DP-600 Sample Questions Answers

The relationship between OrderItem and Product must be based on: Both the CompanyID and the ProductID columns

The Company entity must be: Denormalized into the Customer and Product entities

In a dimensional model, the relationships are typically based on foreign key constraints between the fact table (OrderItem) and dimension tables (Product, Customer, Date). Since CompanyID is present in both the OrderItem and Product tables, it acts as a foreign key in the relationship. Similarly, ProductID is a foreign key that relates these two tables. To enable analysis by date, product, and customer, the Company entity would need to be denormalized into the Customer and Product entities to ensure that the relevant company information is available within those dimensions for querying and reporting purposes.

References =

Dimensional modeling

Star schema design

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

The requirement is to copy data from CSV files in Azure Storage into a Fabric Lakehouse, and it must support Power Query M formula language.

Dataflow activity in Fabric pipelines allows you to use Power Query Online, which is based on the M formula language.

Notebook uses Spark (Python, PySpark, Scala, SQL), not M.

Copy data activity moves data but does not support Power Query M transformations.

Script is for T-SQL or stored procedure execution, not M.

Therefore, the correct activity is Dataflow.

You should set Repeat for the schedule to Weekly (C). This allows you to specify the pipeline to run on specific days of the week, in this case, every four hours on Mondays and Fridays. References = Scheduling options for data pipelines are available in the Azure Data Factory documentation, which includes details on configuring recurring triggers.

Finance department users can create and edit items in Workspace1 →

The correct role is Contributor.

To minimize effort, assign this role to the Microsoft Entra group (Group1) instead of assigning it to each user individually.

So answer A is correct, not B.

Workspace1 can securely access storagel (Azure Storage) to read and write data →

To connect a Fabric workspace to external resources securely, you use a Workspace identity (a managed identity for the workspace).

This allows Fabric items to authenticate to Azure Storage without embedding credentials.

So answer D is correct.

You are the only admin of Workspace1 →

By default, the workspace creator (you) is the admin. You do not need to explicitly reassign the admin role to yourself (so C is unnecessary).

Minimize administrative effort →

Assigning Contributor role to the group (A) is minimal effort compared to assigning it individually to each user (B).

Final Answer:

A. Assign the Contributor role to Group1

D. Create a workspace identity

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

Yes, the DESCRIBE HISTORY statement does meet the goal. It provides information on the history of operations, including maintenance tasks, performed on a Delta table. References = The functionality of the DESCRIBE HISTORY statement can be verified in the Delta Lake documentation.

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. "

The transformation you should select from the context menu of the VendorID column to transform the date columns into attribute-value pairs, where columns become rows, is Unpivot columns (B). This transformation will turn the selected columns into rows with two new columns, one for the attribute (the original column names) and one for the value (the data from the cells). References = Techniques for unpivoting columns are covered in the Power Query documentation, which explains how to use the transformation in data modeling.

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 Workspace1 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

To execute each set of items in sequential order with minimized development effort and complexity, you should use the following options:

Orchestration pipeline: Use a pipeline with an Invoke pipeline activity. This allows for orchestrating and scheduling the execution of other pipelines, ensuring they run in the correct sequence.

Bronze layer: Implement a pipeline Copy activity. This aligns with the table indicating that the Bronze layer uses pipelines with Copy activities for data integration.

Silver layer: Implement a pipeline Dataflow activity. The table specifies that Dataflows are used for the Silver layer.

Gold layer: Implement a pipeline Stored procedure activity. Stored procedures are specified for the Gold layer according to the table.

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.

Scenario Recap

Fabric tenant with workspaces: Development, Test, QA, Production.

All workspaces are already in Premium Per User (PPU) mode (which is required for deployment pipelines).

Task: Set up a release pipeline to move through Dev → Test → QA → Production.

Step 1: Create a deployment pipeline

The first step is always to create a new deployment pipeline in the Fabric/Power BI service. This establishes the structure for Dev → Test → Prod (and optionally QA).

Step 2: Create the QA stage

By default, pipelines have 3 stages (Dev, Test, Prod).

Since we need a QA stage in addition, we must explicitly add it as a stage to the pipeline.

Step 3: Assign workspaces

Once the pipeline structure is ready (with QA included), we assign each workspace to the corresponding stage:

Development → Development

Test → Test

QA → QA

Production → Production

Why Not the Other Actions?

