Microsoft DP-600 Implementing Analytics Solutions Using Microsoft Fabric Exam Practice Test

The measure should use the AVERAGE function to calculate the average value.

It should reference the Response Value column from the 'Survey' table.

The 'Number of months' should be used to define the period for the average calculation.

To calculate the average overall satisfaction score using DAX, you would need to use the AVERAGE function on the response values related to satisfaction questions. The DATESINPERIOD function will help in calculating the rolling average over the last 12 months.

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.

For the semantic model design required for the customer satisfaction report, the choices for data source authentication method and mode should be made based on security and performance considerations as per the case study provided.

Authentication method: The data should be accessed securely, and given that row-level security (RLS) is required for users executing T-SQL queries, you should use an authentication method that supports RLS. Service principal authentication is suitable for automated and secure access to the data, especially when the access needs to be controlled programmatically and is not tied to a specific user's credentials.

Mode: The report needs to show data as soon as it is updated in the data store, and it should only contain data from the current and previous year. DirectQuery mode allows for real-time reporting without importing data into the model, thus meeting the need for up-to-date data. It also allows for RLS to be implemented and enforced at the data source level, providing the necessary security measures.

Based on these considerations, the selections should be:

Authentication method: Service principal authentication

Mode: DirectQuery

Comprehensive Detailed Explanation

When assigning permissions to roles in a Fabric workspace for analytics workloads, you must align permissions with the responsibilities and tools typically used by each persona:

1. Data Engineers

Their primary tasks include building and transforming data pipelines, as well as making datasets usable by others.

The appropriate permission is Build Reports on the default dataset, because this allows them to create and manage reports using the curated semantic model while preparing data for analysts and scientists.

2. Data Analysts

Analysts work heavily with Apache Spark for exploratory analysis, cleansing, and shaping data.

The correct permission is Read All Apache Spark, as this grants them the ability to query and analyze Spark-based data directly without elevated permissions that are unnecessary for their role.

3. Data Scientists

Data scientists often use SQL analytics endpoints to run queries, train models, and integrate data into machine learning workflows.

The correct permission is Read All SQL analytics endpoint data, since it enables direct programmatic access to the SQL endpoint, which is needed for advanced modeling and experimentation.

Summary of Selections:

DataEngineers → Build Reports on the default dataset

DataAnalysts → Read All Apache Spark

DataScientists → Read All SQL analytics endpoint data

References (Microsoft Fabric – DP-600 exam scope):

Workspace roles and permissions in Microsoft Fabric

SQL analytics endpoint in Fabric

Apache Spark in Microsoft Fabric

Both a dataflow (A) and a Stored procedure activity in a pipeline (B) are capable of creating and populating a date dimension table. A dataflow can perform the transformation needed to create the date dimension, and it aligns with the preference for using low-code tools for data ingestion when possible. A Stored procedure could be written to generate the necessary date dimension data and executed within a pipeline, which also adheres to the technical requirements for the PoC.

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).

To meet the technical requirement that data loading activities must ensure the raw and cleansed data is updated completely before populating the dimensional model, you would need a mechanism that allows for ordered execution. A pipeline in Microsoft Fabric with dependencies set between activities can ensure that activities are executed in a specific sequence. Once set up, the pipeline can be scheduled to run at the required intervals (hourly or daily depending on the data source).

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.

You should use CREATE VIEW to make the pricing group logic available for T-SQL queries.

The CASE statement should be used to determine the pricing group based on the list price.

The T-SQL statement should create a view that classifies products into pricing groups based on the list price. The CASE statement is the correct conditional logic to assign each product to the appropriate pricing group. This view will standardize the pricing group logic across different databases and semantic models.

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

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.

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

Requirement: “The number of rows added to the Orders table during refreshes must be minimized.”

The Orders table has a sequential OrderID.

To minimize refresh size, you must only import new rows, not the entire table.

This requires retrieving the maximum OrderID currently loaded, then only loading rows with a greater OrderID.

So, the correct solution is to use a pipeline (ADF) with a dataflow to retrieve the maximum OrderID.

Answer: C. an Azure Data Factory pipeline that executes a dataflow to retrieve the maximum value of the OrderID column in the destination lakehouse

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.

 Calculation groups are a Tabular Object Model (TOM) feature.

 They cannot be authored in Power BI Desktop or the service.

 They are implemented using Tabular Editor.

Comprehensive Detailed Explanation

From the case study:

Research division data for Productline2 is currently in CSV format in storage2.

Requirement: “All the Research division data in the lakehouses must be presented as managed tables in Lakehouse explorer.”

In Fabric lakehouses, managed tables are stored in Delta format inside the Tables folder.

Step 1: Reading the source

df = spark.read.format("csv") \

options(header="true", inferSchema="true") \

load("abfss://storage1.dfs.core.windows.net/files/productline2")

This correctly ingests the CSV source from ADLS Gen2.

Step 2: Writing to Lakehouse as a managed table

You must write the data in Delta format to ensure it is queryable and managed within the lakehouse.

