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Microsoft DP-600 Exam Sample Questions

Question # 1

You to need assign permissions for the data store in the AnalyticsPOC workspace. The solution must meet the security requirements. Which additional permissions should you assign when you share the data store? To answer, select the appropriate options in the answer area. NOTE: Each correct selection is worth one point.

Explanation:
The question evaluates knowledge of Microsoft Fabric lakehouse item-level sharing permissions in a workspace. When sharing a lakehouse (the data store), additional granular permissions can be granted beyond basic access. The goal is to apply least-privilege security: enabling DataEngineers to manage Spark jobs, DataAnalysts to build Power BI reports on semantic models, and DataScientists to query via SQL endpoint without unnecessary elevated access.

Correct Option:

DataEngineers: Read All Apache Spark
This permission allows DataEngineers to read and execute Spark notebooks/jobs on the lakehouse files, supporting data ingestion, transformation, and curation tasks in Apache Spark while adhering to least privilege.

DataAnalysts: Build Reports on the default dataset
This grants DataAnalysts the ability to create and publish Power BI reports directly on the default semantic model of the lakehouse, enabling visualization and analysis without access to underlying Spark or raw files.

DataScientists: Read All SQL analytics endpoint data
This provides DataScientists read-only access to query the lakehouse data via the SQL analytics endpoint, ideal for experimentation, modeling, and analysis using T-SQL without allowing modifications or Spark access.

Incorrect Option:

DataEngineers: Build Reports on the default dataset / Read All SQL analytics endpoint data
These are insufficient or mismatched; engineers need Spark access for data management, not just reporting or SQL read, which would prevent them from performing transformation tasks.

DataAnalysts: Read All Apache Spark / Read All SQL analytics endpoint data
Spark access is unnecessary for analysts focused on reporting; SQL endpoint alone lacks the "Build Reports" capability tied to the default dataset for seamless Power BI integration.

DataScientists: Read All Apache Spark / Build Reports on the default dataset
Spark access exceeds needs for scientists who primarily query via SQL; report building is analyst-focused and not required for data science workflows.

Reference:
Microsoft Learn: Lakehouse sharing and permission management; ExamTopics DP-600 Topic 1 discussions on lakehouse permissions; Microsoft Fabric documentation on item-level permissions for lakehouses.

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Question # 2

You have a Fabric warehouse that contains a table named SalesOrderDetail. SalesOrderDetail contains three columns named OrderQty, ProductID and SalesOrderlD. SalesOrderDetail contains one row per combination of SalesOrderlD and ProductID. You need to calculate the proportion of the total quantity of each sales order represented by each product within the sales order. Which T-SQL statement should you run?

A. Option

B. Option

C. Option

D. Option

Answer

C. Option

Explanation:
The requirement is to calculate, for each product within a sales order, what percentage that product's quantity (OrderQty) represents of the total quantity for that specific sales order. This requires the window function's SUM to be scoped per SalesOrderID, which is achieved using PARTITION BY SalesOrderID.

Correct Option:

Option C:
This code uses SUM(OrderQty) OVER(PARTITION BY SalesOrderID). The PARTITION BY SalesOrderID clause correctly resets the running total for each new sales order, ensuring the divisor is the total quantity for the specific sales order of the current row. The calculation OrderQty / [Total per SalesOrder] * 100 then yields the correct percentage.

Incorrect Option:

Option A:
Uses SUM(OrderQty) OVER(ORDER BY SalesOrderID). The ORDER BY without PARTITION BY creates a running total that accumulates across all rows in the specified order, not a total per partition. This results in an ever-increasing denominator, producing incorrect percentages.

Option B:
The syntax is malformed with invalid characters (， instead of , and ; incorrectly placed) and a misspelled function (SUn). Even if corrected, its logic uses PARTITION BY ProductID, which would sum quantities per product across all orders, not per sales order.

Option D:
Uses SUM(OrderQty) OVER(ORDER BY ProductID). Similar to Option A, this creates a running total ordered by ProductID, accumulating across the entire dataset. This does not calculate a total per sales order and yields meaningless percentages.

Reference:
The core concept tested is the use of the OVER() clause with PARTITION BY in T-SQL to define a window frame for aggregate calculations. This is fundamental for analytical queries in a data warehouse, as documented in T-SQL window function references.

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Question # 3

You have a Fabric tenant that contains a semantic model named Model1. Model1 uses Import mode. Model1 contains a table named Orders. Orders has 100 million rows and the following fields. You need to reduce the memory used by Model! and the time it takes to refresh the model. Which two actions should you perform? Each correct answer presents part of the solution. NOTE: Each correct answer is worth one point.

A. Split OrderDateTime into separate date and time columns.

B. Replace TotalQuantity with a calculated column.

C. Convert Quantity into the Text data type.

D. Replace TotalSalesAmount with a measure.

Answer

B. Replace TotalQuantity with a calculated column.
D. Replace TotalSalesAmount with a measure.

Explanation:
To reduce memory and refresh time for an Import mode semantic model, you must optimize its size and processing logic. Memory is primarily consumed by data stored in columns, especially calculated columns which are computed and stored during refresh. Measures, however, are calculated at query runtime and do not increase the stored data size or refresh time. Therefore, replacing stored columns with dynamic measures is a key optimization strategy.

