DEA-C01 practice question: Data preparation with DataBrew and SageMaker
Data Operations and Support A free question from our original DEA-C01 practice exam. Try it, then open the answer to see why each option is right or wrong.
A public library's analysts receive patron-visit exports from 30 branches, and the files have inconsistent date formats, duplicate barcodes and blank branch codes. The analysts know SQL but do not write Python, and the cleaned data must refresh every Monday before the weekly report.
The data team wants the cleaning steps to be visible and reusable, and it wants the cleaned output written to S3 without manual runs.
Which approach meets these requirements?
Choose one answer.
AA. Ask an engineer to write a Python notebook each week that cleans the files, and have the analysts run the notebook by hand on Monday.
BB. Write a set of Athena views that clean the files on each query, and have the report read the views directly.
CC. Build the cleaning steps as a DataBrew recipe, and schedule a recipe job that writes the output to S3 every Monday.
DD. Load the raw files into Amazon Quick Sight and fix the duplicates and blank codes in its dashboard fields before each report.
Show the answer and explanation
Answer: C
A. Wrong: A weekly notebook depends on an engineer and a manual run, and the analysts do not write Python. The option misses the requirement that the refresh happen without manual work.
B. Wrong: Views clean the data on every query, which repeats the work and makes the cleaning steps hard to inspect. The option does not produce a cleaned output in S3 for the report.
C. Right: Correct. DataBrew lets analysts build the cleaning steps visually, saves them as a recipe, and runs them as a recipe job. A schedule runs the job every Monday and writes the output to S3 without a manual run. Learn more: What is AWS Glue DataBrew? (opens in a new tab)
D. Wrong: Quick Sight, formerly QuickSight, is a visualization service, and fixing data inside a dashboard is manual and hard to reuse. The option does not write cleaned data to S3 on a schedule.
Explanation
DataBrew lets analysts without Python build cleaning steps as a recipe, then run that recipe on a schedule and write the output to S3. The steps stay visible and reusable across the weekly refresh. Engineer-run notebooks, on-query views and dashboard fixes each require manual work or do not produce a cleaned output.
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