A company has an Amazon S3 bucket that contains 1 ТВ of files from different sources. The S3 bucket contains the following file types in the same S3 folder: CSV, JSON, XLSX, and Apache Parquet. An…

Question

Options

• AUse DataBrew to process the existing S3 folder. Store the output in Apache Parquet format.
• BUse DataBrew to process the existing S3 folder. Store the output in AWS Glue Parquet format.
• CSeparate the data into a different folder for each file type. Use DataBrew to process each folder
• DSeparate the data into a different folder for each file type. Use DataBrew to process each folder

• A
  3% (1)
• B
  10% (3)
• C
  84% (26)
• D
  3% (1)

Explanation

Option C is correct because AWS Glue DataBrew requires a dataset to consist of a single file type - it cannot process a folder containing mixed formats (CSV, JSON, XLSX, Parquet) in a single job. By separating files into type-specific folders first, the ML engineer can create a valid DataBrew dataset for each format and process them individually.

Options A and B fail for the same root reason: both attempt to run DataBrew directly against the mixed-format folder, which DataBrew does not support - it will error when it encounters inconsistent file types in a single dataset connection. The distinction between A (Apache Parquet) and B (AWS Glue Parquet) is a distractor; neither can even reach the output step with mixed inputs.

Option D appears identical to C in the question as presented, but in the actual exam it likely differs in output format or configuration detail - a common exam pattern where one answer stores output in a format AWS Glue cannot efficiently consume (e.g., XLSX), making C the better end-to-end solution since Parquet is the preferred format for downstream AWS Glue consumption.

Memory tip: Think "DataBrew = one type per folder" - like a picky eater who won't eat a plate with mixed foods. Separate first, process second, output to Parquet for Glue.

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