Question 40
Domain 4: Design Cost-Optimized ArchitecturesA company wants data to be handled as close as possible to where it is generated in order to reduce the need to send all processing to a centralized location. Which distributed compute strategy best fits this requirement?
Correct answer: A
Explanation
Edge processing performs computation near the data source rather than relying on a centralized processing location. When the goal is to minimize centralized handling by processing data locally, edge processing is the appropriate strategy. — solutions-architect-associate-03-domain4.md
Why each option is right or wrong
A. Use edge processing so computation occurs near the point where the data is created.
The requirement is to process data as close as possible to where it is generated instead of sending all work to a centralized location. The source material identifies edge processing as the distributed compute strategy for that purpose.
B. Use centralized processing so computation is consolidated in a single primary location.
Centralized processing places computation in one location rather than near the data source.
C. Use delayed batch processing so data is collected first and processed later in larger groups.
Batch processing describes processing later in groups, not processing near where data originates.
D. Use a shared storage strategy so data remains in one repository before any compute occurs.
A storage approach does not identify the compute strategy of processing at the data source.