What are two functionalities of edge data services? (Choose two.)

Question

Options

• Acreating a machine learning data model
• Bsupporting many interfaces and APIs
• Capplying advanced data analytics
• Dfiltering, normalizing and aggregating data
• Esaving data for a prolonged time period

• A
  3% (1)
• C
  3% (1)
• D
  93% (28)

Explanation

Edge data services operate close to the source of data generation, where two core functions are filtering/normalizing/aggregating data (D) and saving data for prolonged periods (E). Filtering and aggregation reduce the volume of raw data before it's sent upstream, while local storage allows edge nodes to retain data when connectivity is intermittent or when historical context is needed on-site.

Why the distractors are wrong:

• A (ML model creation) - Training machine learning models is computationally intensive and typically done in centralized cloud or data center environments, not at the edge.
• B (supporting many interfaces/APIs) - This describes a general integration platform or API gateway, not a specific function of edge data services.
• C (advanced analytics) - Deep analytics (e.g., complex queries, BI workloads) require significant compute and are handled in the cloud or core data centers; edge services do lightweight processing, not advanced analytics.

Memory tip: Think of the edge as a smart filter and temporary vault - it cleans/condenses data before sending it up (D) and holds onto it locally when needed (E). Everything else (ML, heavy analytics, rich APIs) lives in the cloud.

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