Free and Premium Microsoft DP-600 Dumps Questions Answers

Yes, the df.summary() method does meet the goal. This method is used to compute specified statistics for numeric and string columns. By default, it provides statistics such as count, mean, stddev, min, and max. References = The PySpark API documentation details the summary() function and the statistics it provides.

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.

Workspace_DEV contents:

Lakehouse1, Notebook1, Pipeline1, SemanticModel1

Workspace_TEST contents (before deployment):

Lakehouse2, Notebook2, SemanticModel1

After deployment:

SemanticModel1 → same name, so it will be paired and overwritten with the DEV version.

Lakehouse1 and Notebook1 → new items, so they will be added to TEST.

Lakehouse2 and Notebook2 → remain because they don’t conflict in name.

Pipeline1 → new item, so it will also be added.

So the final content is:

Lakehouse1, Lakehouse2, Notebook1, Notebook2, Pipeline1, SemanticModel1

Answer: B

For the PeriodDate that returns the first day of the month for OrderDate, you should use DATEFROMPARTS as it allows you to construct a date from its individual components (year, month, day).

For the DayName that returns the name of the day for OrderDate, you should use DATENAME with the weekday date part to get the full name of the weekday.

The complete SQL query should look like this:

SELECT OrderID, CustomerID,

DATEFROMPARTS(YEAR(OrderDate), MONTH(OrderDate), 1) AS PeriodDate,

DATENAME(weekday, OrderDate) AS DayName

FROM Sales.Orders

Select DATEFROMPARTS for the PeriodDate and weekday for the DayName in the answer area.

A possible cause for the failure is that query folding is NOT occurring (D). Query folding helps optimize refresh by pushing down the query logic to the source system, reducing the amount of data processed and transferred, hence conserving resources. References = The Power BI documentation on incremental refresh and query folding provides detailed information on this topic.

Scenario:

Dev = Workspace1 with Dataflow Gen2 (source = blob storage CSV).

Prod = Workspace2, different storage account.

Need: ensure deployed dataflow points to the production storage location.

Analysis:

Data source rules: used to remap data sources between environments (e.g., blob storage dev → blob storage prod).

Parameter rules: used when the data source location is parameterized (for example, a parameter storing the file path or connection string).

Best practice: use parameters in the dataflow for connection strings, then apply parameter rules in deployment pipelines.

In this case, since the requirement is about ensuring the reference updates correctly, only parameter rules are needed (not data source rules).

Correct Answer:

B. Create a parameter rule only.

Comprehensive Detailed Explanation

We need to calculate the proportion of the total quantity of each sales order represented by each product within the sales order.

Step 1: Analyze the requirement

Table: SalesOrderDetail

Columns: OrderQty, ProductID, SalesOrderID

Each row = one product in a sales order.

Requirement: For each SalesOrderID, calculate what percentage each product contributes to the total order quantity.

This means we must:

Calculate the total order quantity per SalesOrderID.

Divide each product’s OrderQty by that total.

Express it as a percentage.

Step 2: Evaluate the options

A. Uses OVER(ORDER BY ProductID)

Wrong: ORDER BY does not segment data, it only defines sequence. We need grouping by sales order.

B. Uses OVER(PARTITION BY ProductID)

Wrong: This would calculate percentages per product across all sales orders, not per sales order.

C. Uses OVER(PARTITION BY SalesOrderID)

Correct: This computes the total OrderQty per SalesOrderID and divides each product’s quantity by that total. Exactly what the requirement asks.

D. Uses OVER(ORDER BY SalesOrderID)

Wrong: Again, ORDER BY just sequences rows, does not group them.

Step 3: Correct Query

SELECT

SalesOrderID,

ProductID,

OrderQty,

CAST(1. * OrderQty / SUM(OrderQty)

OVER(PARTITION BY SalesOrderID) * 100 AS DECIMAL(5,2))

AS PercentByProductID

FROM Sales.SalesOrderDetail;

Why Option C is Correct

PARTITION BY SalesOrderID ensures the denominator is the total quantity for the current order.

This produces the correct percentage breakdown of each product inside its sales order.

References

T-SQL Window Functions

Aggregate Functions with PARTITION BY

Microsoft Fabric Warehouse T-SQL Support

The Vertipaq Analyzer tool (B) and querying the $system.discovered_STORAGE_TABLE_COLUMNS_IN_SEGMENTS dynamic management view (DMV) (C) can help identify which columns are frequently loaded into memory. Both methods provide insights into the storage and retrieval aspects of the semantic model. References = The Power BI documentation on Vertipaq Analyzer and DMV queries offers detailed guidance on how to use these tools for performance analysis.

CTAS creates a new table from the results of a query, automatically inheriting the column types of the source tables. It is the simplest and most efficient way to create a point-in-time (PIT) table.

INSERT INTO requires a table to already exist.

CREATE MATERIALIZED VIEW keeps results up to date but is heavier and not required for PIT.

CLONE creates a shallow copy of an existing table, but we are joining two tables, so clone is not applicable.

Correct: A.

The issue: USERNAME() returns blank because Fabric uses Microsoft Entra (Azure AD) identities, not on-premises AD DS identities.

Adding user objects to sync via Entra Connect does not solve the issue by itself.

The correct fix is to ensure the UPN is synchronized and properly mapped (usually via Entra Connect attribute mappings).

What is needed is to ensure the userPrincipalName attribute is synced, not just “user objects.”

Correct answer: No.

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 relationship between OrderItem and Product must be based on: Both the CompanyID and the ProductID columns

The Company entity must be: Denormalized into the Customer and Product entities

In a dimensional model, the relationships are typically based on foreign key constraints between the fact table (OrderItem) and dimension tables (Product, Customer, Date). Since CompanyID is present in both the OrderItem and Product tables, it acts as a foreign key in the relationship. Similarly, ProductID is a foreign key that relates these two tables. To enable analysis by date, product, and customer, the Company entity would need to be denormalized into the Customer and Product entities to ensure that the relevant company information is available within those dimensions for querying and reporting purposes.

References =

Dimensional modeling

Star schema design

In Microsoft Fabric, the OneLake endpoint allows users with appropriate permissions to read data from a lakehouse, leveraging the unified data storage system. Since DBUser has been granted access to Lakehouse1, they can utilize the OneLake endpoint for reading data. For querying, the OneLake file explorer provides a user interface to interact with and query data within the lakehouse, aligning with DBUser's access rights without requiring broader tenant permissions.

In the ROW_NUMBER() function, choose OVER (PARTITION BY CustomerID ORDER BY LastUpdated DESC).

In the WHERE clause, choose WHERE X = 1.

To select the most recent row for each customer ID, you use the ROW_NUMBER() window function partitioned by CustomerID and ordered by LastUpdated in descending order. This will assign a row number of 1 to the most recent update for each customer. By selecting rows where the row number (X) is 1, you get the latest update per customer.

References =

Use the OVER clause to aggregate data per partition

Use window functions

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

For users to create and publish custom Direct Lake semantic models using external tools, following the principle of least privilege, the actions to be included are enabling XMLA Endpoints (A), editing data models in Power BI service (C), and setting XMLA Endpoint to Read-Write in the capacity settings (D). References = More information can be found in the Admin portal of the Power BI service documentation, detailing tenant and capacity settings.

The display function is the correct choice to make the data available in the Chart view within a Fabric notebook. This function is used to visualize Spark DataFrames in various formats including charts and graphs directly within the notebook environment. References = Further explanation of the display function can be found in the official documentation on Azure Synapse Analytics notebooks.

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.

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

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

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

 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.

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.

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

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

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.

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.

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).