DP-600 Microsoft Implementing Analytics Solutions Using Microsoft Fabric Free Practice Exam Questions (2025 Updated)

To create a calculation item that changes the context from the selected date to month-to-date (MTD), the appropriate DAX expression involves using the CALCULATE function to alter the filter context and the DATESMTD function to specify the month-to-date context.

The correct completion for the DAX expression would be:

In the first dropdown, select CALCULATE.

In the second dropdown, select SELECTEDMEASURE.

This would create a DAX expression in the form:

CALCULATE(

SELECTEDMEASURE(),

DATESMTD('Date'[DateColumn])

)

Tabular Editor is an advanced tool for editing Tabular models outside of Power BI Desktop that allows you to script out changes and apply them across multiple columns or tables. To accomplish the task programmatically, you would:

Open the model in Tabular Editor.

Create an Advanced Script using C# to iterate over all tables and their respective columns.

Within the script, check if the column name ends with 'Key'.

For columns that meet the condition, set the properties accordingly: IsHidden = true, IsNullable = false, SummarizeBy = None, IsAvailableInMDX = false.

Additionally, mark the column as a key column.

Save the changes and deploy them back to the Fabric tenant.

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.

The Table.Profile function in Power Query is used to generate column statistics such as count, average, null count, distinct count, and standard deviation. You can use this function as follows:

Invoke the Power Query Editor.

Apply the Table.Profile function to your table.

The result will be a table where each row represents a column from the original table, and each column in the result represents a different statistic such as those listed in the requirement.

Yes, the df.summary() method does meet the goal. This method is used to compute specified statistics for numeric and string columns. By default, it provides statistics such as count, mean, stddev, min, and max. References = The PySpark API documentation details the summary() function and the statistics it provides.

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

The two languages you can use to perform model scoring in a Fabric notebook using the predict function are Spark SQL (option C) and PySpark (option D). These are both part of the Apache Spark ecosystem and are supported for machine learning tasks in a Fabric environment. References = You can find more information about model scoring and supported languages in the context of Fabric notebooks in the official documentation on Azure Synapse Analytics.

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

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.

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).

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.

Data Engineers: Read All SQL analytics endpoint data

Data Analysts: Read All Apache Spark

Data Scientists: Read All SQL analytics endpoint data

The permissions for the data store in the AnalyticsPOC workspace should align with the principle of least privilege:

Data Engineers need read and write access but not to datasets or reports.

Data Analysts require read access specifically to the dimensional model objects and the ability to create Power BI reports.

Data Scientists need read access via Spark notebooks. These settings ensure each role has the necessary permissions to fulfill their responsibilities without exceeding their required access level.

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.

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.