DEA-C01 Data Ingestion and Transformation Practice Question

A data engineer is using AWS Glue DataBrew to clean a dataset stored in Amazon S3. The dataset contains a column with inconsistent date formats and another with trailing whitespace. The engineer wants to apply these transformations reproducibly and schedule the recipe to run daily. Which combination of steps should the engineer take?

⚠ Common exam trap

The trap here is assuming a DataBrew recipe can be attached to a Glue ETL job, when recipes are executed only through DataBrew jobs.

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

✓

Create a project, add the dataset, build a recipe with the appropriate transformation steps, publish the recipe, and create a job that runs the recipe on a schedule.

DataBrew workflows use projects to build recipes, which are published and then executed by jobs on a schedule. This captures the date-format and whitespace transformations reproducibly and runs them daily, matching the requirement exactly.

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 the DataBrew console to apply transformations, then export the results manually each day without creating a recipe or job.
Why it's wrong here
Manual exports are not reproducible and cannot be scheduled. The requirement is for reproducible transformations and a daily schedule, which manual exports do not provide. A recipe and job are needed to capture and automate the steps.
✗
Create a recipe, then attach it to a Glue ETL job as a transform step and schedule the Glue job using a trigger.
Why it's wrong here
While Glue ETL can consume curated data, DataBrew recipes are applied through DataBrew jobs, not as Glue ETL transform steps. Attaching a recipe to a Glue job is not a supported integration, so this method does not correctly implement the scheduled cleaning workflow.
✗
Create a Glue crawler to catalog the dataset, then use a Glue ETL job with custom PySpark code to apply the transformations on a schedule.
Why it's wrong here
This approach uses Glue ETL rather than DataBrew, and the scenario specifically asks for a DataBrew-based solution. While Glue ETL can perform the transformations, it does not use DataBrew recipes and requires custom code, which is not the intended DataBrew workflow.
✓
Create a project, add the dataset, build a recipe with the appropriate transformation steps, publish the recipe, and create a job that runs the recipe on a schedule.
Why this is correct
DataBrew projects are used to explore and build recipes interactively. Publishing the recipe captures the transformation steps, and a DataBrew job applies the recipe to the dataset on a defined schedule. This workflow is the intended way to perform reproducible, scheduled cleaning tasks in DataBrew.

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

DEA-C01 Data Ingestion and Transformation Practice Question

Why each option matters

Option-by-option breakdown

AWS S3 Storage Class Comparison

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