Given a Customer Scenario, Recommend Approaches and Techniques for Consolidating Data Attributes from Multiple Sources. Discuss Criteria and Methodology for Picking the Winning Attributes (data Survivorship Rules). DATA-ARCHITECT Practice Questions (Page 4)

Questions 16–20

1. 16foundation · easy

   Two source systems provide different values for a customer's annual revenue. The data architect decides to calculate the average of the two values and use that as the master value. Which conflict resolution technique is being applied?

   • AMerging
   • BAveraging
   • CFlagging for review
   • DSource priority
   Reveal Solution Discussion Select an answer first
2. 17foundation · easy

   A data architect encounters conflicting values for a customer's job title across two sources. The architect decides to keep both values in a single field, separated by a comma, to preserve all information. Which conflict resolution technique is this?

   • AAveraging
   • BMerging
   • CFlagging for review
   • DSource priority
   Reveal Solution Discussion Select an answer first
4. 18foundation · easy

   A data architect is analyzing two source systems that both provide a customer's phone number. The architect wants to determine which source is more reliable for this attribute. Which factor is most relevant to this analysis?

   • AThe age of the source system's hardware
   • BThe completeness of the phone number field in each source
   • CThe number of users who access the source system
   • DThe color scheme of the source system's user interface
   Reveal Solution Discussion Select an answer first
5. 19application · medium

   A media company needs to consolidate viewer data from a streaming platform and a marketing automation tool. The streaming platform has accurate viewing history, while the marketing tool has email engagement data. The company wants a unified view for campaign targeting, and the data is needed in near real-time. Which consolidation approach is most appropriate?

   • AData virtualization to create a real-time unified view
   • BELT into a data lake with hourly loads
   • CManual export and merge in spreadsheets
   • DETL into a data warehouse with nightly batches
   Reveal Solution Discussion Select an answer first
6. 20expert · hard

   A logistics company needs to consolidate shipment tracking data from multiple carriers into a single operational dashboard. The data is high-volume, arrives in different formats, and must be available with minimal latency for live tracking. The company also needs historical data for analytics. Which consolidation approach best meets these needs?

   • AUse ETL to transform and load into a data warehouse with batch updates
   • BUse data virtualization to query carrier APIs in real time
   • CUse manual file uploads from carriers into a shared folder
   • DUse ELT to load raw data into a data lake, then transform for analytics and serve live views via a separate streaming path
   Reveal Solution Discussion Select an answer first