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Answer: Create aggregations at the individual table level for each data type and region, then combine them into a single composite model to maintain relationships and improve query performance.
Creating aggregations at the individual table level for each data type and region, then combining them into a single composite model (Option C) is the best practice. This approach ensures that the model maintains data relationships, improves query performance, and supports scalability. Consolidating all data into a single large composite model (Option A) may lead to performance degradation and complexity. Designing separate composite models for each region and data type (Option B) can introduce redundancy and complicate data management. Avoiding aggregations (Option D) would not meet the requirement for quick query performance and scalability.
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As a Microsoft Fabric Analytics Engineer Associate, you are tasked with designing and building composite models that include aggregations for a large e-commerce company. The company has a vast amount of data on product sales, customer information, and inventory levels spread across multiple regions. The solution must ensure quick query performance, maintain data accuracy, and support scalability for future data growth. Considering these requirements, which of the following best practices should you follow when designing composite models with aggregations? (Choose one option.)
A
Consolidate all data into a single large composite model to simplify queries and reduce complexity.
B
Design separate composite models for each region and data type, then use a master model to combine them, ensuring regional data isolation and performance optimization.
C
Create aggregations at the individual table level for each data type and region, then combine them into a single composite model to maintain relationships and improve query performance.
D
Avoid using aggregations and rely solely on direct querying of the raw data to ensure data accuracy and freshness.