The answer is to use Amazon SageMaker Data Wrangler to create a data flow that samples and profiles the data. This is correct because Data Wrangler operates within SageMaker Studio and processes large datasets in a distributed, memory-efficient manner, allowing you to perform exploratory data analysis (EDA) on a 100 GB CSV without loading the entire file into a pandas DataFrame. It handles out-of-memory issues by automatically sampling and profiling column distributions, missing values, and outliers directly from Amazon S3. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this question tests your understanding of SageMaker’s native tools for large-scale EDA versus costly or complex alternatives. A common trap is choosing to increase the instance size, which is expensive and may still fail, or using AWS Glue ETL, which is less integrated with Studio. Remember the memory tip: “Data Wrangler wrangles without strangling memory—sample first, profile fast.”
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 analyzing a dataset stored in Amazon S3 (100 GB, CSV format) using Amazon SageMaker Studio. The dataset contains 500 columns and 10 million rows. The data scientist wants to understand the distribution of each column, detect missing values, and identify outliers. However, the SageMaker Studio notebook instance runs out of memory when loading the entire dataset into a pandas DataFrame. The data scientist needs to complete the EDA efficiently without modifying the source data. What should the data scientist do?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Use Amazon SageMaker Data Wrangler to create a data flow that samples and profiles the data.
Using SageMaker Data Wrangler allows processing data in a distributed manner without loading everything into memory. Option A is wrong because increasing instance size may still not be enough and is costly. Option B is wrong because Glue ETL is more complex and not integrated with Studio. Option D is wrong because sampling may miss important data.
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.
✗
Write a script that loads only a random 10% sample of rows to reduce memory usage.
Why it's wrong here
Sampling may miss outliers and patterns.
✗
Use AWS Glue ETL to transform the data into Parquet format and then load into pandas.
Why it's wrong here
Complex and not necessary.
✗
Launch a larger notebook instance with more memory (e.g., ml.r5.24xlarge) and reload the data.
Why it's wrong here
May still fail and is costly.
✓
Use Amazon SageMaker Data Wrangler to create a data flow that samples and profiles the data.
Why this is correct
Data Wrangler can handle large datasets efficiently.
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 startup's cloud architect reviews their monthly bill and notices costs are higher than expected for a long-running batch job. Switching from on-demand instances to Reserved Instances — or using Spot/Preemptible VMs — can reduce compute costs by up to 72 %. Questions like this test whether you understand the tradeoffs between commitment, flexibility, and cost across cloud pricing models.
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 Amazon SageMaker Data Wrangler to create a data flow that samples and profiles the data. — Using SageMaker Data Wrangler allows processing data in a distributed manner without loading everything into memory. Option A is wrong because increasing instance size may still not be enough and is costly. Option B is wrong because Glue ETL is more complex and not integrated with Studio. Option D is wrong because sampling may miss important data.
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. A data analyst is using Amazon SageMaker Studio to perform exploratory data analysis on a dataset stored in S3. The analyst wants to generate summary statistics and visualizations quickly. Which built-in feature of SageMaker Studio should the analyst use?
easy
A.SageMaker Ground Truth
✓ B.SageMaker Data Wrangler
C.SageMaker Autopilot
D.SageMaker Clarify
Why B: Option C is correct because SageMaker Data Wrangler is a visual data preparation tool integrated into SageMaker Studio that provides summary statistics, histograms, and correlation matrices without code. Option A is wrong because SageMaker Autopilot automates model building, not EDA. Option B is wrong because SageMaker Clarify is for bias detection and explainability. Option D is wrong because SageMaker Ground Truth is for labeling.
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.
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