Alright, now let's talk about Data Factory. One amazing feature of Data Factory is that it can access data that's outside of Azure, whether it's on-premises or in another cloud platform, such as AWS or GCP. To make that work, you need to install a self-hosted integration runtime in the other environment. Data Factory connects to the other environment through the integration runtime.

Data Factory supports two other integration runtimes as well. The Azure-SSIS Integration Runtime lets you run SQL Server Integration Services packages on Azure. An SSIS package is actually kind of like a Data Factory pipeline. It contains a series of tasks to transform data. If you already have SSIS packages that you've been running locally, then this is a great way to lift and shift them to Azure.

Finally, there's the Azure Integration Runtime. You don't normally need to explicitly create this one because when you create a Data Factory, it automatically creates an Azure integration runtime as well, as you can see here. It's still possible to create one manually, though. Why would you want to? Well, you see how it says Auto Resolve in the REGION column? That means it will try to choose the best region based on your data source, destination, etc. But if you have special requirements for what location it should use, then you'll have to create one manually. For example, for compliance reasons, you might need to keep your data in a particular country.

Okay, another thing you should know about Data Factory is how to start a pipeline run. There are four ways: manually, with a schedule trigger, which means it will start the run at a particular time, with a tumbling window trigger, which means it will run every X number of minutes or hours, or with an event-based trigger, which means it will run when a specific type of event occurs, such as when a blob is created in a particular container. 

That's it for additional topics for the DP-200 exam. If you have any questions or comments, please let us know.

Thanks and good luck on the exam!