Comprehensive Detailed Explanation
The question asks: Which syntax should you use in a Fabric notebook to access the Research division data for Productline1?
Key Background from the Case
In Productline1ws , a lakehouse named Lakehouse1 is created.
In Lakehouse1 , a shortcut is created to storage1 , named ResearchProduct .
Storage1 contains the Research division data for Productline1 in Delta format .
Requirement: All data in lakehouses must be presented as managed tables in Lakehouse explorer .
Analyzing the Syntax Options
Option A:
spark.sql( " SELECT * FROM Lakehouse1.ResearchProduct " )
This syntax directly queries the ResearchProduct shortcut within Lakehouse1 using Spark SQL.
Since the shortcut points to Delta data , Spark can directly query it.
This is the correct way to retrieve Productline1 data from Lakehouse1.
Option B:
spark.sql( " SELECT * FROM Lakehouse1.productline1.ResearchProduct " )
This introduces an extra schema-like path ( productline1 ) that is not part of the shortcut name .
Incorrect, because the shortcut was created as ResearchProduct inside Lakehouse1, not under another schema.
Option C:
external_table( ' Tables/ResearchProduct ' )
external_table is not the correct way to access a Lakehouse shortcut.
Shortcuts in Lakehouses appear as tables and can be queried using Spark SQL directly.
Option D:
spark.sql( " SELECT * FROM Lakehouse1.productline1.ResearchProduct " )
Same issue as Option B, includes a schema path that does not exist.
Correct Choice
Since the shortcut to ResearchProduct was created inside Lakehouse1 , and Spark SQL can query it directly, the correct syntax is:
spark.sql( " SELECT * FROM Lakehouse1.ResearchProduct " )
That matches Option A .
References
Microsoft Fabric Lakehouse - Shortcuts
Query data in a lakehouse using Spark SQL
Delta format support in Microsoft Fabric

You need to ensure that Contoso can use version control to meet the data analytics requirements and the general requirements.
Requirement: "All the semantic models and reports for the Research division must use version control that supports branching." Fabric supports Git integration (Azure Repos or GitHub) for semantic models and reports.
Storing in Data Lake Gen2 or OneDrive does not provide version control with branching.
The correct action is to integrate workspaces with a Git repository.

Explanation:

For the PeriodDate that returns the first day of the month for OrderDate , you should use DATEFROMPARTS as it allows you to construct a date from its individual components (year, month, day).
For the DayName that returns the name of the day for OrderDate , you should use DATENAME with the weekday date part to get the full name of the weekday.
The complete SQL query should look like this:
SELECT OrderID, CustomerID,
DATEFROMPARTS(YEAR(OrderDate), MONTH(OrderDate), 1) AS PeriodDate,
DATENAME(weekday, OrderDate) AS DayName
FROM Sales.Orders
Select DATEFROMPARTS for the PeriodDate and weekday for the DayName in the answer area.

Explanation:
Remove the files: Run the VACUUM command on a schedule.
Combine the files: Set the optimizeWrite table setting. or Run the OPTIMIZE command on a schedule.
To remove files that are no longer used, the VACUUM command is used in Delta Lake to clean up invalid files from a table. To combine smaller files into larger ones, you can either set the optimizeWrite setting to combine files during write operations or use the OPTIMIZE command, which is a Delta Lake operation used to compact small files into larger ones.

Explanation:
PBIR-Legacy format # No
Located in Power BI service # No
Opens in full edit mode # Yes
Comprehensive Detailed Explanation
We are analyzing a Power BI Report (PBIR) definition file in JSON format:
{
" version " : " 1.0 " ,
" byPath " : {
" path " : " ../Sales.Dataset "
}
}
}
Step 1: Is this PBIR-Legacy?
PBIR-Legacy format is used when referencing a dataset hosted in the Power BI service (cloud).
In this case, the JSON uses byPath , which references a local dataset file ( Sales.Dataset ) in the project folder , not a service dataset.
Therefore: No , it is not PBIR-Legacy.
Step 2: Where is the semantic model located?
The datasetReference points to ../Sales.Dataset .
That means the semantic model is stored locally in the project structure , not in the Power BI service.
Therefore: No , it is not located in the Power BI service.
Step 3: What happens when opening in Power BI Desktop?
Since the dataset reference is local ( byPath ), Power BI Desktop opens the semantic model in full edit mode (allowing schema, relationships, measures, etc. to be modified).
If it were PBIR-Legacy with a service model, only Live Connection mode would be available.
Therefore: Yes , Power BI Desktop opens it in full edit mode.
References
PBIR file structure in Power BI projects
Semantic model references: byPath vs byConnection
PBIR-Legacy format # No
Located in Power BI service # No
Opens in full edit mode # Yes