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Exploratory Data Analysis →hardMultiple ChoiceObjective-mapped

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 on a predictive maintenance project for a manufacturing company. Sensor data is collected every second from 100 machines and stored in an Amazon S3 bucket as Parquet files, partitioned by machine_id and date. The dataset is massive (10 TB) and contains over 2000 features per machine. The data scientist needs to perform exploratory data analysis to identify which features are most predictive of machine failure. They have access to Amazon SageMaker Studio with a SageMaker Data Wrangler flow. The initial data exploration is taking too long due to the volume of data. The data scientist wants to speed up the analysis without losing accuracy in feature selection. Which course of action is most appropriate?

A
Switch to using Amazon EMR with Spark to perform distributed feature selection on the full dataset
Why wrong: Incorrect: This is a valid approach but is more complex and time-consuming than sampling.
B
Reduce the data to a single partition by concatenating all files and use only one machine's data
Why wrong: Incorrect: This loses cross-machine variability and may bias feature selection.
C
Use SageMaker Data Wrangler to create a stratified sample by machine_id and date, then analyze the sample
Correct: Stratified sampling preserves distribution of key variables and reduces data size.
D
Use Amazon Athena to query a random sample of rows from the dataset
Why wrong: Incorrect: Random sampling may break time series dependencies and is not stratified.

Full breakdown with real-world context →

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

✓

Use SageMaker Data Wrangler to create a stratified sample by machine_id and date, then analyze the sample

Option C is correct because SageMaker Data Wrangler supports stratified sampling, which preserves the distribution of machine failure across machine_id and date, allowing for faster exploratory data analysis while maintaining representativeness for feature selection. Option A is incorrect because distributed processing with EMR on the full dataset may still be slow and is unnecessary when sampling can capture the signal. Option B is incorrect because using only one machine's data loses cross-machine variability and may bias feature selection. Option D is incorrect because random sampling does not guarantee preservation of time series order or failure distribution, potentially compromising analysis accuracy.

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.

✗
Switch to using Amazon EMR with Spark to perform distributed feature selection on the full dataset
Why it's wrong here
Incorrect: This is a valid approach but is more complex and time-consuming than sampling.
✗
Reduce the data to a single partition by concatenating all files and use only one machine's data
Why it's wrong here
Incorrect: This loses cross-machine variability and may bias feature selection.
✓
Use SageMaker Data Wrangler to create a stratified sample by machine_id and date, then analyze the sample
Why this is correct
Correct: Stratified sampling preserves distribution of key variables and reduces data size.
Related concept
Read the scenario before looking for a memorised answer.
✗
Use Amazon Athena to query a random sample of rows from the dataset
Why it's wrong here
Incorrect: Random sampling may break time series dependencies and is not stratified.

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.

Quick reference

AWS S3 Storage Class Comparison

Storage Class	Min Duration	Retrieval	Use Case
S3 Standard	None	Immediate	Frequently accessed data
S3 Standard-IA	30 days	Immediate	Infrequent access, rapid retrieval
S3 One Zone-IA	30 days	Immediate	Non-critical infrequent data
S3 Intelligent-Tiering	None	Immediate–hours	Unknown or changing access patterns
S3 Glacier Instant	90 days	Milliseconds	Archive with instant retrieval
S3 Glacier Flexible	90 days	Minutes–hours	Archive, flexible retrieval
S3 Glacier Deep Archive	180 days	Hours	Long-term compliance archive

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.

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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: Use SageMaker Data Wrangler to create a stratified sample by machine_id and date, then analyze the sample — Option C is correct because SageMaker Data Wrangler supports stratified sampling, which preserves the distribution of machine failure across machine_id and date, allowing for faster exploratory data analysis while maintaining representativeness for feature selection. Option A is incorrect because distributed processing with EMR on the full dataset may still be slow and is unnecessary when sampling can capture the signal. Option B is incorrect because using only one machine's data loses cross-machine variability and may bias feature selection. Option D is incorrect because random sampling does not guarantee preservation of time series order or failure distribution, potentially compromising analysis accuracy.

What should I do if I get this MLS-C01 question wrong?

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.