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NEW QUESTION # 46
DRAG DROP
You have a Fabric eventhouse that contains a KQL database. The database contains a table named TaxiData.
The following is a sample of the data in TaxiData.
You need to build two KQL queries. The solution must meet the following requirements:
One of the queries must partition RunningTotalAmount by VendorID.
The other query must create a column named FirstPickupDateTime that shows the first value of each hour from tpep_pickup_datetime partitioned by payment_type.
How should you complete each query? To answer, drag the appropriate values 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:
Partition the RunningTotalAmount by VendorID. - Row_cumsum
The Row_cumsum function computes the cumulative sum of a column while optionally restarting the accumulation based on a condition. In this case, it calculates the cumulative sum of total_amount for each VendorID, restarting when the VendorID changes (VendorID != prev(VendorID)).
Create a column FirstPickupDateTime that shows the first value of each hour from tpep_pickup_datetime, partitioned by payment_type - Row_window_session
NEW QUESTION # 47
You are developing a data pipeline named Pipeline1.
You need to add a Copy data activity that will copy data from a Snowflake data source to a Fabric warehouse.
What should you configure?
Answer: A
Explanation:
When using the Copy data activity in a data pipeline to move data from Snowflake to a Fabric warehouse, the process often involves intermediate staging to handle data efficiently, especially for large datasets or cross-cloud data transfers.
Staging involves temporarily storing data in an intermediate location (e.g., Blob storage or Azure Data Lake) before loading it into the target destination.
For cross-cloud data transfers (e.g., from Snowflake to Fabric), enabling staging ensures data is processed and stored temporarily in an efficient format for transfer.
Staging is especially useful when dealing with large datasets, ensuring the process is optimized and avoids memory limitations.
NEW QUESTION # 48
You need to recommend a solution to resolve the MAR1 connectivity issues. The solution must minimize development effort. What should you recommend?
Answer: D
NEW QUESTION # 49
You have a Fabric workspace that contains a warehouse named DW1. DW1 contains the following tables and columns.
You need to create an output that presents the summarized values of all the order quantities by year and product. The results must include a summary of the order quantities at the year level for all the products.
How should you complete the code? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
NEW QUESTION # 50
You have a Fabric workspace named Workspace1 that contains a warehouse named Warehouse1.
You plan to deploy Warehouse1 to a new workspace named Workspace2.
As part of the deployment process, you need to verify whether Warehouse1 contains invalid references. The solution must minimize development effort.
What should you use?
Answer: D
Explanation:
A deployment pipeline in Fabric allows you to deploy assets like warehouses, datasets, and reports between different workspaces (such as from Workspace1 to Workspace2). One of the key features of a deployment pipeline is the ability to check for invalid references before deployment. This can help identify issues with assets, such as broken links or dependencies, ensuring the deployment is successful without introducing errors. This is the most efficient way to verify references and manage the deployment with minimal development effort.
NEW QUESTION # 51
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