You have a Fabric tenant that contains a lakehouse named lakehouse1. Lakehouse1 contains a table named Table1. You are creating a new data pipeline. You plan to copy external data to Table1. The schema of the external data changes regularly. You need the copy operation to meet the following requirements: Replace Table1 with the schema of the external data. Replace all the data in Table1 with the rows in the external data. You add a Copy data activity to the pipeline. What should you do for the Copy data activity?
You have a Fabric tenant that contains a new semantic model in OneLake. You use a Fabric notebook to read the data into a Spark DataFrame. You need to evaluate the data to calculate the min, max, mean, and standard deviation values for all the string and numeric columns. Solution: You use the following PySpark expression: df.show() Does this meet the goal?
You have a Fabric tenant that contains two workspaces named Woritspace1 and Workspace2. Workspace1 contains a lakehouse named Lakehouse1. Workspace2 contains a lakehouse named Lakehouse2. Lakehouse! contains a table named dbo.Sales. Lakehouse2 contains a table named dbo.Customers. You need to ensure that you can write queries that reference both dbo.Sales and dbo.Customers in the same SQL query without making additional copies of the tables. What should you use?
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