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

Step 1 – Understanding star schema design

Fact tables → contain transactional, numeric, and foreign key columns (e.g., TransactionID, Date, SalesAmount, ProductID).

Dimension tables → contain descriptive attributes and business keys (e.g., ProductID, ProductName, ProductColor).

Step 2 – Column classification

TransactionID → Fact (surrogate key for the fact table).

Date → Goes into a Date Dimension (not into DimProduct).

ProductID → Business key → belongs in DimProduct.

ProductColor → Descriptive attribute → belongs in DimProduct.

ProductName → Descriptive attribute → belongs in DimProduct.

SalesAmount → Fact (measure, belongs in FactSales).

Step 3 – Select DimProduct columns

So DimProduct should include:

ProductID

ProductName

ProductColor

That matches option A.

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

 The issue is that USERNAME() is returning blank in Fabric because it needs the user principal name (UPN) from Microsoft Entra ID.

 Simply creating a role in the model does not fix the problem of the identity not being surfaced.

 To solve this, you need to ensure that the Entra UPN is synced correctly (via Entra Connect attribute mappings) and use the right function (USERPRINCIPALNAME() in modern models).

 Therefore, just creating a role does not meet the goal.

You should set Repeat for the schedule to Weekly (C). This allows you to specify the pipeline to run on specific days of the week, in this case, every four hours on Mondays and Fridays. References = Scheduling options for data pipelines are available in the Azure Data Factory documentation, which includes details on configuring recurring triggers.

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")

The results will form a hierarchy of folders for each partition key. - Yes

The resulting file partitions can be read in parallel across multiple nodes. - Yes

The resulting file partitions will use file compression. - No

Partitioning data by columns such as year, month, and day, as shown in the DataFrame write operation, organizes the output into a directory hierarchy that reflects the partitioning structure. This organization can improve the performance of read operations, as queries that filter by the partitioned columns can scan only the relevant directories. Moreover, partitioning facilitates parallelism because each partition can be processed independently across different nodes in a distributed system like Spark. However, the code snippet provided does not explicitly specify that file compression should be used, so we cannot assume that the output will be compressed without additional context.

References =

DataFrame write partitionBy

Apache Spark optimization with partitioning

Comprehensive Detailed Explanation

Workspace Roles in Microsoft Fabric (same as Power BI workspaces):

Viewer

Can only read/view content.

Cannot create or edit.

Contributor

Can create and write data (datasets, reports, semantic models).

Cannot manage permissions or workspace settings.

Member

Can create and edit content.

Can also add/remove people to the workspace (except Admins).

Cannot delete the workspace itself.

Admin

Full control: can configure settings, add/remove members, and delete the workspace.

Step 1: Group1 Requirements

Must be able to write data.

Must NOT be able to add members.

→ Contributor (can write but cannot manage membership).

Step 2: Group2 Requirements

Must be able to configure and maintain workspace settings.

→ Only Admin role allows configuring workspace-level settings.

→ Admin.

Step 3: Group3 Requirements

Must be able to write data.

Must be able to add members.

Must NOT be able to delete the workspace.

→ This matches the Member role.

User-defined aggregations (A) and query caching (C) are two features that can help reduce query execution time. User-defined aggregations allow precalculation of large datasets, and query caching stores the results of queries temporarily to speed up future queries. References = Microsoft Power BI documentation on performance optimization offers in-depth knowledge on these features.

Comprehensive Detailed Explanation

We are tasked with writing a T-SQL query to denormalize the tables Employee and Contract in a Fabric warehouse.

Requirements Breakdown

Include attributes from matching rows in the Contract table

This means we must join Employee and Contract on EmployeeID.

Ensure that all rows from the Employee table are preserved

This requires a LEFT OUTER JOIN from Employee to Contract. If an employee has no contract, we still want the employee record included.

Return the total number of employees per contract type for all contract types that have more than two employees

This means we need to:

Group by ContractType

Count distinct EmployeeID

Filter groups where that count > 2.

To filter aggregated results, we use HAVING, not WHERE.

Completed Query

WITH result AS (

SELECT

e.EmployeeID,

e.EmployeeName,

e.EmployeePosition,

c.ContractType,

c.StartDate

FROM Employee AS e

LEFT OUTER JOIN Contract AS c

ON c.EmployeeID = e.EmployeeID

)

SELECT

ContractType,

COUNT(DISTINCT EmployeeID) AS TotalEmployees

FROM result

GROUP BY ContractType

HAVING COUNT(DISTINCT EmployeeID) > 2;

Why This is Correct

LEFT OUTER JOIN ensures all employees are included, regardless of contracts.

HAVING allows filtering aggregated groups by employee counts.

The query returns ContractType, StartDate (as requested in denormalization), and aggregated employee counts.

