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Microsoft Implementing Analytics Solutions Using Microsoft Fabric Sample Questions (Q96-Q101):
NEW QUESTION # 96
You have a Fabric tenant that uses a Microsoft tower Bl Premium capacity. You need to enable scale-out for a semantic model. What should you do first?
Answer: D
Explanation:
To enable scale-out for a semantic model, you should first set Large dataset storage format to On (C) at the semantic model level. This configuration is necessary to handle larger datasets effectively in a scaled-out environment. Reference = Guidance on configuring large dataset storage formats for scale-out is available in the Power BI documentation.
NEW QUESTION # 97
You are creating a dataflow in Fabric to ingest data from an Azure SQL database by using a T-SQL statement.
You need to ensure that any foldable Power Query transformation steps are processed by the Microsoft SQL Server engine.
How should you complete the code? To answer, drag the appropriate values to the correct targets. Each value may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
You should complete the code as follows:
* Table
* NativeQuery
* EnableFolding
In Power Query, using Table before the SQL statement ensures that the result of the SQL query is treated as a table. NativeQuery allows a native database query to be passed through from Power Query to the source database. The EnableFolding option ensures that any subsequent transformations that can be folded will be sent back and executed at the source database (Microsoft SQL Server engine in this case).
NEW QUESTION # 98
You have a Fabric eventhouse that contains a KQL database. The database contains a table named TaxiData that stores the following data.
You need to create a column named FirstPickupDateTime that will contain the first value of each hour from tpep_pickup_datetime partitioned by payment_type.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
NEW QUESTION # 99
You have a Fabric tenant that contains a warehouse named DW1 and a lakehouse named LH1.
DW1 contains a table named Sales.Product. LH1 contains a table named Sales.Orders.
You plan to schedule an automated process that will create a new point-in-time (PIT) table named Sales.ProductOrder in DW1. Sales.ProductOrder will be built by using the results of a query that will join Sales.Product and Sales.Orders.
You need to ensure that the types of columns in Sales.ProductOrder match the column types in the source tables. The solution must minimize the number of operations required to create the new table.
Which operation should you use?
Answer: D
Explanation:
The CREATE TABLE AS SELECT (CTAS) statement allows you to create a new table directly from the results of a query, including joining tables. It automatically determines the column types based on the query's result set, ensuring they match the source tables. This minimizes the number of operations required.
NEW QUESTION # 100
Hotspot Question
You have a Fabric tenant that contains a workspace named Workspace_DEV. Workspace_DEV contains the semantic models shown in the following table.
Workspace_DEV contains the dataflows shown in the following table.
You create a new workspace named Workspace_TEST.
You create a deployment pipeline named Pipeline1 to move items from Workspace_DEV to Workspace_TEST.
You run Pipeline1.
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
NEW QUESTION # 101
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