DP-600 Microsoft Implementing Analytics Solutions Using Microsoft Fabric Free Practice Exam Questions (2026 Updated)

Step 1 – Read the Parquet file into a DataFrame

df = spark.read.parquet( " abfss://fs1@storage1.dfs.core.windows.net/files/sales.parquet " )

This correctly loads the Parquet data into Spark.

Step 2 – Write into the Lakehouse as a managed table

If we want the result to be registered as a Lakehouse table and automatically appear in Lakehouse Explorer, we must:

Write the data in delta format (because Fabric Lakehouse tables are Delta tables ).

Save the table under the tables folder, not files .

So the correct code is:

df.write.mode( " overwrite " ). format ( " delta " ).saveAs Table( " tables/sales " )

Final Answer:

Format: delta

SaveAsTable Path: tables/sales

In Microsoft Fabric, semantic models (formerly datasets) can be managed using external tools like Tabular Editor . To push or publish changes directly to a Fabric workspace from Tabular Editor, the workspace must support XMLA endpoint connectivity with read-write access .

Explanation of Each Option

A. For Workspace1, enable Git integration.

Git integration allows source control and versioning of items in a Fabric workspace. It is intended for lifecycle management of artifacts and does not provide the connectivity required for Tabular Editor to publish semantic model changes.

B. For Workspace1, create a managed private endpoint.

A managed private endpoint provides secure connectivity between Fabric and external data sources. It is not related to the ability to publish semantic models through XMLA.

C. For Model1, enable external sharing.

External sharing governs who can access the model outside the organization. This does not affect publishing workflows from developer tools.

D. For Capacity1, set XMLA Endpoint to Read Write.

This is the correct choice. The XMLA endpoint provides a programmatic connection point for external tools like Tabular Editor. By default, the XMLA endpoint is enabled only in read-only mode. Changing the setting to Read Write allows developers to push changes, update metadata, and deploy semantic models directly from Tabular Editor into the Fabric workspace.

Summary

To publish changes to a Fabric semantic model using Tabular Editor, the XMLA endpoint of the assigned capacity must be set to Read Write . Without this configuration, Tabular Editor can only connect in read-only mode and cannot deploy updates.

References

Use the XMLA endpoint in Microsoft Fabric

Manage capacities in Microsoft Fabric

Tabular Editor and XMLA endpoint

 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 .

In Azure DevOps workspace (project) security settings, role assignments determine access levels for groups:

Viewer (equivalent to the Readers security group): Grants read-only access to work items, code, pipelines, and other artifacts. Ideal for review-only scenarios where users need to view but not edit content.

Contributor (equivalent to the Contributors security group): Allows full edit and contribute privileges, such as updating work items, code reviews, and pipeline runs. Suitable for active participation in reviews that may involve feedback or changes.

Based on the group names implying review-focused access, Viewer fits Group1 for passive observation, while Contributor fits Group2 for interactive review tasks. These align with default Azure DevOps permissions for secure, least-privilege access.

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.

We are designing a star schema for sales transactions. In a star schema:

Dimension tables contain descriptive attributes (e.g., Customer, Product, Date).

Fact tables contain measurable, quantitative data (e.g., SalesTransactionID, Quantity, UnitPrice).

Factless fact tables capture events without numeric measures.

Junk dimensions store miscellaneous low-cardinality attributes.

The requirement says dimension tables will use Type 2 Slowly Changing Dimensions (SCD) → so Customer and Date attributes belong to Dimension tables .

Field-by-field classification

CustomerCode

A business key (natural key) for the customer dimension.

Belongs in the Customer Dimension table.

Answer: Dimension .

CustomerName

A descriptive attribute of the customer.

Belongs in the Customer Dimension table.

Answer: Dimension .

SaleDate

A time attribute used for analysis.

Belongs in the Date Dimension table.

Answer: Dimension .

Final Answer:

CustomerCode → Dimension

CustomerName → Dimension

SaleDate → Dimension

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.ResearchPro duct " )

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.ResearchProduc t " )

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.Res earchProduct " )

That matches Option A .

References

Microsoft Fabric Lakehouse – Shortcuts

Query data in a lakehouse using Spark SQL

Delta format support in Microsoft Fabric

A lakehouse (C) should be recommended for the AnalyticsPOC workspace. It combines the capabilities of a data warehouse with the flexibility of a data lake. A lakehouse supports semi-structured and unstructured data and allows for T-SQL and Python read access, fulfill ing the technical requirements outlined for Litware. References = For further understanding, Microsoft ' s documentation on the lakehouse architecture provides insights into how it supports various data types and analytical operations.

