syntax, but not spelling errors directly; however, it can be used to identify unusual patterns. Actually, for spelling errors, a custom solution may be needed. But among options, Comprehend is the only AWS AI service that processes text. Option A is wrong because SageMaker Data Wrangler is for tabular data. Option B is wrong because Athena is for SQL. Option D is wrong because QuickSight is for visualization.
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 MLS-C01 NAT questions on configuration and troubleshooting.
Yes — watch for: "best". Signals that multiple options may be partially correct. Choose the option that most directly solves the exact problem described, not the one that sounds most complete.
Static NAT maps one inside address to one outside address.
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Last reviewed: Jun 20, 2026
This MLS-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 MLS-C01 exam.
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