Best Microsoft DP-600 2025 Training With 112 QA's [Q47-Q70]

Best Microsoft DP-600 2025 Training With 112 QA’s [Q47-Q70]

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Best Microsoft DP-600 2025 Training With 112 QA’s

Microsoft DP-600 Certification Exam Questions

NO.47 You have a Fabric tenant that contains a new semantic model in OneLake.
You use a Fabric notebook to read the data into a Spark DataFrame.
You need to evaluate the data to calculate the min, max, mean, and standard deviation values for all the string and numeric columns.
Solution: You use the following PySpark expression:
df.explain()
Does this meet the goal?

Yes

The df.explain() method does not meet the goal of evaluating data to calculate statistical functions. It is used to display the physical plan that Spark will execute. References = The correct usage of the explain() function can be found in the PySpark documentation.

NO.48 You have a Fabric tenant that contains a warehouse.
You use a dataflow to load a new dataset from OneLake to the warehouse.
You need to add a Power Query step to identify the maximum values for the numeric columns.
Which function should you include in the step?

Table. MaxN

Table.Max

Table.Range

Table.Profile

The Table.Max function should be used in a Power Query step to identify the maximum values for the numeric columns. This function is designed to calculate the maximum value across each column in a table, which suits the requirement of finding maximum values for numeric columns. References = For detailed information on Power Query functions, including Table.Max, please refer to Power Query M function reference.

NO.49 You have a Fabric tenant that contains 30 CSV files in OneLake. The files are updated daily.
You create a Microsoft Power Bl semantic model named Modell that uses the CSV files as a data source. You configure incremental refresh for Model 1 and publish the model to a Premium capacity in the Fabric tenant.
When you initiate a refresh of Model1, the refresh fails after running out of resources.
What is a possible cause of the failure?

Query folding is occurring.

Only refresh complete days is selected.

XMLA Endpoint is set to Read Only.

Query folding is NOT occurring.

The data type of the column used to partition the data has changed.

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. Reference = The Power BI documentation on incremental refresh and query folding provides detailed information on this topic.

NO.50 You have a Fabric tenant that contains a warehouse.
A user discovers that a report that usually takes two minutes to render has been running for 45 minutes and has still not rendered.
You need to identify what is preventing the report query from completing.
Which dynamic management view (DMV) should you use?

sys.dm-exec_requests

sys.dn_.exec._sessions

sys.dm._exec._connections

sys.dm_pdw_exec_requests

NO.51 You need to resolve the issue with the pricing group classification.
How should you complete the T-SQL statement? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Explanation:

* 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.

NO.52 You have a Microsoft Power Bl semantic model.
You plan to implement calculation groups.
You need to create a calculation item that will change the context from the selected date to month-to-date (MTD).
How should you complete the DAX expression? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Explanation:

To create a calculation item that changes the context from the selected date to month-to-date (MTD), the appropriate DAX expression involves using the CALCULATE function to alter the filter context and the DATESMTD function to specify the month-to-date context.
The correct completion for the DAX expression would be:
* In the first dropdown, select CALCULATE.
* In the second dropdown, select SELECTEDMEASURE.
This would create a DAX expression in the form:
CALCULATE(
SELECTEDMEASURE(),
DATESMTD(‘Date'[DateColumn])
)

NO.53 You have a Microsoft Fabric tenant that contains a dataflow.
You are exploring a new semantic model.
From Power Query, you need to view column information as shown in the following exhibit.

Which three Data view options should you select? Each correct answer presents part of the solution. NOTE: Each correct answer is worth one point.

Enable column profile

Show column quality details

Show column profile in details pane

Enable details pane

Show column value distribution

To view column information like the one shown in the exhibit in Power Query, you need to select the options that enable profiling and display quality and distribution details. These are: A. Enable column profile – This option turns on profiling for each column, showing statistics such as distinct and unique values. B. Show column quality details – It displays the column quality bar on top of each column showing the percentage of valid, error, and empty values. E. Show column value distribution – It enables the histogram display of value distribution for each column, which visualizes how often each value occurs.

