Free Microsoft DP-600 Practice Exam with Questions & Answers

We need to enable version control for a Fabric workspace ( Workspace1 ) and ensure that Model1 (a semantic model) is added to an Azure DevOps repository.

Key facts:

Workspace1 is in Pro mode . To enable Git integration , the workspace must be assigned to a Fabric capacity .

Git integration steps in Fabric:

Assign the workspace to a Fabric capacity.

Connect the workspace to a Git provider (e.g., Azure DevOps or GitHub).

Sync the workspace with the repository (to push artifacts like semantic models).

Branch policies and deployment pipelines are not needed for simply enabling version control.

Correct Sequence:

Assign Workspace1 to a Fabric capacity.

Connect Workspace 1 to a Git provider.

Sync Workspace1 with the repository.

Final Answer:

Step 1 → Assign Workspace1 to a Fabric capacity

Step 2 → Connect Workspace1 to a Git provider

Step 3 → Sync Workspace1 with the repository

Step 1 – Creating the new table from a query

To create a table in a SQL warehouse using a query, the correct syntax is:

CREATE TABLE < table_name > AS SELECT ...

So we must select:

CREATE TABLE dbo.POSCustomers AS SELECT

Step 2 – Selecting the source table

Since the data source is the Lakehouse1 table Customer , the fully qualified name is:

lakehouse1.dbo.Customer

Final Answer:

CREATE TABLE dbo.POSCustomers AS SELECT

postalcode,

category

FROM lakehouse1.dbo.Customer;

Comprehensive Detailed Explan ation

From the case study:

Research division data for Productline2 is currently in CSV format in storage2.

Requirement: “All the Research division data in the lakehouses must be presented as managed tables in Lakehouse explorer.”

In Fabric lakehouses, managed tables are stored in Delta format inside the Tables folder.

Step 1: Reading the source

df = spark.read. format ( " csv " ) \

options(header= " true " , inferSchema= " true " ) \

load( " abfss://storage1.dfs.core.windows.net/files/productline2 " )

This correctly ingests the CSV source from ADLS Gen2.

Step 2: Writing to Lakehouse as a managed table

You must write the data in Delta format to ensure it is queryable and managed within the lakehouse.

The correct path is under Tables/ , because this is where Fabric automatically manages Lakehouse managed tables .

The target table should be named productline2 , so the correct path is:

df.write.mode( " overwrite " ). format ( " delta " ).save( " Tables/productline2 " )

Why not other options?

CSV or Parquet formats would not create a managed Lakehouse table; they would just create files.

Writing to productline2 directly (without Tables/ ) would store unmanaged files in the Lakehouse Files area, not managed tables.

Writing to Tables/research/productline2 adds an unnecessary subdirectory and is not the standard structure for managed tables.

Correct Final Code

df.write.mode( " overwrite " ). format ( " delta " ).save( " Tables/productline2 " )

References

Managed tables in Microsoft Fabric Lakehouse

Delta Lake support in Fabric

Spark write to Lakehouse

The command:

DESCRIBE DETAIL Customer

Returns metadata about the Delta table, such as format, schema, partitioning, size, number of files, and creation/modification timestamps.

However, it does not provide information about whether maintenance tasks (such as OPTIMIZE , VACUUM , or Z-Ordering) were performed on the table.

To check maintenance history, you would typically use:

DESCRIBE HISTORY Customer

which shows operations executed on the table (e.g., OPTIMIZE, VACUUM, MERGE).

Since DE SCRIBE DETAIL does not satisfy the requirement, the solution does not meet the goal.

 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

When saving a semantic model to a file that can be edited using the Tabular Model Scripting Language (TMSL) extension for Visual Studio Code, the PBIX (Power BI Desktop) file format is the correct choice. The PBIX format contains the report, data model, and queries, and is the primary file format for editing in Power BI Desktop. References = Micr osoft ' s documentation on Power BI file formats and Visual Studio Code provides further clarification on the usage of PBIX files.

 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 .

The measure should use the AVERAGE function to calculate the average value.

It should reference the Response Value column from the ' Survey ' table.

The ' Number of months ' should be used to define the period for the average calculation.

To calculate the average overall satisfaction score using DAX, yo u would need to use the AVERAGE function on the response values related to satisfaction questions. The DATESINPERIOD function will help in calculating the rolling average over the last 12 months.

Requirements:

Parallel processing → PySpark runs distributed across Spark clusters.

Minimize duplication of data → OneLake shortcut + Spark queries avoid unnecessary copies.

Time series analysis, anoma ly detection, visualization → can be done in PySpark within Fabric Notebooks.

pandas (C) is single-threaded and not efficient for billions of items.

Power BI visuals (A) are good for visualization but not for transformation or large-scale anomaly detection.

Correct: B .

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 i ntegration (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.

Answer: D. Modify the settings o f the Research division workspaces to use an Azure Repos repository.