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

Requirements:

Parallel processing → PySpark runs distributed across Spark clusters.

Minimize duplication of data → OneLake shortcut + Spark queries avoid unnecessary copies.

Time series analysis, anoma ly detection, visualization → can be done in PySpark within Fabric Notebooks.

pandas (C) is single-threaded and not efficient for billions of items.

Power BI visuals (A) are good for visualization but not for transformation or large-scale anomaly detection.

Correct: B .

The correct query to use in order to return data for the year 2023 that displays ProductID, ProductName, and has a sum marized Amount greater than 10,000 is Option B. The reason is that it uses the GROUP BY clause to organize the data by ProductID and ProductName and then filters the result using the HAVING clause to only include groups where the sum of Amount is greater t han 10,000. Additionally, the DATEPART(YEAR, SaleDate) = ' 2023 ' part of the HAVING clause ensures that only records from the year 2023 are included. References = For more information, please visit the official documentation on T-SQL queries and the GROUP BY clause at T-SQL GROUP BY .

Scenario:

Tables:

DimAccount (accounts)

FactTransaction (transactions)

DimCustomer (customers)

Business rule:

A customer can have multiple accounts .

An account can be linked to multiple customers .

Many-to-many relationship.

Requirement: Calculate total transaction amounts by customer .

Analysis:

Direct many-to-many between FactTransaction and DimCustomer would not properly resolve aggregation.

Adding CustomerKey directly in FactTransaction is not possible because transactions link to accounts, not customers directly.

Correct approach: Create a bridge table (CustomerAccount) that maps Customer ↔ Account .

Then link FactTransaction → DimAccount → Bridge → DimCustomer .

Correct Answer:

C. A bridge table with relationships to DimCustomer and DimA ccount