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Answer: Implement a combined sub-partitioning strategy using both customer segment and transaction type to optimize for a broader range of query scenarios.
Option D is the most effective because it leverages multiple dimensions (customer segment and transaction type) for sub-partitioning, thereby optimizing query performance across a wider array of scenarios. This approach allows for more precise data filtering, reducing the amount of data scanned during queries, which is crucial for cost efficiency and scalability. While options A, B, and C focus on single dimensions, which may improve performance for specific queries, they lack the comprehensive optimization provided by combining multiple partitioning strategies.
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As a Microsoft Fabric Analytics Engineer working on a lakehouse project, you are tasked with optimizing the partitioning strategy for a large dataset containing customer transaction data, initially partitioned by date. The goal is to enhance query performance while considering cost efficiency and scalability. The dataset includes transactions from various customer segments (premium, regular, loyalty), across different transaction types (online, in-store, mobile), and product categories (electronics, clothing, groceries). Given these requirements, which of the following partitioning strategies would BEST meet the project's needs? Choose one option.
A
Sub-partition the data solely by customer segment to target marketing analysis more efficiently.
B
Sub-partition the data solely by transaction type to streamline transaction processing reports.
C
Sub-partition the data solely by product category to improve inventory management queries.
D
Implement a combined sub-partitioning strategy using both customer segment and transaction type to optimize for a broader range of query scenarios.
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