MLS-C01 Exploratory Data Analysis Practice Question
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?
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.
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.
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.
- ✗
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.
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 |
Go deeper
Related to this question
About these practice questions
This MLS-C01 question is part of Courseiva's 1,672-question bank — original exam-style content with full explanations and wrong-answer analysis, never real exam questions or exam dumps. Learn why practice questions differ from exam dumps →
JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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.