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Answer: Implement a queuing mechanism to serialize the write operations, reducing contention and lock contention without significantly increasing costs.
Implementing a queuing mechanism to serialize the write operations (B) is the best approach because it directly addresses the issue of concurrent writes by reducing contention and lock contention, which improves performance without significantly increasing costs. Increasing the batch size (A) might reduce the number of operations but could introduce latency. The 'overwrite' option (C) could lead to data loss and higher compute costs, making it less ideal. While the 'upsert' operation (D) reduces the volume of data written, it doesn't fully solve the problem of concurrent writes, making it a less optimal solution compared to serializing writes through a queuing mechanism.
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As a Microsoft Fabric Analytics Engineer Associate working on optimizing a data pipeline in Azure Data Factory, you encounter performance issues with a dataflow writing data to a Delta table due to a high volume of concurrent writes. The organization emphasizes minimizing costs while ensuring data integrity and scalability. Considering these constraints, which of the following approaches would BEST optimize the write operations to the Delta table? (Choose one option.)
A
Increase the batch size of the write operations to reduce the number of operations, potentially lowering costs but risking increased latency for large batches.
B
Implement a queuing mechanism to serialize the write operations, reducing contention and lock contention without significantly increasing costs.
C
Use the 'overwrite' option to replace the entire table with each write operation, which could simplify the process but may not be feasible for all use cases due to potential data loss and increased compute costs.
D
Use the 'upsert' operation to update the table with new data instead of overwriting it, which could reduce the volume of data written but may not fully address the issue of concurrent writes.
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