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Answer: Implement a staging area strategy to partition the data into manageable chunks, enabling parallel processing and reducing load times.
Implementing a staging area to break down the data into smaller chunks (C) is the most effective and cost-efficient solution. It directly addresses the performance bottleneck by enabling parallel processing and reducing the load on the system, aligning with the organization's priorities of cost-effectiveness and scalability. Scaling up the dataflow cluster (A) may improve performance but at a higher operational cost, which contradicts the organization's emphasis on cost-effectiveness. Adding more indexes to the source database (B) might improve query performance but does not necessarily resolve the data loading bottleneck. Migrating to a premium data source (D) could offer better performance but at a significant cost, making it less aligned with the organization's priorities.
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As a Microsoft Fabric Analytics Engineer Associate, you are optimizing a dataflow in Azure Data Factory that processes a large volume of data, leading to significant delays. The organization emphasizes cost-effectiveness and scalability in its solutions. Which of the following approaches would BEST address the performance bottlenecks while aligning with the organization's priorities? (Choose one option.)
A
Scale up the dataflow cluster by increasing the number of nodes, despite the higher operational costs.
B
Enhance the source database by adding more indexes, assuming it will automatically improve data loading times.
C
Implement a staging area strategy to partition the data into manageable chunks, enabling parallel processing and reducing load times.
D
Migrate to a premium data source with higher performance capabilities, disregarding the potential cost implications.