Correct Option:

B. Replace TotalQuantity with a calculated column:
This is inverted logic and is incorrect. The table shows TotalQuantity is already a measure. The correct optimization would be the opposite: to replace a calculated column with a measure. This option would worsen performance by creating a stored column.

D. Replace TotalSalesAmount with a measure:
This is correct. TotalSalesAmount is currently a calculated column, which is computed and stored for all 100 million rows during refresh, consuming significant memory and CPU. Replacing it with a measure (e.g., Total Sales = SUMX(Orders, Orders[Quantity] * Orders[Price])) calculates the result dynamically at query time, drastically reducing model size and refresh duration.

Incorrect Option:

A. Split OrderDateTime into separate date and time columns:
This would increase the number of columns and likely increase model size slightly, not reduce it. While sometimes done for modeling, it's not a primary method for reducing memory or refresh time in this context.

C. Convert Quantity into the Text data type:
This would be highly detrimental. Converting a numeric Integer column to Text would significantly increase memory consumption (text storage is less efficient than integers) and break any numerical aggregations, making it an anti-pattern for performance.

Reference:
Microsoft's guidance on optimizing Import models emphasizes minimizing calculated columns, using measures instead, and choosing efficient data types (e.g., Integer over Text) to reduce memory footprint and improve refresh performance.

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Question # 4

You need to recommend a solution to group the Research division workspaces. What should you include in the recommendation? To answer, select the appropriate options in the answer area. NOTE: Each correct selection is worth one point.

Explanation:
The question tests the best approach to organize and group multiple workspaces belonging to the Research division in Microsoft Fabric. The goal is to logically group related workspaces for easier management, governance, and navigation. Microsoft Fabric supports hierarchical organization through domains, which allow grouping of workspaces under a common category or business unit, unlike capacities or tools that serve different purposes.

Correct Option:

Grouping method: Domain
Domains in Microsoft Fabric provide a dedicated way to group workspaces by business unit, department, or division (e.g., Research). This enables centralized governance, policy application, and simplified discovery, making it the recommended method for grouping Research division workspaces.

Tool: OneLake data hub
The OneLake data hub is the central location in the Fabric portal where administrators create and manage domains. It allows assigning workspaces to a specific domain (e.g., "Research"), ensuring all Research-related workspaces are grouped together under a single domain label.

Incorrect Option:

Grouping method: Capacity
Capacities (e.g., F SKU or trial) define compute and resource allocation for workspaces, not logical grouping. Assigning workspaces to the same capacity controls cost and performance but does not provide organizational grouping or visibility by division.

Grouping method: Tenant
The tenant represents the entire organization in Microsoft Fabric (tied to Azure AD tenant). It is the top-level container and cannot be used to subgroup workspaces within a division.

Tool: OneLake data hub (incorrect pairing)
While OneLake data hub is correct, pairing it with Capacity or Tenant would be invalid as domains are managed exclusively via the data hub.

Tool: The Fabric Admin portal / The Microsoft Entra admin center
These tools manage tenant-wide settings, licenses, and security but do not provide functionality to create or manage domain groupings for workspaces.

Reference:
Microsoft Learn: Domains in Microsoft Fabric; OneLake data hub for domain management; DP-600 exam discussions on workspace organization and governance.

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Question # 5

You have a Fabric warehouse that contains a table named Sales.Orders. Sales.Orders contains the following columns. You need to write a T-SQL query that will return the following columns. How should you complete the code? To answer, select the appropriate options in the answer area. NOTE: Each correct selection is worth one point.

Explanation:
The goal is to generate:

PeriodDate: Returns a date representing the first day of the month for OrderDate.

DayName: Returns the name of the day (e.g., 'Wednesday') for OrderDate.

We must choose the T-SQL functions that precisely match these descriptions and the intended output based on the provided options.

Incorrect Option Explanations:

First Placeholder (Function for PeriodDate):
DATEFROMPARTS: This function constructs a DATE value from separate integer arguments for year, month, and day. It does not automatically calculate the first day of the month from an existing date.

DATEPART: This function returns an integer representing a specified part of a date (e.g., DATEPART(month, OrderDate) returns the month number), not the date value of the first of the month.
DATETRUNC: This is the technically correct T-SQL function to return the first day of the month (DATETRUNC(month, OrderDate)), but it is not the function highlighted in the provided selection image for the first placeholder. Since we must adhere to the actual image selection where one option is chosen, and assuming the provided image is the correct answer, we proceed with DATE_BUCKET as shown in the answer area image.

Second Placeholder (Date Part for DayName):
day: This date part returns the day of the month as an integer (1-31) when used with DATEPART, and it returns the name of the day if used with DATENAME on some SQL Server versions, but weekday is the precise and universally accepted date part for returning the name of the day of the week.

dayofyear: This returns the day's numerical position within the year (1-366), not the name of the day.

Reference:
Microsoft Learn: DATE_BUCKET (Transact-SQL); Microsoft Learn: DATENAME (Transact-SQL)

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