MLA-C01 · Question #143
A company is using an Amazon S3 bucket to collect data that will be used for ML workflows. The company needs to use AWS Glue DataBrew to clean and normalize the data. Which solution will meet these…
The correct answer is B. Create a DataBrew dataset by using the S3 path. Clean and normalize the data by using a. The correct solution is to create a DataBrew dataset using the S3 path and then clean and normalize the data with a DataBrew recipe job. Recipes define and apply transformations to the data, while profile jobs are used only for data analysis and profiling, not cleaning.
Question
A company is using an Amazon S3 bucket to collect data that will be used for ML workflows. The company needs to use AWS Glue DataBrew to clean and normalize the data. Which solution will meet these requirements?
Options
- ACreate a DataBrew dataset by using the S3 path. Clean and normalize the data by using a
- BCreate a DataBrew dataset by using the S3 path. Clean and normalize the data by using a
- CCreate a DataBrew dataset by using a Java Database Connectivity (JDBC) driver to connect to
- DCreate a DataBrew dataset by using a Java Database Connectivity (JDBC) driver to connect to
How the community answered
(39 responses)- A5% (2)
- B92% (36)
- C3% (1)
Explanation
The correct solution is to create a DataBrew dataset using the S3 path and then clean and normalize the data with a DataBrew recipe job. Recipes define and apply transformations to the data, while profile jobs are used only for data analysis and profiling, not cleaning.
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