You should use CREATE VIEW to make the pricing group logic available for T-SQL queries.

The CASE statement should be used to determine the pricing group based on the list price.

The T-SQL statement should create a view that classifies products into pricing groups based on the list price. The CASE statement is the correct conditional logic to assign each product to the appropriat e pricing group. This view will standardize the pricing group logic across different databases and semantic models.

For the semantic model design required for the customer satisfaction report, the choices for data source authentication method and mode should be made b ased on security and performance considerations as per the case study provided.

Authentication method: The data should be accessed securely, and given that row-level security (RLS) is required for users executing T-SQL queries, you should use an authentica tion method that supports RLS. Service principal authentication is suitable for automated and secure access to the data, especially when the access needs to be controlled programmatically and is not tied to a specific user ' s credentials.

Mode: The report n eeds to show data as soon as it is updated in the data store, and it should only contain data from the current and previous year. DirectQuery mode allows for real-time reporting without importing data into the model, thus meeting the need for up-to-date da ta. It also allows for RLS to be implemented and enforced at the data source level, providing the necessary security measures.

Based on these considerations, the selections should be:

Authentication method: Service principal authentication

Mode: DirectQuery

Both a dataflo w (A) and a Stored procedure activity in a pipeline (B) are capable of creating and populating a date dimension table. A dataflow can perform the transformation needed to create the date dimension, and it aligns with the preference for using low-code tools for data ingestion when possible. A Stored procedure could be written to generate the necessary date dimension data and executed within a pipeline, which also adheres to the technical requirements for the PoC.

For ingesting customer data into the data store in the AnalyticsPOC workspace, a dataflow (D) should be recommended. Dataflows are designed within the Power B I service to ingest, cleanse, transform, and load data into the Power BI environment. They allow for the low-code ingestion and transformation of data as needed by Litware ' s technical requirements. References = You can learn more about dataflows and their use in Power BI environments in Microsoft ' s Power BI documentation.

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.

To meet the technical requireme nt that data loading activities must ensure the raw and cleansed data is updated completely before populating the dimensional model, you would need a mechanism that allows for ordered execution. A pipeline in Microsoft Fabric with dependencies set between activities can ensure that activities are executed in a specific sequence. Once set up, the pipeline can be scheduled to run at the required intervals (hourly or daily depending on the data source).

The PoC is planned to be completed using a Fabric trial capacity, which implies that users involved in the PoC should be able to try paid features. However, this should be limited to specific security groups involved in the PoC to prevent the entire organization from accessing the se features before the trial is proven successful (A). The ability for users to create Fabric items should also be enabled for specific security groups to ensure that only the relevant team members participating in the PoC can create items in the Fabric en vironment (E).

To implement row-level security (RLS) in a Fabric Warehouse (like in Azure Synapse or SQL Server):

You must define a p redicate function (usually an inline table-valued function) that filters rows for each user → Function .

Then you bind that function to the table using a Security Policy → Security Policy .

A Database Role is used for group-based access control but not specifically for implementing RLS.

Stored procedures and constraints are not used for RLS.

Correct answers: A and D

We are analyzing this KQL query:

Sales

| where Status != " Cancelled "

| where OrderDate > = ago(30d)

| summarize TotalSales = sum(SalesAmount) by ProductCategory

| where TotalSales > 0

Statement 1:

" The query excludes sales that have a Status of Cancelled. "

Yes → where Status != " Cancelled " filters them out.

Statement 2:

" The query calculates the total sales of each product category for the last 30 days. "

Yes → where OrderDate > = ago(30d) ensures only last 30 days.

summarize TotalSales = sum(SalesAmount) by ProductCategory groups by category.

Statement 3:

" The query includes product categories that have had zero sales during the last 30 days. "

No → The final filter where TotalSales > 0 excludes categories with zero sales.

Final Answer:

Excludes Cancelled sales → Yes

Calculates total sales by product category for last 30 days → Yes

Includes product categories with zero sales → No

For a Type 1 SCD, you should include actions that update rows when non-key attributes have changed (A), and insert new records when the natural key is a new value i n the table (D). A Type 1 SCD does not track historical data, so you always overwrite the old data with the new data for a given key. References = Details on Type 1 slowly changing dimension patterns can be found in data warehousing literature and Microsof t ' s official documentation.

For the Copy data activit y, 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 = Info rmation 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.