NO.54 You have a Fabric tenant that contains a warehouse.
You are designing a star schema model that will contain a customer dimension. The customer dimension table will be a Type 2 slowly changing dimension (SCD).
You need to recommend which columns to add to the table. The columns must NOT already exist in the source.
Which three types of columns should you recommend? Each correct answer presents part of the solution.
NOTE: Each correct answer is worth one point.

an effective end date and time

a foreign key

a surrogate key

a natural key

an effective start date and time

For a Type 2 slowly changing dimension (SCD), you typically need to add the following types of columns that do not exist in the source system:
An effective start date and time (E): This column records the date and time from which the data in the row is effective.
An effective end date and time (A): This column indicates until when the data in the row was effective. It allows you to keep historical records for changes over time.
A surrogate key (C): A surrogate key is a unique identifier for each row in a table, which is necessary for Type 2 SCDs to differentiate between historical and current records.

NO.55 You have a Fabric tenant that contains a lakehouse named Lakehouse1
Readings from 100 loT devices are appended to a Delta table in Lakehouse1. Each set of readings is approximately 25 KB. Approximately 10 GB of data is received daily.
All the table and SparkSession settings are set to the default.
You discover that queries are slow to execute. In addition, the lakehouse storage contains data and log files that are no longer used.
You need to remove the files that are no longer used and combine small files into larger files with a target size of 1 GB per file.
What should you do? To answer, drag the appropriate actions to the correct requirements. Each action may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content.
NOTE: Each correct selection is worth one point.

Explanation:
* Remove the files: Run the VACUUM command on a schedule.
* Combine the files: Set the optimizeWrite table setting. or Run the OPTIMIZE command on a schedule.
To remove files that are no longer used, the VACUUM command is used in Delta Lake to clean up invalid files from a table. To combine smaller files into larger ones, you can either set the optimizeWrite setting to combine files during write operations or use the OPTIMIZE command, which is a Delta Lake operation used to compact small files into larger ones.

NO.56 You have a Fabric tenant that contains 30 CSV files in OneLake. The files are updated daily.
You create a Microsoft Power Bl semantic model named Modell that uses the CSV files as a data source. You configure incremental refresh for Model 1 and publish the model to a Premium capacity in the Fabric tenant.
When you initiate a refresh of Model1, the refresh fails after running out of resources.
What is a possible cause of the failure?

Query folding is occurring.

Only refresh complete days is selected.

XMLA Endpoint is set to Read Only.

Query folding is NOT occurring.

The data type of the column used to partition the data has changed.

NO.57 You have a Fabric tenant that contains a warehouse.
Several times a day. the performance of all warehouse queries degrades. You suspect that Fabric is throttling the compute used by the warehouse.
What should you use to identify whether throttling is occurring?

the Capacity settings

the Monitoring hub

dynamic management views (DMVs)

the Microsoft Fabric Capacity Metrics app

To identify whether throttling is occurring, you should use the Monitoring hub (B). This provides a centralized place where you can monitor and manage the health, performance, and reliability of your data estate, and see if the compute resources are being throttled. References = The use of the Monitoring hub for performance management and troubleshooting is detailed in the Azure Synapse Analytics documentation.

NO.58 You have a Fabric tenant that contains a data pipeline.
You need to ensure that the pipeline runs every four hours on Mondays and Fridays.
To what should you set Repeat for the schedule?

Daily

By the minute

Hourly

Weekly

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. Reference = Scheduling options for data pipelines are available in the Azure Data Factory documentation, which includes details on configuring recurring triggers.

NO.59 You have a Fabric notebook that has the Python code and output shown in the following exhibit.

Which type of analytics are you performing?

predictive

descriptive

prescriptive

diagnostic

The Python code and output shown in the exhibit display a histogram, which is a representation of the distribution of data. This kind of analysis is descriptive analytics, which is used to describe or summarize the features of a dataset. Descriptive analytics answers the question of “what has happened” by providing insight into past data through tools such as mean, median, mode, standard deviation, and graphical representations like histograms.
References: Descriptive analytics and the use of histograms as a way to visualize data distribution are basic concepts in data analysis, often covered in introductory analytics and Python programming resources.

