DP-600 Exam Dumps - Implementing Analytics Solutions Using Microsoft Fabric

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.

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

We are designing a star schema for sales transactions. In a star schema:

Dimension tables contain descriptive attributes (e.g., Customer, Product, Date).

Fact tables contain measurable, quantitative data (e.g., SalesTransactionID, Quantity, UnitPrice).

Factless fact tables capture events without numeric measures.

Junk dimensions store miscellaneous low-cardinality attributes.

The requirement says dimension tables will use Type 2 Slowly Changing Dimensions (SCD) → so Customer and Date attributes belong to Dimension tables.

Field-by-field classification

CustomerCode

A business key (natural key) for the customer dimension.

Belongs in the Customer Dimension table.

Answer: Dimension.

CustomerName

A descriptive attribute of the customer.

Belongs in the Customer Dimension table.

Answer: Dimension.

SaleDate

A time attribute used for analysis.

Belongs in the Date Dimension table.

Answer: Dimension.

Final Answer:

CustomerCode → Dimension

CustomerName → Dimension

SaleDate → Dimension

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