The correct answer is to use Data Wrangler’s built-in visualizations for quick analysis and to export the flow as a Jupyter notebook for sharing with the team. These two practices directly support the iterative, collaborative nature of exploratory data analysis (EDA) by enabling rapid insight generation through native charts and ensuring full reproducibility of transformations in a familiar notebook environment. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this question tests your understanding of how Data Wrangler fits into the ML pipeline—specifically that it is designed for visual, code-free EDA, not for heavy lifting like EMR processing or ad-hoc querying with Athena. A common trap is confusing Data Wrangler’s export capabilities with external services like QuickSight for visualization or Athena for SQL-based exploration; remember that Data Wrangler’s strength is its seamless integration with SageMaker Studio and its ability to produce a shareable, version-controlled notebook. Memory tip: think “Visualize and Export” for EDA—Data Wrangler gives you the quick look, and the notebook gives your team the reproducible path.
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.
Which TWO of the following are best practices for exploratory data analysis when using Amazon SageMaker Data Wrangler? (Select TWO.)
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 the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Use Data Wrangler's built-in data visualizations to explore feature distributions and relationships.
Using Data Wrangler's built-in visualizations for quick analysis and exporting the flow as a Jupyter notebook for reproducibility are best practices. Option B (QuickSight) is separate. Option C (EMR) is not needed. Option D (Athena) is for queries, not for building into pipeline.
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.
✗
Store all intermediate results in Amazon Athena for querying.
Why it's wrong here
Athena is not needed for intermediate results; Data Wrangler manages state.
✓
Use Data Wrangler's built-in data visualizations to explore feature distributions and relationships.
Why this is correct
Built-in visualizations enable quick EDA.
Clue confirmation
The clue word "best" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
✗
Use Amazon EMR to run Spark jobs for data profiling.
Why it's wrong here
Data Wrangler provides profiling without needing EMR.
✗
Always export the data to Amazon QuickSight for analysis before transformation.
Why it's wrong here
QuickSight is not necessary; Data Wrangler has its own analysis tools.
✓
Export the Data Wrangler flow as a Jupyter notebook to share with the team.
Why this is correct
Exporting as a notebook promotes reproducibility.
Clue confirmation
The clue word "best" in the question point toward this answer.
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 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. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. 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.
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 Data Wrangler's built-in data visualizations to explore feature distributions and relationships. — Using Data Wrangler's built-in visualizations for quick analysis and exporting the flow as a Jupyter notebook for reproducibility are best practices. Option B (QuickSight) is separate. Option C (EMR) is not needed. Option D (Athena) is for queries, not for building into pipeline.
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.
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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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