You need to schedule the population of the medallion layers to meet the technical requirements.
What should you do?

• A.Schedule a data pipeline that calls other data pipelines.
• B.Schedule a notebook.
• C.Schedule an Apache Spark job.
• D.Schedule multiple data pipelines.

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You have two Fabric notebooks named Load_Salesperson and Load_Orders that read data from Parquet files in a lakehouse. Load_Salesperson writes to a Delta table named dim_salesperson. Load.Orders writes to a Delta table named fact_orders and is dependent on the successful execution of Load_Salesperson.
You need to implement a pattern to dynamically execute Load_Salesperson and Load_Orders in the appropriate order by using a notebook.
How should you complete the code? 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.

Correct Answer:

Explanation:

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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?

• A.Degree of copy parallelism
• B.Fault tolerance
• C.Enable staging
• D.Enable logging

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You have a Fabric workspace that contains a warehouse named DW1. DW1 is loaded by using a notebook named Notebook1.
You need to identify which version of Delta was used when Notebook1 was executed.
What should you use?

• A.Real-Time hub
• B.OneLake data hub
• C.the Admin monitoring workspace
• D.Fabric Monitor
• E.the Microsoft Fabric Capacity Metrics app

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HOTSPOT
You need to troubleshoot the ad-hoc query issue.
How should you complete the statement? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Correct Answer:

Explanation:
A screenshot of a computer Description automatically generated

SELECT last_run_start_time, last_run_command: These fields will help identify the execution details of the long-running queries.
FROM queryinsights.long_running_queries: The correct solution is to check the long-running queries using the queryinsights.long_running_queries view, which provides insights into queries that take longer than expected to execute.
WHERE last_run_total_elapsed_time_ms > 7200000: This condition filters queries that took more than 2 hours to complete (7200000 milliseconds), which is relevant to the issue described.
AND number_of_failed_runs > 1: This condition is key for identifying queries that have failed more than once, helping to isolate the problematic queries that cause failures and need attention.

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