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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. 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 team is using Amazon SageMaker Data Wrangler to perform exploratory data analysis on a large dataset stored in S3. The dataset contains missing values, outliers, and categorical variables with high cardinality. The team wants to understand data distributions and relationships before modeling. Which combination of Data Wrangler features should they use?

A
Generate a data quality report, view histograms, and create scatter plots for selected features.
Data quality report provides summary statistics and missing values; histograms and scatter plots show distributions and relationships.
B
Drop rows with missing values and visualize box plots for numerical features.
Why wrong: Dropping rows may introduce bias; initial EDA should include missing value analysis.
C
Use imputation to handle missing values and one-hot encoding for categorical features.
Why wrong: These are data preparation steps, not EDA.
D
Generate a data quality report and a correlation heatmap.
Why wrong: Data Wrangler does not produce correlation heatmaps natively.

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

✓

Generate a data quality report, view histograms, and create scatter plots for selected features.

Option A is correct because SageMaker Data Wrangler provides built-in features for exploratory data analysis, including data quality reports (with summary statistics and missing value analysis), histograms for distribution visualization, and scatter plots to explore relationships between features. These are ideal for understanding distributions and correlations early in the pipeline. Option B is incorrect because dropping rows is a data cleaning transformation, not an EDA step, and box plots alone are insufficient for understanding relationships. Option C is incorrect because imputation and one-hot encoding are data preparation transformations applied after EDA. Option D is incorrect because while Data Wrangler generates a data quality report, it does not directly include correlation heatmaps; scatter plots (as in A) are a more direct way to assess relationships.

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.

✓
Generate a data quality report, view histograms, and create scatter plots for selected features.
Why this is correct
Data quality report provides summary statistics and missing values; histograms and scatter plots show distributions and relationships.
Related concept
Read the scenario before looking for a memorised answer.
✗
Drop rows with missing values and visualize box plots for numerical features.
Why it's wrong here
Dropping rows may introduce bias; initial EDA should include missing value analysis.
✗
Use imputation to handle missing values and one-hot encoding for categorical features.
Why it's wrong here
These are data preparation steps, not EDA.
✗
Generate a data quality report and a correlation heatmap.
Why it's wrong here
Data Wrangler does not produce correlation heatmaps natively.

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: Generate a data quality report, view histograms, and create scatter plots for selected features. — Option A is correct because SageMaker Data Wrangler provides built-in features for exploratory data analysis, including data quality reports (with summary statistics and missing value analysis), histograms for distribution visualization, and scatter plots to explore relationships between features. These are ideal for understanding distributions and correlations early in the pipeline. Option B is incorrect because dropping rows is a data cleaning transformation, not an EDA step, and box plots alone are insufficient for understanding relationships. Option C is incorrect because imputation and one-hot encoding are data preparation transformations applied after EDA. Option D is incorrect because while Data Wrangler generates a data quality report, it does not directly include correlation heatmaps; scatter plots (as in A) are a more direct way to assess relationships.

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.