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Answer: Identify the downstream dependencies by reviewing the data lineage, create backups, collaborate with stakeholders, and test changes in a separate environment before deploying them to the production data warehouse.
The most comprehensive approach to minimize disruptions and ensure data integrity involves a multi-faceted strategy: identifying downstream dependencies through data lineage review ensures awareness of all potential impacts; creating backups safeguards against data loss; collaborating with stakeholders gathers valuable insights into potential impacts; and testing changes in a separate environment allows for the identification and resolution of issues before they affect production. This approach balances thoroughness with practicality, adhering to best practices for change management in data environments.
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As a Microsoft Fabric Analytics Engineer Associate, you are tasked with performing an impact analysis of downstream dependencies from various data sources before implementing changes to a data warehouse. The organization prioritizes minimizing disruption to dataflows and semantic models, ensuring data integrity, and maintaining compliance with data governance policies. Given these constraints, which of the following steps is the MOST comprehensive approach to analyze the impact of changes and minimize potential disruptions? (Choose the best single option.)
A
Identify the downstream dependencies by reviewing the data lineage and relationships between the data warehouse, dataflows, and semantic models, but without testing changes in a separate environment to save time.
B
Create a backup of the data warehouse, dataflows, and semantic models before making any changes, and proceed directly to implement changes in the production environment to meet tight deadlines.
C
Notify the relevant stakeholders and team members of the planned changes, and rely solely on their feedback to understand the potential impact on downstream dependencies without reviewing data lineage.
D
Identify the downstream dependencies by reviewing the data lineage, create backups, collaborate with stakeholders, and test changes in a separate environment before deploying them to the production data warehouse.
E
All of the above