 First blank: CALCULATE

 Second blank: _LYSales

Step 1 – Defining the variable

We want to calculate sales for the same period in the prior year. The correct function here is CALCULATE, because it allows applying a filter context modification (with SAMEPERIODLASTYEAR).

So:

VAR _LYSales =

CALCULATE ( [Total Sales], SAMEPERIODLASTYEAR ( 'Orders'[Order Date] ) )

Step 2 – Returning the value

We then need to RETURN the value of the variable. From the dropdown options, we select _LYSales.

So:

EVALUATE

VAR _LYSales =

CALCULATE ( [Total Sales], SAMEPERIODLASTYEAR ( 'Orders'[Order Date] ) )

RETURN

ROW ( "LastYearSales", _LYSales )

Note: In XMLA DAX queries, you usually need a table expression, so wrapping with ROW or SELECTCOLUMNS is common, but based on the provided answer box, just returning _LYSales is sufficient here.

Final Answer:

First blank: CALCULATE

Second blank: _LYSales

DF1 will be deployed to Workspace_TEST: Yes

Data from Model1 will be deployed to Workspace_TEST: No

The scheduled refresh policy for Model1 will be deployed to Workspace_TEST: No

DF1 is a Dataflow Gen1 with a configured scheduled refresh policy, so it will be deployed to Workspace_TEST.

Data from Model1 will not be deployed as Model1's scheduled refresh policy is not configured, indicating it is not set up for deployment.

The scheduled refresh policy for Model1 will not be deployed since it is listed as "Not configured."

When creating a OneLake shortcut to Amazon S3, authentication uses the standard AWS access key ID + secret access key pair.

SAS token is for Azure Storage, not AWS.

Certificate thumbprint is not used for S3.

Access ID is incorrect naming; the correct term is access key ID.

Correct answers: B and D.

Scenario Recap

Fabric tenant → Workspace1

Contains:

Lakehouse LH1 (with table signindata in schema dho)

Warehouse DW1

Task: Create a stored procedure in DW1 that deduplicates rows in signindata.

Step 1: Stored procedure structure

In T-SQL, stored procedures begin with:

AS

BEGIN

-- logic

END

So the correct option for the first blank is BEGIN.

BEGIN DISTRIBUTED TRANSACTION is not needed because we are not spanning multiple servers or needing distributed transactions.

SET is not the right way to start the logic block.

Step 2: Deduplication logic

To remove duplicates from signindata, the query should return unique rows.

The simplest way is:

SELECT DISTINCT PersonID, FirstName, LastName

FROM dho.signindata;

Thus the correct choice for the second blank is DISTINCT.

GROUP BY could also deduplicate but is less efficient here since no aggregation is requested.

TOP 100 PERCENT WITH TIES is irrelevant.

Step 3: Final T-SQL stored procedure

CREATE PROCEDURE dbo.usp_GetPerson

AS

BEGIN

SELECT DISTINCT PersonID, FirstName, LastName

FROM dho.signindata;

END;

Why this is correct

BEGIN → correct stored procedure structure.

DISTINCT → ensures deduplication of rows from signindata.

References

CREATE PROCEDURE (Transact-SQL)

DISTINCT (Transact-SQL)

Yes, the df.summary() method does meet the goal. This method is used to compute specified statistics for numeric and string columns. By default, it provides statistics such as count, mean, stddev, min, and max. References = The PySpark API documentation details the summary() function and the statistics it provides.

In Microsoft Fabric, a shortcut lets you reference data from another workspace’s Lakehouse or external storage without duplicating the data.

A view would only apply within the same database/Lakehouse, not across workspaces.

A dataflow transforms and loads data but creates a new copy.

A managed table is a physical table stored in the Lakehouse itself, not a cross-reference.

So, to query dbo.Sales (Lakehouse1) and dbo.Customers (Lakehouse2) in a single SQL query, you use shortcuts.

No, the REFRESH TABLE statement does not provide information on whether maintenance tasks were performed. It only updates the metadata of a table to reflect any changes on the data files. References = The use and effects of the REFRESH TABLE command are explained in the Spark SQL documentation.