Microsoft Updated DP-600 Exam Questions and Answers by milan

Comprehensive Detailed Explanation

We need to write a query that summarizes order quantities by year and product, and also include the yearly total for all products in each row.

Step 1: What the query needs

Extract the year from SalesOrderDetail.ModifiedDate.

Join with the Product table to get the product name.

Aggregate with SUM(OrderQty).

Return grouped data by year and product, with an additional subtotal row per year (all products combined).

Step 2: Evaluate the SELECT Clause

We must extract the year using:

YEAR(so.ModifiedDate) AS OrderDate

This converts the ModifiedDate column into a year value for grouping.

Step 3: Evaluate the GROUP BY options

CUBE(YEAR, P.Name) → Produces all combinations of year totals, product totals, and grand totals. Too many combinations, not required.

GROUPING SETS → Could achieve the result but requires explicitly listing the sets. Less direct.

ROLLUP(YEAR, P.Name) → Produces grouping by (Year, Product) and then a subtotal per Year. Exactly what is required.

YEAR only → Would group only by year, losing per-product breakdown.

Correct: ROLLUP(YEAR(so.ModifiedDate), P.Name)

Step 4: Completed Query

SELECT

YEAR(so.ModifiedDate) AS OrderDate,

p.Name,

SUM(so.OrderQty) AS OrderQty

FROM dbo.SalesOrderDetail so

INNER JOIN dbo.Product p

ON p.ProductID = so.ProductID

GROUP BY ROLLUP(YEAR(so.ModifiedDate), p.Name);

Why This Works

YEAR(so.ModifiedDate) extracts year for grouping.

ROLLUP(YEAR, P.Name) provides both product-level totals and yearly subtotals.

Ensures the requirement: “include the yearly sum of order quantities for all the products in each row.”

References

GROUP BY ROLLUP in T-SQL

Aggregate functions in Microsoft Fabric warehouses

When users interact with a report built from a Direct Lake semantic model that uses row-level security (RLS), the DAX queries will operate in DirectQuery mode (A). This is because the model directly queries the underlying data source without importing data into Power BI. References = The Power BI documentation on DirectQuery provides detailed explanations of how RLS and DAX queries function in this mode.

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