The correct path is under Tables/, because this is where Fabric automatically manages Lakehouse managed tables.

The target table should be named productline2, so the correct path is:

df.write.mode("overwrite").format("delta").save("Tables/productline2")

Why not other options?

CSV or Parquet formats would not create a managed Lakehouse table; they would just create files.

Writing to productline2 directly (without Tables/) would store unmanaged files in the Lakehouse Files area, not managed tables.

Writing to Tables/research/productline2 adds an unnecessary subdirectory and is not the standard structure for managed tables.

Correct Final Code

df.write.mode("overwrite").format("delta").save("Tables/productline2")

References

Managed tables in Microsoft Fabric Lakehouse

Delta Lake support in Fabric

Spark write to Lakehouse

In Azure DevOps workspace (project) security settings, role assignments determine access levels for groups:

Viewer (equivalent to the Readers security group): Grants read-only access to work items, code, pipelines, and other artifacts. Ideal for review-only scenarios where users need to view but not edit content.

Contributor (equivalent to the Contributors security group): Allows full edit and contribute privileges, such as updating work items, code reviews, and pipeline runs. Suitable for active participation in reviews that may involve feedback or changes.

Based on the group names implying review-focused access, Viewer fits Group1 for passive observation, while Contributor fits Group2 for interactive review tasks. These align with default Azure DevOps permissions for secure, least-privilege access.

To meet the requirements, developers should have Admin access to the deployment pipeline (B), Contributor access to the Development and Test workspaces (E), and Viewer access to the Production workspace (D). This setup ensures they can perform necessary actions in development and test environments without having the ability to affect production. References = The Power BI documentation on workspace access levels and deployment pipelines provides guidelines on assigning appropriate permissions.

Microsoft Power BI Desktop (A) and Tabular Editor (B) are the tools you can use to prevent report creators from using implicit measures. In Power BI Desktop, you can define explicit measures which can be used in visuals. Tabular Editor allows for advanced model editing, where you can enforce the use of explicit measures. References = Guidance on using explicit measures and preventing implicit measures in reports can be found in the Power BI and Tabular Editor official documentation.

We are analyzing the query:

SELECT e.[WWI Employee ID],

e.Employee,

e.[Preferred Name],

gdr.[WWI Employee ID] AS [Direct Report ID],

gdr.Employee AS [Direct Report]

FROM Dimension.Employee AS e

OUTER APPLY Dimension.GetDirectReports(e.[Employee Key]) AS gdr;

Key concepts:

APPLY operator: Used to join a table with a table-valued function.

OUTER APPLY ensures all rows from the left (Employee) are returned, even if the function returns no rows (similar to LEFT JOIN).

Table-valued function (TVF): Returns a set of rows (unlike scalar functions, which return a single value).

The function executes once per row from the left table, not once for the whole query.

Evaluating each statement:

"Dimension.GetDirectReports is a scalar T-SQL function."

No → It must be a table-valued function, because OUTER APPLY requires a table expression.

"The Dimension.GetDirectReports function will run only once when the query runs."

No → With APPLY, the function runs for each row of Dimension.Employee.

"The output rows will include at least one row for each row in the Dimension.Employee table."

Yes → Because of OUTER APPLY, every employee row is included, even if there are no direct reports (in that case, the columns from gdr will be NULL).

Final Answer:

Scalar function → No

Runs once per query → No

At least one row per Employee → Yes

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 DimAccount

Requirements:

Revert to previous versions of Model1.

Identify differences between saved versions.

Publish from Power BI Desktop.

Edit item definition files with Visual Studio Code.

Analysis:

To meet version control and difference tracking requirements, you must use Git integration in the Fabric workspace.

To enable editing with VS Code, you must use PBIP format (Power BI Project), which saves report/model as a folder with definition JSON files (not PBIX).

PBIX is a single binary file, not suitable for Git versioning or editing definition files.

"Enable users to edit in service" is irrelevant.

Correct Answers:

A. Enable Git integration for Workspace1.

C. Save Model1 in Power BI Desktop as a PBIP file.

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.

In Microsoft Fabric, semantic models (formerly datasets) can be managed using external tools like Tabular Editor. To push or publish changes directly to a Fabric workspace from Tabular Editor, the workspace must support XMLA endpoint connectivity with read-write access.

Explanation of Each Option

A. For Workspace1, enable Git integration.

Git integration allows source control and versioning of items in a Fabric workspace. It is intended for lifecycle management of artifacts and does not provide the connectivity required for Tabular Editor to publish semantic model changes.

B. For Workspace1, create a managed private endpoint.

A managed private endpoint provides secure connectivity between Fabric and external data sources. It is not related to the ability to publish semantic models through XMLA.

C. For Model1, enable external sharing.

External sharing governs who can access the model outside the organization. This does not affect publishing workflows from developer tools.

D. For Capacity1, set XMLA Endpoint to Read Write.

This is the correct choice. The XMLA endpoint provides a programmatic connection point for external tools like Tabular Editor. By default, the XMLA endpoint is enabled only in read-only mode. Changing the setting to Read Write allows developers to push changes, update metadata, and deploy semantic models directly from Tabular Editor into the Fabric workspace.

