Question 1easymultiple choice
Full question →- A
AWS Glue DataBrew
Why A is correct
- B
Amazon SageMaker Data Wrangler
Why wrong: Why D is wrong
- C
Amazon QuickSight
Why wrong: Why C is wrong
- D
Amazon Athena
Why wrong: Why B is wrong
Question 207 of 1,755
Quick Answer
The answer is AWS Glue DataBrew, as it is specifically designed for quick data profiling without code. This service allows you to upload a dataset from Amazon S3 and instantly visualize column distributions, missing values, data types, and basic statistics through an interactive, point-and-click interface. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this question tests your ability to distinguish between services for initial data exploration versus deeper analysis or visualization. A common trap is confusing DataBrew with Amazon Athena, which is for querying data with SQL, not profiling, or with SageMaker Data Wrangler, which is better suited for feature engineering within a machine learning pipeline. Remember that DataBrew is the go-to for a fast, code-free snapshot of your data’s health. A helpful memory tip: think of DataBrew as your “data coffee break” — a quick, automated taste test before you commit to a full brew of analysis.
MLS-C01 Exploratory Data Analysis Practice Question
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 needs to analyze a dataset stored in Amazon S3 as CSV files. The dataset contains 100 columns, and the data scientist wants to quickly understand the distribution of each column, including missing values, data types, and basic statistics. Which AWS service 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.
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
✓
AWS Glue DataBrew
Option A is correct because AWS Glue DataBrew provides visual data profiling and preparation without writing code. Option B is wrong because Amazon Athena is an interactive query service, not a profiling tool. Option C is wrong because Amazon QuickSight is for visualization, not data profiling. Option D is wrong because SageMaker Data Wrangler is for feature engineering within SageMaker, but DataBrew is simpler for initial exploration.
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.
- ✓
AWS Glue DataBrew
Why this is correct
Why A is correct
Clue confirmation
The clue word "best" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Amazon SageMaker Data Wrangler
Why it's wrong here
Why D is wrong
- ✗
Amazon QuickSight
Why it's wrong here
Why C is wrong
- ✗
Amazon Athena
Why it's wrong here
Why B is wrong
Common exam traps
Common exam trap: answer the scenario, not the keyword
Many certification questions include familiar terms but test a specific constraint. Read the exact wording before choosing an answer that is generally true but wrong for this case.
Detailed technical explanation
How to think about this question
This question should be treated as a scenario, not a definition check. Identify the problem, the constraint and the best action. Then compare each option against those facts.
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.
- Use explanations to understand the rule behind the answer.
TExam Day Tips
- Underline the problem statement mentally.
- 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 media company stores terabytes of video archives that are accessed once a year for audit purposes. Moving these objects to a cold storage tier (Azure Archive, S3 Glacier, or Google Nearline) costs a fraction of hot storage. Questions like this test whether you understand storage tiers, access frequency tradeoffs, and retrieval latency requirements.
What to study next
Got this wrong? Here's your next step.
Identify which MLS-C01 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
- →
Exploratory Data Analysis — study guide chapter
Learn the concepts, then practise the questions
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Exploratory Data Analysis practice questions
Targeted practice on this topic area only
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FAQ
Questions learners often ask
What does this MLS-C01 question test?
Exploratory Data Analysis — This question tests Exploratory Data Analysis — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: AWS Glue DataBrew — Option A is correct because AWS Glue DataBrew provides visual data profiling and preparation without writing code. Option B is wrong because Amazon Athena is an interactive query service, not a profiling tool. Option C is wrong because Amazon QuickSight is for visualization, not data profiling. Option D is wrong because SageMaker Data Wrangler is for feature engineering within SageMaker, but DataBrew is simpler for initial exploration.
What should I do if I get this MLS-C01 question wrong?
Identify which MLS-C01 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
Are there clue words in this question I should notice?
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.
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 →
Same concept, more angles
1 more ways this is tested on MLS-C01
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. A data scientist is exploring a dataset stored in an Amazon S3 bucket. The dataset contains both numerical and categorical features. The scientist wants to compute summary statistics (mean, median, standard deviation) for all numerical features and count the distinct values for categorical features. Which AWS service is most appropriate for this task with minimal coding?
medium- A.Amazon Athena
- B.AWS Glue ETL jobs
- ✓ C.AWS Glue DataBrew
- D.Amazon SageMaker Data Wrangler
- E.Amazon EMR
Why C: AWS Glue DataBrew provides a visual interface for data preparation and profiling, including summary statistics and distinct value counts, without writing code. Option A is wrong because Amazon SageMaker Data Wrangler requires integration with SageMaker and may require more setup. Option B is wrong because AWS Glue ETL jobs require coding in Python or Scala. Option D is wrong because Amazon Athena requires SQL queries. Option E is wrong because Amazon EMR requires cluster management and coding.
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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