References

T-SQL JOINs in Microsoft Fabric

Aggregate functions and HAVING clause

Denormalization best practices in Fabric warehouses

Requirement

Fabric workspace: Workspace1

Contains: Warehouse1, Model1, Report1

Task: Allow User1 to access a single table in Warehouse1.

Must follow principle of least privilege → grant only the exact permissions required.

Step 1: Evaluate Options

A. Assign object level permissions to User1 for Warehouse1.

In Fabric warehouses, you can grant permissions at the object level (table, schema, or column).

If the requirement is access to only one table, the correct approach is to grant SELECT permissions on that specific table.

This satisfies least privilege.

Correct. ✅

B. Assign the db_datareader role to User1 for Warehouse1.

db_datareader provides read access to all tables in the database/warehouse.

This violates least privilege.

Not correct.

C. Share Warehouse1 with User1.

Sharing grants access to the whole warehouse.

Too broad, not least privilege.

Not correct.

D. Assign the Viewer role to User1 for Workspace1.

Viewer role allows seeing all items in the workspace (warehouse, model, reports).

This would expose more than the single table.

Not correct.

Step 2: Correct Action

Use object-level permissions:

GRANT SELECT ON dbo.TableName TO [User1];

This ensures User1 can only query that specific table, nothing else.

References

Manage object-level security in Microsoft Fabric warehouses

Principle of least privilege in Fabric

User1 must be able to:

Create a new domain.

Create two subdomains.

Assign Workspace1 to subdomain1.

Assign Workspace2 to subdomain2.

Key Role Definitions in Fabric:

Domain admin: Can manage domain settings, add/remove workspaces, manage contributors.

Domain contributor: Can contribute to an existing domain but cannot create a domain.

Workspace admin: Only controls permissions/settings inside a workspace, not domains.

Fabric admin: Tenant-wide admin, broader than required.

Requirement: Follow least privilege.

To create a new domain → Requires Domain admin.

To assign workspaces to subdomains → Domain admin rights are sufficient.

Fabric admin is excessive and breaks least privilege.

Correct Answer:

A. Domain admin

Scenario:

Workspace1 contains: Pipeline1 and Lakehouse1.

A folder named Folder1 is created in Workspace1.

Lakehouse1 is moved into Folder1.

A deployment pipeline DeployPipeline1 deploys from Workspace1 → Workspace2.

Key Behavior:

Deployment pipelines in Microsoft Fabric maintain the folder hierarchy of items when deploying between workspaces.

If an item (Lakehouse1) is placed in a folder (Folder1), the same folder structure is replicated in the target workspace.

Items not placed in the folder (Pipeline1) remain at the root level.

Result in Workspace2 after deployment:

Pipeline1 remains at the root.

Lakehouse1 is placed inside Folder1.

Scenario Recap

Fabric tenant → Workspace1

Contains:

Lakehouse LH1 (with table signindata in schema dho)

Warehouse DW1

Task: Create a stored procedure in DW1 that deduplicates rows in signindata.

Step 1: Stored procedure structure

In T-SQL, stored procedures begin with:

AS

BEGIN

-- logic

END

So the correct option for the first blank is BEGIN.

BEGIN DISTRIBUTED TRANSACTION is not needed because we are not spanning multiple servers or needing distributed transactions.

SET is not the right way to start the logic block.

Step 2: Deduplication logic

To remove duplicates from signindata, the query should return unique rows.

The simplest way is:

SELECT DISTINCT PersonID, FirstName, LastName

FROM dho.signindata;

Thus the correct choice for the second blank is DISTINCT.

GROUP BY could also deduplicate but is less efficient here since no aggregation is requested.

TOP 100 PERCENT WITH TIES is irrelevant.

Step 3: Final T-SQL stored procedure

CREATE PROCEDURE dbo.usp_GetPerson

AS

BEGIN

SELECT DISTINCT PersonID, FirstName, LastName

FROM dho.signindata;

END;

Why this is correct

BEGIN → correct stored procedure structure.

DISTINCT → ensures deduplication of rows from signindata.

References

CREATE PROCEDURE (Transact-SQL)

DISTINCT (Transact-SQL)

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.

VACUUM in Delta Lake removes unreferenced files older than a retention period (default 7 days, but configurable, e.g., 30 days).

It ensures that the transaction log remains consistent and ACID properties are maintained.

OPTIMIZE with V-Order or Z-Order improves file organization and query performance, but it does not remove unused files.

Deleting files manually from OneLake would break the transaction log consistency.

Correct answer: C. Run the VACUUM command.

To implement row-level security (RLS) in a Fabric Warehouse (like in Azure Synapse or SQL Server):

You must define a predicate function (usually an inline table-valued function) that filters rows for each user → Function.

Then you bind that function to the table using a Security Policy → Security Policy.

A Database Role is used for group-based access control but not specifically for implementing RLS.

Stored procedures and constraints are not used for RLS.

Correct answers: A and D

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).

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

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.

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.

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.

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).

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

 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

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.

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.

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.

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

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