Free Microsoft DP-600 Practice Exam with Questions & Answers | Set: 2

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

Before specifying the partition column in the Destination settings of the Copy activity, you should set Mode to Append (A). This will allow the Copy activity to add data to the table while taking the partition column into account. References = The configuration options for Copy activities and partitioning in Azure Data Factory, which are applicable to Fabric dataflows, are outlined in the official Azure Data Factory documentation.

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 need to enable version control for a Fabric workspace (Workspace1) and ensure that Model1 (a semantic model) is added to an Azure DevOps repository.

Key facts:

Workspace1 is in Pro mode. To enable Git integration, the workspace must be assigned to a Fabric capacity.

Git integration steps in Fabric:

Assign the workspace to a Fabric capacity.

Connect the workspace to a Git provider (e.g., Azure DevOps or GitHub).

Sync the workspace with the repository (to push artifacts like semantic models).

Branch policies and deployment pipelines are not needed for simply enabling version control.

Correct Sequence:

Assign Workspace1 to a Fabric capacity.

Connect Workspace1 to a Git provider.

Sync Workspace1 with the repository.

Final Answer:

Step 1 → Assign Workspace1 to a Fabric capacity

Step 2 → Connect Workspace1 to a Git provider

Step 3 → Sync Workspace1 with the repository

Requirements:

Parallel processing → PySpark runs distributed across Spark clusters.

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

Time series analysis, anomaly 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.

o add new data to the Delta table while meeting the specified requirements:

You should use the append mode to ensure that all new data is added to the table without affecting the existing rows.

You should set the mergeSchema option to true to allow the schema of the Delta table to be updated with the new columns found in the DataFrame.

The completed code would look like this:

df.write.format("delta").mode("append")

option("mergeSchema", "true")

saveAsTable("Lakehouse1.TableName")

No, the EXPLAIN TABLE statement does not identify whether maintenance tasks were performed on a table. It shows the execution plan for a query. References = The usage and output of the EXPLAIN command can be found in the Spark SQL documentation.

The PoC is planned to be completed using a Fabric trial capacity, which implies that users involved in the PoC should be able to try paid features. However, this should be limited to specific security groups involved in the PoC to prevent the entire organization from accessing these features before the trial is proven successful (A). The ability for users to create Fabric items should also be enabled for specific security groups to ensure that only the relevant team members participating in the PoC can create items in the Fabric environment (E).

For ingesting customer data into the data store in the AnalyticsPOC workspace, a dataflow (D) should be recommended. Dataflows are designed within the Power BI service to ingest, cleanse, transform, and load data into the Power BI environment. They allow for the low-code ingestion and transformation of data as needed by Litware's technical requirements. References = You can learn more about dataflows and their use in Power BI environments in Microsoft's Power BI documentation.

A lakehouse (C) should be recommended for the AnalyticsPOC workspace. It combines the capabilities of a data warehouse with the flexibility of a data lake. A lakehouse supports semi-structured and unstructured data and allows for T-SQL and Python read access, fulfilling the technical requirements outlined for Litware. References = For further understanding, Microsoft's documentation on the lakehouse architecture provides insights into how it supports various data types and analytical operations.