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CERTIFIED-DATA-ENGINEER-PROFESSIONAL · Question #111

The marketing team is looking to share data in an aggregate table with the sales organization, but the field names used by the teams do not match, and a number of marketing specific fields have not be

The correct answer is A. Create a view on the marketing table selecting only these fields approved for the sales team alias. Creating a view on the marketing table is the simplest solution. A view can SELECT only the approved columns and use aliases to rename them to match the sales team's naming conventions-no data is duplicated, no additional pipeline complexity is added, and access can be controlled

Data Access and Security

Question

The marketing team is looking to share data in an aggregate table with the sales organization, but the field names used by the teams do not match, and a number of marketing specific fields have not been approval for the sales org. Which of the following solutions addresses the situation while emphasizing simplicity?

Options

  • ACreate a view on the marketing table selecting only these fields approved for the sales team alias
  • BUse a CTAS statement to create a derivative table from the marketing table configure a
  • CAdd a parallel table write to the current production pipeline, updating a new sales table that varies
  • DCreate a new table with the required schema and use Delta Lake's DEEP CLONE functionality to
  • EInstruct the marketing team to download results as a CSV and email them to the sales

How the community answered

(15 responses)
  • A
    93% (14)
  • C
    7% (1)

Explanation

Creating a view on the marketing table is the simplest solution. A view can SELECT only the approved columns and use aliases to rename them to match the sales team's naming conventions-no data is duplicated, no additional pipeline complexity is added, and access can be controlled via ACLs on the view. Option B (CTAS) creates a physical copy of the data, adding storage cost and synchronization complexity. Option C requires modifying the production pipeline. Option D (DEEP CLONE) also duplicates data and must be kept in sync. Option E is an unsustainable manual process.

Topics

#SQL Views#Data Access Control#Data Transformation#Data Governance

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