Microsoft DP-600 Dumps

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

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.

Power BI Premium Per User (PPU) already supports XMLA read/write. You don’t need to upgrade to Premium capacity.

The workspace itself does not get a managed identity; rather, App1 (Azure App Service) can be configured with a managed identity.

Once App1 has a managed identity, you grant it access to the Power BI model in the workspace via XMLA endpoint.

Modifying the XMLA endpoint setting is not sufficient; authentication must come from the service (App1).

Correct answer: B.

The correct query to use in order to return data for the year 2023 that displays ProductID, ProductName, and has a summarized Amount greater than 10,000 is Option B. The reason is that it uses the GROUP BY clause to organize the data by ProductID and ProductName and then filters the result using the HAVING clause to only include groups where the sum of Amount is greater than 10,000. Additionally, the DATEPART(YEAR, SaleDate) = '2023' part of the HAVING clause ensures that only records from the year 2023 are included. References = For more information, please visit the official documentation on T-SQL queries and the GROUP BY clause at T-SQL GROUP BY.

Comprehensive Detailed Explanation

Step 1: Requirement

We need to compare the average miles per trip for:

Statutory holidays (when IsHoliday = 1)

Non-statutory holidays (when IsHoliday = 0)

Step 2: Formula for average miles per trip

Average miles per trip = total miles ÷ number of trips

Total miles → SUM(t.tripDistance)

Number of trips → COUNT(t.tripID)

So the calculation is:

(SUM(t.tripDistance) / COUNT(t.tripID)) AS MilesPerTrip

Step 3: Grouping

We need a comparison by holiday status.

So we must group the results by:

GROUP BY d.IsHoliday

This ensures we get two rows: one for IsHoliday = 1 and one for IsHoliday = 0.

Step 4: Final Query

SELECT

d.IsHoliday,

(SUM(t.tripDistance) / COUNT(t.tripID)) AS MilesPerTrip

FROM DimDate d

INNER JOIN Trips t ON d.DateID = t.DateID

GROUP BY d.IsHoliday;

Why This is Correct

The formula ensures average miles per trip.

Grouping ensures comparison between holidays vs non-holidays.

Efficient aggregation, minimal computation.

References

Aggregate functions in T-SQL

GROUP BY clause

The df.show() method also does not meet the goal. It is used to show the contents of the DataFrame, not to compute statistical functions. References = The usage of the show() function is documented in the PySpark API documentation.

Comprehensive Detailed Explanation

Step 1: Reviewing the provided DAX expression

SWITCH(

SELECTEDVALUE(Metric[Metric]),

"# of Customers", "#",

"Gross Margin %", "0.00%",

"#,###"

)

This is applied as a dynamic format string.

It changes the display formatting of the Value measure depending on the selected metric.

Step 2: Identify the feature

The screenshot shows Format = Dynamic in the Power BI Modeling tab.

This confirms that a dynamic format string has been applied.

So, for the first blank → dynamic format string.

Step 3: Identify the output formats

If the metric = # of Customers → format is "#" → whole numbers.

If the metric = Gross Margin % → format is "0.00%" → percentages.

Fallback → "#,###" → also whole numbers.

Thus, the measure supports both whole numbers and percentages.

So, for the second blank → percentages or whole numbers.

References

Dynamic format strings in Power BI

SWITCH function in DAX

When creating a OneLake shortcut to Amazon S3, authentication uses the standard AWS access key ID + secret access key pair.

SAS token is for Azure Storage, not AWS.

Certificate thumbprint is not used for S3.

Access ID is incorrect naming; the correct term is access key ID.

Correct answers: B and D.

In a Fabric Data Factory pipeline, to execute a stored procedure and make the returned values available for downstream activities, the Lookup activity is used. This activity can retrieve a dataset from a data store and pass it on for further processing. Here’s how you would use the Lookup activity in this context:

Add a Lookup activity to your pipeline.

Configure the Lookup activity to use the stored procedure by providing the necessary SQL statement or stored procedure name.

In the settings, specify that the activity should use the stored procedure mode.

Once the stored procedure executes, the Lookup activity will capture the results and make them available in the pipeline’s memory.

Downstream activities can then reference the output of the Lookup activity.

XMLA endpoint connectivity (read/write) requires the workspace to be in Fabric (Premium) capacity.

A Pro workspace does not support XMLA connections.

Therefore, to enable XMLA for Model1, you must change the License mode from Pro to Premium/Fabric.

Correct answer: B

Comprehensive Detailed Explanation

We have a KQL table (TaxiData) with columns:

VendorID

tpep_pickup_datetime (timestamp)

payment_type

total_amount

The requirement:

Create a new column FirstPickupDateTime

It should contain the first pickup timestamp per hour

Partitioning should be done by payment_type

Step 1: Which windowing function?

row_cumsum → running cumulative sum (not needed here).

row_rank_dense → assigns ranks without gaps, but does not guarantee minimum value only.

row_rank_min → gives the first/minimum value in each window partition. ✅ Correct.

row_window_session → sessionization of events, not required.

