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Answer: Benefits: Improved query performance and reduced data storage requirements. Drawbacks: Possible loss of valuable data and constrained data analysis capabilities.
In the context of optimizing a data warehouse for a retail company, data filtering can significantly improve query performance and reduce storage requirements by focusing on relevant data subsets. However, this approach may inadvertently exclude potentially valuable data, limiting the depth of analysis possible. The correct answer highlights these key trade-offs, aligning with the company's priorities and concerns.
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As a Microsoft Fabric Analytics Engineer Associate, you are tasked with optimizing a data warehouse solution for a retail company. The company wants to improve query performance and reduce storage costs by implementing data filtering. However, they are concerned about the potential impact on data analysis capabilities and the risk of losing valuable insights. Considering these constraints, which of the following best describes the potential benefits and drawbacks of implementing data filtering in this scenario? (Choose one option.)
A
Benefits: Enhanced data security and faster data access times. Drawbacks: Higher complexity in managing data updates and increased risk of data duplication.
B
Benefits: Greater data granularity and enhanced analysis capabilities. Drawbacks: Elevated storage costs and potential for data redundancy and inconsistencies.
C
Benefits: Streamlined data model and easier management of data updates. Drawbacks: Increased storage needs and degraded query performance.
D
Benefits: Improved query performance and reduced data storage requirements. Drawbacks: Possible loss of valuable data and constrained data analysis capabilities.
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