Exam DP-203 topic 4 question 42 discussion

I would answer A. https://learn.microsoft.com/en-us/azure/data-factory/concepts-integration-runtime Copying between a cloud data source and a data source in a private network: if either the source or sink linked service points to a self-hosted IR, the copy activity is executed on the self-hosted IR.

Why not B? https://learn.microsoft.com/en-us/azure/data-factory/concepts-integration-runtime Azure integration runtime provides the native compute to move data between cloud data stores in a secure, reliable, and high-performance manner. You can set how many data integration units to use on the copy activity, and the compute size of the Azure IR is elastically scaled up accordingly without requiring you to explicitly adjust the size of the Azure Integration Runtime. For high availability and scalability, you can scale out the self-hosted IR by associating the logical instance with multiple on-premises machines in active-active mode.

Correct

To maximize the amount of compute resources available to the Copy activity (Copy1) in your Azure Data Factory pipeline while minimizing administrative effort, the most appropriate action would be: A. Scale out the self-hosted integration runtime. Explanation: Self-Hosted Integration Runtime (SHIR): Since the source of Copy1 is an on-premises SQL Server, the data needs to be transferred using the self-hosted integration runtime. Scaling out the self-hosted integration runtime means adding more nodes to the integration runtime cluster, which increases the number of parallel connections and throughput for data transfer from the on-premises SQL Server to Azure. Azure Integration Runtime: The sink is an Azure SQL Database accessed via an Azure integration runtime. Azure integration runtime is managed by Azure, and it automatically scales based on the load. Therefore, you generally do not need to manually scale up the Azure integration runtime.

A is correct

I chose B option

A is the answer; as BillMyI quoted microsoft - copy is happening on self-hosted IR

c is correct

The question explicitly requires to minimize the administrative cost. Every time there's such a request, the solution must be as much automated as possible. Self-Hosted has to be managed by some IT department, so it implies effort ad then administrative costs. Instead, simply scaling up the data flow runtime does not require infrastructural costs, since Azure provides such a machine type in the cloud, so we don't have to handle our machine in the company. Just a detail: dataflows, even with a giant cluster, have very poor performances in practical situations. Just scaling up the IR could be not enough...

es, the answer is still the same. Scaling up the data flow runtime of the Azure integration runtime is still the best option to maximize the amount of compute resources available to Copy1. The reasoning is that the copy activity is configured to use the self-hosted integration runtime because the source linked service is connected to a self-hosted integration runtime. This means that the copy activity will be executed on the self-hosted integration runtime, and scaling up the Azure integration runtime will have no effect on the copy activity's performance. Scaling up the Azure integration runtime would only help if the source and sink linked services were both connected to the Azure integration runtime. In that case, scaling up the Azure integration runtime would provide more processing power for the copy activity.

Integration runtime is hosted on the location of the sink for copy activity if I am not mistaken.

chat gpt C. Scale up the data flow runtime of the Azure integration runtime. Explanation: In Azure Data Factory, when you're copying data between different data stores, the compute resources used by the Copy activity are mainly determined by the data flow involved in the copying process. Azure Data Factory provides two types of integration runtimes:

Chatgpt: The self-hosted integration runtime can be scaled out by adding additional nodes, which allows it to process more activities simultaneously. This is a way to increase compute resources without a significant administrative overhead since it involves configuration changes rather than physical infrastructure changes. Options B and C involve scaling up the data flow runtime, which is not applicable in this context since the Copy activity does not use data flow runtime; it uses the integration runtime for data movement. Therefore, the correct answer to maximize compute resources for Copy1 with minimal administrative effort is: A. Scale out the self-hosted integration runtime.

For maximizing the amount of compute resources available to the Copy activity in Azure Data Factory, you should consider scaling up the data flow runtime of the Azure integration runtime. Option C. Scale up the data flow runtime of the Azure integration runtime.

https://learn.microsoft.com/en-us/azure/data-factory/concepts-integration-runtime#self-hosted-ir-compute-resource-and-scaling A should be corrrect.

My answer is A due to the precedence criteria among Integration runtimes selection when source and sink linked services are linked to different IRs, as described here https://learn.microsoft.com/en-us/azure/data-factory/concepts-integration-runtime#determining-which-ir-to-use

The important point is - "The solution must minimize administrative effort." Azure integration runtime - Cloud-based service - Not as scalable as a SHIR Self-hosted integration runtime - More scalable - Requires more administrative effort Scaling up the Azure IR will give us more compute resources without increasing the administrative effort.