DP-600 Questions and Answers

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.

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

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).

The results will form a hierarchy of folders for each partition key. - Yes

The resulting file partitions can be read in parallel across multiple nodes. - Yes

The resulting file partitions will use file compression. - No

Partitioning data by columns such as year, month, and day, as shown in the DataFrame write operation, organizes the output into a directory hierarchy that reflects the partitioning structure. This organization can improve the performance of read operations, as queries that filter by the partitioned columns can scan only the relevant directories. Moreover, partitioning facilitates parallelism because each partition can be processed independently across different nodes in a distributed system like Spark. However, the code snippet provided does not explicitly specify that file compression should be used, so we cannot assume that the output will be compressed without additional context.

References =

DataFrame write partitionBy

Apache Spark optimization with partitioning

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.

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.

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

To meet the requirements, developers should have Admin access to the deployment pipeline (B), Contributor access to the Development and Test workspaces (E), and Viewer access to the Production workspace (D). This setup ensures they can perform necessary actions in development and test environments without having the ability to affect production. References = The Power BI documentation on workspace access levels and deployment pipelines provides guidelines on assigning appropriate permissions.

To validate the data seen by the HR manager, you should use the 'Test as role' feature in Power BI service. This allows you to see the data exactly as it would appear for the HR role, considering the dynamic RLS setup. Here is how you would proceed:

Navigate to the Power BI service and locate Model1.

Access the dataset settings for Model1.

Find the security/RLS settings where you configured the roles.

Use the 'Test as role' feature to simulate the report viewing experience as the HR role.

Review the data and the filters applied to ensure that the RLS is functioning correctly.

If discrepancies are found, adjust the RLS expressions or the role membership as needed.

When working with Power BI Desktop and Git integration for version control, report and semantic model definitions should be saved in the PBIX format. PBIX is the Power BI Desktop file format that contains definitions for reports, data models, and queries, and it can be easily saved and tracked in a version-controlled environment. The solution should minimize development and maintenance effort, and saving in PBIX format allows for the easiest transition from development to deployment, especially when using Azure Pipelines for CI/CD (continuous integration/continuous deployment) practices.

Comprehensive Detailed Explanation

Requirement

We have two columns in Power Query Editor:

Order_Date

Shipping_Date

We need to calculate the number of days between the two dates.

Step 1: How date differences work in Power Query (M language)

Subtracting two date or datetime values in Power Query returns a duration value.

Example:

[Shipping_Date] - [Order_Date]

This produces a duration such as 3.00:00:00 (3 days).

Step 2: Converting duration into days

To get the number of days as an integer, we use Duration.Days().

Example:

Duration.Days([Shipping_Date] - [Order_Date])

This returns 3 if the shipping date is 3 days after the order date.

Step 3: Evaluate other options

A. Duration.From → Converts a value to a duration type but does not extract the number of days.

B. Date.AddDays → Adds days to a given date, not used for calculating the difference.

C. Duration.Days → Correct, extracts total days from a duration.

D. DateTime.LocalNow → Returns the current system datetime, unrelated to this requirement.

References

Duration.Days function (Power Query M)

Working with dates in Power Query

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

 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.

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.

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

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

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

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