Step 1 – Select the required fields and count

We want to group by CustomerAltKey and count occurrences.

SELECT CustomerAltKey, COUNT ( * )

Step 2 – From the table

FROM DimCustomer

Step 3 – Grouping by email

GROUP BY CustomerAltKey

Step 4 – Filtering duplicates (only values with count > 1)

HAVING COUNT ( * ) > 1

Step 5 – Limiting the result to 1,000 rows

LIMIT 1000

(or in T-SQL, SELECT TOP(1000) ... ). Since both options are shown, we use LIMIT 1000 because Fabric Warehouse supports Synapse SQL T-SQL + ANSI SQL style.

Correct Sequence:

SELECT CustomerAltKey, COUNT(*)

FROM DimCustomer

GROUP BY CustomerAltKey

HAVING COUNT(*) > 1

LIMIT 1000

For the Copy data activity, from the Destination tab, setting Table action to Overwrite (B) will ensure that Table1 is replaced with the schema and rows of the external data, meeting the requirements of replacing both the schema and data of the destination table. References = Information about Copy data activity and table actions in Azure Data Factory, which can be applied to data pipelines in Fabric, is available in the Azure Data Factory documentation.

To enable scale-out for a semantic model, you should first set Large dataset storage format to On (C) at the semantic model level. This configuration is necessary to handle larger datasets effectively in a scaled-out environment. References = Guidance on configuring large dataset storage formats for scale-out is available in the Power BI documentation.

You should use the Parquet format (B) for the sales data files because it is optimized for performance with large datasets in analytical processing and create a shortcut in the Tables section (D) to facilitate SQL queries through the lakehouse ' s SQL endpoint. References = The best practices for working with file formats and shortcuts in a lakehouse environment are covered in the lakehouse and SQL endpoint documentation provided by the cloud data platform services.

We are analyzing the JSON for definition.pbir in a Power BI Project:

{

" version " : " 1.0 " ,

" datasetReference " : {

" byPath " : {

" path " : " ../Sales.Dataset "

},

" byConnection " : null

}

}

Statement 1:

" definition.pbir is in the PBIR-Legacy format. "

No → The legacy PBIR format references datasets by byConnection (pointing to the Power BI service).

Here, it uses byPath , which is the new project format , not legacy.

Statement 2:

" The semantic model referenced by definition.pbir is located in the Power BI service. "

No → The JSON shows byPath: " ../Sales.Dataset " , meaning the semantic model is referenced locally within the project folder (Sales.Dataset), not in the Power BI service.

Statement 3:

" When the related report is opened, Power BI Desktop will open the semantic model in full edit mode. "

Yes → Since the semantic model is referenced by path (local PBIP project files), Power BI Desktop will open the model in full edit mode . If it had been byConnection , it would open in live connect mode to the service instead.

Final Answer:

PBIR-Legacy format → No

Semantic model in Power BI service → No

Opens in full edit mode → Yes

Requirements:

Revert to previous versions of Model1.

Identify differences between saved versions.

Publish from Power BI Desktop.

Edit item definition files with Visual Studio Code.

Analysis:

To meet version control and difference tracking requirements, you must use Git integration in the Fabric workspace.

To enable editing with VS Code, you must use PBIP format (Power BI Project), which saves report/model as a folder with definition JSON files (not PBIX).

PBIX is a single binary file, not suitable for Git versioning or editing definition files.

" Enable users to edit in service " is irrelevant.

Correct Answers:

A. Enable Git integration for Workspace1.

C. Save Model1 in Power BI Desktop as a PBIP file.

Requirement

Fabric workspace: Workspace1

Contains: Warehouse1, Model1, Report1

Task: Allow User1 to access a single table in Warehouse1 .

Must follow principle of least privilege → grant only the exact permissions required.

Step 1: Evaluate Options

A. Assign object level permissions to User1 for Warehouse1.

In Fabric warehouses, you can grant permissions at the object level (table, schema, or column).

If the requirement is access to only one table , the correct approach is to grant SELECT permissions on that specific table .

This satisfies least privilege.

Correct. ✅

B. Assign the db_datareader role to User1 for Warehouse1.

db_datareader provides read access to all tables in the database/warehouse.

This violates least privilege.

Not correct.

C. Share Warehouse1 with User1.

Sharing grants access to the whole warehouse.

Too broad, not least privilege.

Not correct.

D. Assign the Viewer role to User1 for Workspace1.

Viewer role allows seeing all items in the workspace (warehouse, model, reports).

This would expose more than the single table.

Not correct.

Step 2: Correct Action

Use object-level permissions :

GRANT SELECT ON dbo.TableName TO [User1];

This ensures User1 can only query that specific table, nothing else.

References

Manage object-level security in Microsoft Fabric warehouses

Principle of least privilege in Fabric

Folding in Power Query refers to operations that can be translated into source queries. In this case, " some " of the steps can be folded, which means that some transformations will be executed at the data source level. The steps that cannot be folded will be executed within the Power Query engine. Custom steps, especially those that are not standard query operations, are usually executed within Power Query engine rather than being pushed down to the source system.

References =

Query folding in Power Query

Power Query M formula language

 Calculation groups are a Tabular Object Model (TOM) feature.

 They cannot be authored in Power BI Desktop or the service.

 They are implemented using Tabular Editor .

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