Summary

To publish changes to a Fabric semantic model using Tabular Editor, the XMLA endpoint of the assigned capacity must be set to Read Write. Without this configuration, Tabular Editor can only connect in read-only mode and cannot deploy updates.

References

Use the XMLA endpoint in Microsoft Fabric

Manage capacities in Microsoft Fabric

Tabular Editor and XMLA endpoint

GRANT ALTER ON SCHEMA::schemaA TO User1;

The ALTER permission allows a user to modify the schema of an object, and granting ALTER on a schema will allow the user to perform operations like TRUNCATE TABLE on any object within that schema. It is the correct permission to grant to User1 for truncating tables in schemaA.

References =

GRANT Schema Permissions

Permissions That Can Be Granted on a Schema

The transformation you should select from the context menu of the VendorID column to transform the date columns into attribute-value pairs, where columns become rows, is Unpivot columns (B). This transformation will turn the selected columns into rows with two new columns, one for the attribute (the original column names) and one for the value (the data from the cells). References = Techniques for unpivoting columns are covered in the Power Query documentation, which explains how to use the transformation in data modeling.

The command:

DESCRIBE DETAIL Customer

Returns metadata about the Delta table, such as format, schema, partitioning, size, number of files, and creation/modification timestamps.

However, it does not provide information about whether maintenance tasks (such as OPTIMIZE, VACUUM, or Z-Ordering) were performed on the table.

To check maintenance history, you would typically use:

DESCRIBE HISTORY Customer

which shows operations executed on the table (e.g., OPTIMIZE, VACUUM, MERGE).

Since DESCRIBE DETAIL does not satisfy the requirement, the solution does not meet the goal.

Step 1: Understand the scenario

Workspace1 contains a semantic model and two Power BI reports.

A Microsoft 365 Data Loss Prevention (DLP) policy (DLP1) already exists.

Requirement: Apply DLP1 to the workspace items.

In Microsoft Fabric and Power BI, DLP policies are enforced through Microsoft Purview Information Protection (MIP) sensitivity labels.

Step 2: Evaluate each option

A. Apply a master data endorsement to the semantic model.

Endorsements (promoted, certified) help users find and trust datasets but do not enforce DLP policies.

Not correct.

B. Apply a certified endorsement to the semantic model.

Certification improves discoverability and trust, but again, it does not trigger DLP policy enforcement.

Not correct.

C. Create a workspace identity.

Workspace identity (service principal) is used for connecting to external resources with managed identity.

It does not relate to DLP policy application.

Not correct.

D. Apply sensitivity labels to the semantic model and reports.

Sensitivity labels (from Microsoft Purview) integrate with Microsoft 365 DLP policies.

When sensitivity labels are applied to Power BI datasets, reports, dashboards, and workspaces, DLP1 can enforce protections such as preventing sharing or exporting sensitive data.

This is the correct answer.

Step 3: Correct Action

To apply DLP1 to Workspace1 items, you must:

Configure sensitivity labels in Microsoft Purview.

Apply those sensitivity labels to the semantic model and reports inside Workspace1.

The existing DLP1 policy will then govern data handling for those items.

References

Sensitivity labels in Power BI

Apply sensitivity labels to Power BI items

DLP policies in Microsoft Purview

There is no USEROBJECT() function in DAX. The correct functions available are USERNAME() and USERPRINCIPALNAME().

USERPRINCIPALNAME() is the one that returns the UPN directly.

Since the solution refers to a non-existent function (USEROBJECT()), it cannot solve the problem.

Correct approach: Update the measure to use USERPRINCIPALNAME(), not USEROBJECT().

Step 1 – Read the Parquet file into a DataFrame

df = spark.read.parquet("abfss://fs1@storage1.dfs.core.windows.net/files/sales.parquet")

This correctly loads the Parquet data into Spark.

Step 2 – Write into the Lakehouse as a managed table

If we want the result to be registered as a Lakehouse table and automatically appear in Lakehouse Explorer, we must:

Write the data in delta format (because Fabric Lakehouse tables are Delta tables).

Save the table under the tables folder, not files.

So the correct code is:

df.write.mode("overwrite").format("delta").saveAsTable("tables/sales")

Final Answer:

Format: delta

SaveAsTable Path: tables/sales

INSERT INTO … SELECT and SELECT INTO both physically copy all rows, which is heavy and not minimal.

CREATE TABLE AS SELECT (CTAS) also copies the data.

CREATE TABLE … AS CLONE OF creates a shallow clone in Fabric Warehouse, meaning it references the existing data without duplicating it, minimizing storage and copying.

Correct answer: B.

The pickupLongitude column has duplicate values. This can be inferred because the 'Distinct count' is 935 while the 'Count' is 1000, indicating that there are repeated values within the column. References = Microsoft Power BI documentation on data profiling could provide further insights into understanding and interpreting column statistics like these.