The answer is to use AWS Glue DataBrew to create a profile job that outputs data quality reports. This is correct because DataBrew is purpose-built for data profiling without code, automatically detecting data types, missing values, and summary statistics across large datasets like the 500-column, 2-million-row CSV files stored in S3. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this question tests your ability to distinguish between visual ETL tools and manual coding approaches—a common trap is choosing SageMaker Data Wrangler, which requires more manual configuration and is better suited for feature engineering than quick profiling. Remember that DataBrew’s profile jobs are designed for one-click exploratory data analysis (EDA) on massive datasets, while Athena needs SQL and Glue ETL jobs demand scripting. Memory tip: think “Brew for preview, Wrangler for transform”—DataBrew profiles without code, so when the question emphasizes speed and no coding, reach for the brew.
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 is working with a dataset containing customer transaction records stored in Amazon S3 as CSV files. The dataset has 500 columns and 2 million rows. The scientist wants to perform EDA to understand data types, missing values, and summary statistics for each column. They need to do this quickly and without writing custom code. The scientist has access to AWS Glue DataBrew and Amazon SageMaker Data Wrangler. Which approach should the scientist take?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Use AWS Glue DataBrew to create a profile job that outputs data quality reports
AWS Glue DataBrew provides a visual interface for data profiling and can handle large datasets without writing code. It automatically detects data types, missing values, and summary statistics. Option B is wrong because SageMaker Data Wrangler requires more manual setup and coding. Option C is wrong because Athena requires SQL queries and is not a profiling tool. Option D is wrong because Glue ETL jobs require writing code.
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.
✗
Use Amazon SageMaker Data Wrangler to import the data and generate a report
Why it's wrong here
Data Wrangler requires more steps and coding; DataBrew is simpler for profiling.
✗
Use Amazon Athena to run SELECT statements on each column
Why it's wrong here
Athena is for querying, not automated profiling; writing queries for 500 columns is tedious.
✓
Use AWS Glue DataBrew to create a profile job that outputs data quality reports
Why this is correct
DataBrew's profile job automatically computes statistics and detects missing values.
Related concept
Read the scenario before looking for a memorised answer.
✗
Use AWS Glue ETL jobs with PySpark to compute statistics
Why it's wrong here
Writing custom PySpark code is not the quickest or code-free approach.
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 — 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: Use AWS Glue DataBrew to create a profile job that outputs data quality reports — AWS Glue DataBrew provides a visual interface for data profiling and can handle large datasets without writing code. It automatically detects data types, missing values, and summary statistics. Option B is wrong because SageMaker Data Wrangler requires more manual setup and coding. Option C is wrong because Athena requires SQL queries and is not a profiling tool. Option D is wrong because Glue ETL jobs require writing code.
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.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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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.
Question Discussion
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