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Answer: Use Azure Data Factory's autoscaling feature to dynamically allocate resources based on workload.
Option B is the correct approach as it leverages Azure Data Factory's autoscaling feature to dynamically allocate resources based on the workload. This ensures optimal performance and cost efficiency by scaling resources up or down as needed. Option A may lead to resource contention and increased costs, while Option C requires manual intervention and may not be scalable. Option D is not recommended as it compromises monitoring and troubleshooting capabilities.
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In a scenario where you need to implement a data pipeline that processes large volumes of data in Azure Data Factory, which of the following strategies would you use to optimize performance and minimize costs?
A
Increase the number of parallel activities to maximize resource utilization.
B
Use Azure Data Factory's autoscaling feature to dynamically allocate resources based on workload.
C
Manually adjust the degree of parallelism for each activity based on the size of the input data.
D
Disable all monitoring and logging to reduce overhead.