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

The code embeds an existing Power BI report. - No

The code creates a Power BI report. - Yes

The code displays a summary of the DataFrame. - Yes

The code provided seems to be a snippet from a SQL query or script which is neither creating nor embedding a Power BI report directly. It appears to be setting up a DataFrame for use within a larger context, potentially for visualization in Power BI, but the code itself does not perform the creation or embedding of a report. Instead, it ' s likely part of a data processing step that summarizes data.

References =

Introduction to DataFrames - Spark SQL

Power BI and Azure Databricks

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 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

ï‚· Calculation groups are a Tabular Object Model (TOM) feature.

ï‚· They cannot be authored in Power BI Desktop or the service.

ï‚· They are implemented using Tabular Editor.

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.