You have an Azure Data Factory pipeline that loads data from an on-premises SQL Server to an Azure Synapse Analytics dedicated SQL pool. The pipeline uses a staging Azure Blob Storage account. Recently, the pipeline has been failing with timeout errors. You need to ensure the pipeline completes successfully within the scheduled window. What should you do?
Increasing timeout allows more time for large data transfers, and parallel copy improves throughput.
Why this answer
The timeout errors occur because the default copy timeout for staging blob storage is insufficient for large data volumes or slow network conditions. Increasing the staging blob's copy timeout and enabling parallel copy in the copy activity directly addresses this by allowing more time for data transfer and leveraging multiple concurrent connections to improve throughput, ensuring the pipeline completes within the scheduled window.
Exam trap
The trap here is that candidates may confuse scaling the destination (Synapse DWUs) or changing storage tiers with fixing the root cause of timeout errors, which is often a configuration issue in the copy activity's staging settings rather than a capacity problem.
How to eliminate wrong answers
Option B is wrong because moving to Azure Data Lake Storage Gen2 does not inherently resolve timeout issues; it provides hierarchical namespace and POSIX permissions but does not change the copy timeout or parallelism settings. Option C is wrong because increasing DWUs scales the dedicated SQL pool's compute resources for query performance, not for data ingestion timeout issues during staging. Option D is wrong because PolyBase is an alternative loading method that bypasses staging but does not address timeout errors in the existing staging-based pipeline; it would require architectural changes and may introduce different performance characteristics.