Amazon Web Services Data-Engineer-Associate Exam With Confidence Using Practice Dumps

Option C (LAC-A) is the correct choice because the requirement is to always show the Region-level total even when the visual is drilled down into lower levels (for example, country → city → store). A simple calculated field (Option B) is computed at the row level and then aggregated by the visual, so it will change as the drill-down changes the grain. A table calculation (Option A) is evaluated based on the current visual layout and can vary with the fields placed in the visual, which makes it unreliable for enforcing a fixed “Region total regardless of drill-down.”

A level-aware calculation (aggregate) is specifically intended to “lock” an aggregation to a chosen dimensional level (here: Region). That means you can compute revenue aggregated at the Region level and reuse that value across lower drill levels without it recalculating at city/store granularity. A window LAC (LAC-W) is primarily for windowed analytics (running totals, period-over-period, rank, moving averages) over a partition/order, not for enforcing a fixed dimensional aggregation level. Therefore, LAC-A best matches the requirement.

The requirement involves transforming CSV files by renaming columns, removing rows, and other operations with minimal development effort. AWS Glue DataBrew is the best solution here because it allows you to visually create transformation recipes without writing extensive code.

Option D: Use AWS Glue DataBrew recipes to read and transform the CSV files.DataBrew provides a visual interface where you can build transformation steps (e.g., renaming columns, filtering rows, creating new columns, etc.) as a " recipe " that can be applied to datasets, making it easy to handle complex transformations on CSV files with minimal coding.

Other options (A, B, C) involve more manual development and configuration effort (e.g., writing Python jobs or creating custom workflows in Glue) compared to the low-code/no-code approach of DataBrew.