DEA-C01 · Question #279
A company is creating a new data pipeline to populate a data lake. A data analyst needs to prepare and standardize the data before a data engineering team can perform advanced data transformations…
The correct answer is C. Use AWS Glue Studio with data preparation recipe transformations. Ensure that the data. AWS Glue Studio lets analysts build no-code/low-code visual ETL with built-in preparation transformations (recipe-style), producing Glue jobs that engineers can extend - minimizing coding and operational overhead.
Question
A company is creating a new data pipeline to populate a data lake. A data analyst needs to prepare and standardize the data before a data engineering team can perform advanced data transformations. The data analyst needs a solution to process the data that does not require writing new code. Which solution will meet these requirements with the LEAST operational effort?
Options
- AUse Python and Pandas in an AWS Glue Studio notebook. Ensure that the data engineers add
- BUse Amazon SageMaker Canvas and SageMaker Data Wrangler to write to a new dataset.
- CUse AWS Glue Studio with data preparation recipe transformations. Ensure that the data
- DCreate a document that includes the data preparation rules. Ensure that the data engineers
How the community answered
(35 responses)- A6% (2)
- B11% (4)
- C80% (28)
- D3% (1)
Explanation
AWS Glue Studio lets analysts build no-code/low-code visual ETL with built-in preparation transformations (recipe-style), producing Glue jobs that engineers can extend - minimizing coding and operational overhead.
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