Move each workspace to Pro license mode → Incorrect, because deployment pipelines require Premium capacity or PPU, and the workspaces already are in PPU mode.

Create a deployment rule → Deployment rules (e.g., parameter or data source rules) are optional for environment-specific changes, but not part of the initial required setup sequence.

References

Deployment pipelines overview

Assigning workspaces to deployment pipelines

To ensure all rows appear in the Python visual within a Power BI report, option C, adding a unique field to each row, is the correct solution. This will prevent automatic grouping by unique values and allow for all instances of data to be represented in the visual. References = For more on Power BI Python visuals and how they handle data, please refer to the Power BI documentation.

For ingesting customer data into the data store in the AnalyticsPOC workspace, a dataflow (D) should be recommended. Dataflows are designed within the Power BI 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.

Both a dataflow (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.

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 these 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 environment (E).

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, you 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.

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, fulfilling 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.

For the semantic model design required for the customer satisfaction report, the choices for data source authentication method and mode should be made based 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 authentication 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 needs 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 data. 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

Comprehensive Detailed Explanation

When assigning permissions to roles in a Fabric workspace for analytics workloads, you must align permissions with the responsibilities and tools typically used by each persona:

1. Data Engineers

Their primary tasks include building and transforming data pipelines, as well as making datasets usable by others.

The appropriate permission is Build Reports on the default dataset, because this allows them to create and manage reports using the curated semantic model while preparing data for analysts and scientists.

2. Data Analysts

Analysts work heavily with Apache Spark for exploratory analysis, cleansing, and shaping data.

The correct permission is Read All Apache Spark, as this grants them the ability to query and analyze Spark-based data directly without elevated permissions that are unnecessary for their role.

3. Data Scientists

Data scientists often use SQL analytics endpoints to run queries, train models, and integrate data into machine learning workflows.

The correct permission is Read All SQL analytics endpoint data, since it enables direct programmatic access to the SQL endpoint, which is needed for advanced modeling and experimentation.

Summary of Selections:

DataEngineers → Build Reports on the default dataset

DataAnalysts → Read All Apache Spark

DataScientists → Read All SQL analytics endpoint data

References (Microsoft Fabric – DP-600 exam scope):

Workspace roles and permissions in Microsoft Fabric

SQL analytics endpoint in Fabric

Apache Spark in Microsoft Fabric

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 appropriate pricing group. This view will standardize the pricing group logic across different databases and semantic models.

To meet the technical requirement 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).

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

 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 requirement is to logically group the Research division workspaces and support filtering in the OneLake data hub.

Why Domain?

Microsoft Fabric allows you to assign workspace domains (e.g., Finance, HR, Research).

This provides a logical grouping of workspaces within the organization.

Domains are the only supported way to enable filtering in OneLake data hub by department.

Capacity or tenant grouping would not allow filtering by department in OneLake.

Why OneLake data hub?

The OneLake data hub is the centralized interface to browse and discover data assets across the organization.

With workspace domains configured, users can filter directly in the OneLake data hub by domain (e.g., “Research”).

The Fabric Admin portal and Entra admin center are used for administration, not for workspace filtering in data discovery.

Correct Selection for the Hotspot

Grouping method: Domain

Tool: OneLake data hub

References

Workspace domains in Microsoft Fabric

Discover and filter data in OneLake data hub

Final Answer:

Grouping method = Domain

Tool = OneLake data hub

Requirement: “The number of rows added to the Orders table during refreshes must be minimized.”

The Orders table has a sequential OrderID.

To minimize refresh size, you must only import new rows, not the entire table.

This requires retrieving the maximum OrderID currently loaded, then only loading rows with a greater OrderID.

So, the correct solution is to use a pipeline (ADF) with a dataflow to retrieve the maximum OrderID.

Answer: C. an Azure Data Factory pipeline that executes a dataflow to retrieve the maximum value of the OrderID column in the destination lakehouse

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.

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

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 recommend which type of Fabric capacity SKU meets the data analytics requirements for the Research division.

Requirement: “The Research division workspaces must use a dedicated, on-demand capacity that has per-minute billing.”

Fabric capacity SKUs:

F (Fabric) = dedicated Fabric capacity, available on pay-as-you-go with per-minute billing.

P (Premium) and EM are Power BI capacities (not Fabric-native).

A refers to Azure Analysis Services capacity.

The only correct option is F SKU.

Comprehensive Detailed Explanation

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