You have a Fabric workspace that contains a lakehouse named Lakehousel. Lakehousel contains a table named Status_Target that has the following columns:
* Key
* Status
* LastModified
The data source contains a table named Status.Source that has the same columns as Status_Target. Status.Source is used to populate Status_Target. In a notebook name Notebook!, you load Status_Source to a DataFrame named sourceDF and Status_Target to a DataFrame named targetDF. You need to implement an incremental loading pattern by using Notebook-!. The solution must meet the following requirements:
* For all the matching records that have the same value of key, update the value of LastModified in Status_Target to the value of LastModified in Status_Source.
* Insert all the records that exist in Status_Source that do NOT exist in Status_Target.
* Set the value of Status in Status_Target to inactive for all the records that were last modified more than seven days ago and that do NOT exist in Status.Source.
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:

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You have a Fabric workspace that contains a large table named Table1. Table1 contains 2 billion rows.
You have a data source that generates a data file every 30 minutes The file contains only changes that occurred since the last file was generated.
You plan to deploy a data pipeline that will process each data file when it is generated and load the contents into Table1. You need to recommend which loading pattern to use for the following operations:
* Create new records.
* Delete existing records.
The solution must support the versioning of existing records.
What should you recommend for each operation? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Correct Answer:

Explanation:

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You need to ensure that WorkspaceA can be configured for source control. Which two actions should you perform?
Each correct answer presents part of the solution. NOTE: Each correct selection is worth one point.

• A.Configure WorkspaceA to use a Premium Per User (PPU) license
• B.Assign WorkspaceA to Capl.
• C.From Tenant setting, set Users can sync workspace items with GitHub repositories to Enabled
• D.From Tenant setting, set Users can synchronize workspace items with their Git repositories to Enabled

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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.

Correct Answer:

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

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Your company has three newly created data engineering teams named Team1, Team2, and Team3 that plan to use Fabric. The teams have the following personas:
* Team1 consists of members who currently use Microsoft Power BI. The team wants to transform data by using by a low-code approach.
* Team2 consists of members that have a background in Python programming. The team wants to use PySpark code to transform data.
* Team3 consists of members who currently use Azure Data Factory. The team wants to move data between source and sink environments by using the least amount of effort.
You need to recommend tools for the teams based on their current personas.
What should you recommend for each team? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Correct Answer:

Explanation:

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