So, the correct function is row_rank_min.

Step 2: Which comparison operator?

We need to select the row where the rank = 1 (the first per partition).

So the correct operator is == (equals).

Step 3: Partitioning

The KQL query should partition by:

bin(tpep_pickup_datetime, 1h) → buckets data into 1-hour windows

payment_type → partitions further by payment type

Completed KQL Query

TaxiData

| sort by tpep_pickup_datetime asc, payment_type asc

| extend FirstPickupDateTime = row_rank_min(tpep_pickup_datetime, 1h, 0m, payment_type)

| where FirstPickupDateTime == 1

This assigns a rank within each 1-hour, per-payment-type window, then keeps the first pickup timestamp.

Why This Works

row_rank_min → ensures we capture the first occurrence in each hour.

== → filters only the first row per partition.

bin(..., 1h) ensures grouping is by hour.

References

Kusto row_rank_min() function

KQL window functions

A replica of dbo.Sales is created in the test schema by copying the metadata only. - Yes

Additional schema changes to dbo.FactSales will also apply to test.FactSales. - No

Additional data changes to dbo.FactSales will also apply to test.FactSales. - No

The CREATE TABLE AS CLONE statement creates a copy of an existing table, including its data and any associated data structures, like indexes. Therefore, the statement does not merely copy metadata; it also copies the data. However, subsequent schema changes to the original table do not automatically propagate to the cloned table. Any data changes in the original table after the clone operation will not be reflected in the clone unless explicitly updated.

References =

CREATE TABLE AS SELECT (CTAS) in SQL Data Warehouse

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

Comprehensive Detailed Explanation

We need to query a KQL database table named Readings to generate output that includes both the current reading and the previous reading values and datetime for each row.

Step 1: Understanding Requirements

The output table shows:

City, Area, MeterReading, Datetime

PrevMeterReading, PrevDatetime

The prev() function in KQL is used to access the value of a column from the previous row (based on order).

Step 2: Query Construction

We start with filtering:

Readings

| where City == "Copenhagen"

| sort by Datetime

To add columns that calculate the previous reading and previous datetime, we use extend:

| extend PrevMeterReading = prev(MeterReading), PrevDatetime = prev(Datetime)

extend creates new columns.

prev() pulls the previous row’s values.

Finally, we only want specific columns in the output. For this, we use project:

| project City, Area, MeterReading, Datetime, PrevMeterReading, PrevDatetime

Step 3: Completed Query

Readings

| where City == "Copenhagen"

| sort by Datetime

| extend PrevMeterReading = prev(MeterReading), PrevDatetime = prev(Datetime)

| project City, Area, MeterReading, Datetime, PrevMeterReading, PrevDatetime

Why This is Correct

extend adds calculated columns.

prev() gives access to the previous row.

project selects only the required columns.

This matches exactly the table shown in the question.

References

Kusto Query Language - extend operator

Kusto Query Language - project operator

prev() function in KQL

Lookup activity can execute a SQL query or stored procedure and retrieve a single row or dataset. The returned values can then be passed to downstream pipeline activities.

Switch is for branching logic, not executing procedures.

Append variable just adds values to a variable.

KQL activity executes Kusto queries, not SQL stored procedures.

Therefore, the correct choice is Lookup.

Scenario Recap

You are working in Dataflow Gen2 inside a Fabric workspace.

Dataflow1 returns 500 rows.

You want to identify the minimum and maximum values for each column in the query results.

Key Concept: Column Profiling in Power Query (Fabric Dataflows)

In Power Query (used in Dataflows), profiling tools help you inspect and understand data. The following profiling features are available:

Column quality → Shows % valid, % error, % empty values.

Does not show min/max values.

Column distribution → Shows distinct and unique value distribution (frequency).

Does not show min/max values.

Column profile → Provides statistics for each column including:

Minimum value

Maximum value

Average, Standard deviation, Count, etc.

This is the feature required here.

Details pane → Displays the profiling results when profiling options (such as column profile) are enabled.

Correct Answer Breakdown

B. Show column profile in details pane

Must be selected to display min/max, averages, and distribution in the details pane.

C. Enable details pane

Required to actually display the profiling results. Without enabling the details pane, you cannot see column profiles.

E. Enable column profile

Enables the calculation of statistics (min, max, avg, etc.) for each column.

These three together give you the ability to see min and max values.

Why the Other Options Are Incorrect

A. Show column quality details

Shows only valid/empty/error statistics. No min/max.

D. Show column value distribution

Displays distinct/unique values and counts but does not calculate min/max.

Summary

To identify min and max values per column in a Fabric Dataflow, you must:

Enable the details pane

Enable column profile

Show column profile in details pane

Correct answers: B, C, E

References

Data profiling tools in Power Query

Column profiling options in Power Query

Fabric Dataflows Gen2 overview

 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.

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

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

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

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.

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

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.

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

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.

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

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.

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).

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.