The answer is to use SageMaker Data Wrangler to import the data and generate a data quality report. This is the most efficient approach because Data Wrangler provides a visual, no-code interface for data profiling, automatically computing summary statistics like count, mean, standard deviation, min, and max for numerical columns, as well as detecting missing values directly from CSV files in Amazon S3. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this question tests your understanding of when to leverage SageMaker’s built-in tools versus more complex solutions; a common trap is choosing AWS Glue Crawler, which only catalogs schema and partitions, not statistical summaries, or writing a Spark job, which is overkill for quick profiling. Remember the memory tip: “Data Wrangler profiles without code—Glue Crawler only knows the road.”
MLS-C01 Exploratory Data Analysis Practice Question
This MLS-C01 practice question tests your understanding of exploratory data analysis. 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 starting a new machine learning project and needs to understand the dataset. The dataset is stored as CSV files in Amazon S3, with a total size of 50 GB. The data scientist wants to quickly get summary statistics (count, mean, standard deviation, min, max) for each numerical column, and also check for missing values. The data scientist has access to SageMaker Studio. What is the most efficient way to achieve this?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Use SageMaker Data Wrangler to import the data and generate a data quality report.
SageMaker Data Wrangler can profile the data without writing code. Option A is wrong because Glue Crawler creates a schema but not statistics. Option B is wrong because writing a Spark job is overkill. Option D is wrong because QuickSight requires data import.
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 AWS Glue Crawler to infer schema and then query with Athena.
Why it's wrong here
Crawler does not compute statistics.
✗
Write a PySpark script in a SageMaker notebook to compute statistics.
Why it's wrong here
More work than necessary.
✗
Load a sample into Amazon QuickSight and use SPICE to compute statistics.
Why it's wrong here
Requires importing data into QuickSight.
✓
Use SageMaker Data Wrangler to import the data and generate a data quality report.
Why this is correct
Data Wrangler provides summary statistics and missing value analysis.
Related concept
Read the scenario before looking for a memorised answer.
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 SageMaker Data Wrangler to import the data and generate a data quality report. — SageMaker Data Wrangler can profile the data without writing code. Option A is wrong because Glue Crawler creates a schema but not statistics. Option B is wrong because writing a Spark job is overkill. Option D is wrong because QuickSight requires data import.
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.
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 →
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. Which AWS service can be used to generate a data profile (including histograms, correlations, and statistics) for a dataset stored in Amazon S3 without writing code?
easy
A.Amazon QuickSight
B.AWS Glue DataBrew
C.Amazon Athena
✓ D.Amazon SageMaker Data Wrangler
Why D: Option D is correct because Amazon SageMaker Data Wrangler provides a visual interface to create data profiles. Option A (QuickSight) is for visualization, not profiling; Option B (Glue DataBrew) also profiles but Data Wrangler is more integrated with SageMaker; Option C (Athena) is for querying.
Last reviewed: Jun 20, 2026
Question Discussion
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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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