A data scientist is analyzing a dataset with missing values in several features. The dataset is large (10 million rows) and stored in an S3 bucket as CSV files. The scientist wants to use AWS Glue to catalog the data and then use Amazon Athena to query it. However, the missing values are causing errors in downstream machine learning models. Which approach should the scientist take to handle missing values during exploratory data analysis?

Question

Accepted Answer

Use AWS Glue ETL jobs with a custom transformation script that uses the AWS Glue library to drop or impute missing values before writing to a new dataset.. AWS Glue ETL jobs can be used with custom scripts to handle missing values by either dropping rows or imputing values using built-in transforms or custom logic. This is ideal for large-scale datasets stored in S3 as CSV files. Glue integrates with the AWS Glue library for transforming data.

Option A (SageMaker Data Wrangler) is more suitable for interactive data preparation and visualization, but not for automated, large-scale ETL processing of 10 million rows.
Option C (Redshift Spectrum) is primarily a query engine that can query data in S3, but it does not provide built-in data cleaning capabilities for missing values; you would need to use SQL functions like COALESCE, but it's not the best approach for comprehensive ETL.
Option D (Athena) is also a query engine and cannot modify the underlying data; it can impute values in query results, but not write transformed data back to S3 as a cleaned dataset without additional steps.

Answer

Use Amazon SageMaker Data Wrangler to create a data flow that imputes missing values and export the transformed dataset to S3.

Answer

Use Amazon Redshift Spectrum with an external table to query the data and use SQL COALESCE to handle missing values on the fly.

Answer

Use Amazon Athena to run SQL queries that impute missing values and write the results to a new table.

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

MLS-C01 Exploratory Data Analysis Practice Question

Why each option matters

Option-by-option breakdown

AWS S3 Storage Class Comparison

Question Discussion