MLS-C01 Exploratory Data Analysis Practice Question
A data scientist is analyzing a dataset with a large number of missing values in several columns. The dataset is stored in an Amazon S3 bucket and is about 5 TB in size. The scientist wants to understand the pattern of missingness (e.g., is it missing completely at random, missing at random, or not missing at random) before deciding on an imputation strategy. The scientist has access to AWS Glue DataBrew and Amazon SageMaker Studio. Which approach should the scientist take to best understand the missing data patterns?
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 AWS Glue DataBrew's data quality and missing data reports
AWS Glue DataBrew provides built-in missing data reports that include visualizations such as heatmaps and bar charts to identify patterns of missingness and help determine whether data is MCAR, MAR, or NMAR. Option A is incorrect because SageMaker Data Wrangler, while useful for data preparation, does not have native missingness pattern analysis. Option C is incorrect because AWS Glue ETL jobs require custom PySpark code and are less efficient for exploratory analysis compared to DataBrew's automated reports. Option D is incorrect because while Amazon Athena can query missing values, it lacks pattern analysis capabilities.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Use Amazon SageMaker Data Wrangler to create a flow and analyze missingness visually
Why it's wrong here
Data Wrangler does not have specific missingness pattern analysis; it's more for transformations.
- ✓
Use AWS Glue DataBrew's data quality and missing data reports
Why this is correct
DataBrew's reports visualize missing data patterns and correlations.
- ✗
Use AWS Glue ETL jobs with PySpark to compute missingness statistics
Why it's wrong here
Custom coding is required and not as straightforward as DataBrew's built-in reports.
- ✗
Use Amazon Athena to run queries to find missing values per column
Why it's wrong here
Athena can find counts but not patterns of missingness.
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 |
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