NO.60 You have a Fabric tenant that contains a workspace named Workspace1. Workspace1 contains a lakehouse named Lakehouse1 and a warehouse named Warehouse1.
You need to create a new table in Warehouse1 named POSCustomers by querying the customer table in Lakehouse1.
How should you complete the T-SQL statement? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

NO.61 You have a Microsoft Power B1 report and a semantic model that uses Direct Lake mode. From Power Si Desktop, you open Performance analyzer as shown in the following exhibit.

Use the drop-down menus to select the answer choice that completes each statement based on the information presented in the graphic. NOTE: Each correct selection is worth one point.

Explanation:
* The Direct Lake fallback behavior is set to: DirectQueryOnly
* The query for the table visual is executed by using: DirectQuery
In the context of Microsoft Power BI, when using DirectQuery in Direct Lake mode, there is no caching of data and all queries are sent directly to the underlying data source. The Performance Analyzer tool shows the time taken for different operations, and from the options provided, it indicates that DirectQuery mode is being used for the visuals, which is consistent with the Direct Lake setting. DirectQueryOnly as the fallback behavior ensures that only DirectQuery will be used without reverting to import mode.

NO.62 You have a Fabric tenant that contains a complex semantic model. The model is based on a star schema and contains many tables, including a fact table named Sales. You need to create a diagram of the model. The diagram must contain only the Sales table and related tables. What should you use from Microsoft Power Bl Desktop?

data categories

Data view

Model view

DAX query view

To create a diagram that contains only the Sales table and related tables, you should use the Model view (C) in Microsoft Power BI Desktop. This view allows you to visualize and manage the relationships between tables within your semantic model. Reference = Microsoft Power BI Desktop documentation outlines the functionalities available in Model view for managing semantic models.

NO.63 You to need assign permissions for the data store in the AnalyticsPOC workspace. The solution must meet the security requirements.
Which additional permissions should you assign when you share the data store? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Explanation:
* Data Engineers: Read All SQL analytics endpoint data
* Data Analysts: Read All Apache Spark
* Data Scientists: Read All SQL analytics endpoint data
The permissions for the data store in the AnalyticsPOC workspace should align with the principle of least privilege:
* Data Engineers need read and write access but not to datasets or reports.
* Data Analysts require read access specifically to the dimensional model objects and the ability to create Power BI reports.
* Data Scientists need read access via Spark notebooks. These settings ensure each role has the necessary permissions to fulfill their responsibilities without exceeding their required access level.

NO.64 You have a Fabric tenant that contains a semantic model. The model contains data about retail stores.
You need to write a DAX query that will be executed by using the XMLA endpoint The query must return a table of stores that have opened since December 1,2023.
How should you complete the DAX expression? To answer, drag the appropriate values to the correct targets. Each value may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content.
NOTE: Each correct selection is worth one point.

NO.65 You are creating a dataflow in Fabric to ingest data from an Azure SQL database by using a T-SQL statement.
You need to ensure that any foldable Power Query transformation steps are processed by the Microsoft SQL Server engine.
How should you complete the code? To answer, drag the appropriate values to the correct targets. Each value may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content.
NOTE: Each correct selection is worth one point.

Explanation:
You should complete the code as follows:
* Table
* NativeQuery
* EnableFolding
In Power Query, using Table before the SQL statement ensures that the result of the SQL query is treated as a table. NativeQuery allows a native database query to be passed through from Power Query to the source database. The EnableFolding option ensures that any subsequent transformations that can be folded will be sent back and executed at the source database (Microsoft SQL Server engine in this case).

NO.66 You have a Fabric workspace named Workspace1 that contains a data flow named Dataflow1. Dataflow1 contains a query that returns the data shown in the following exhibit.

You need to transform the date columns into attribute-value pairs, where columns become rows.
You select the VendorlD column.
Which transformation should you select from the context menu of the VendorlD column?

Group by

Unpivot columns

Unpivot other columns

Split column

Remove other columns

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.

NO.67 You are implementing two dimension tables named Customers and Products in a Fabric warehouse.
You need to use slowly changing dimension (SCO) to manage the versioning of dat a. The solution must meet the requirements shown in the following table.

Which type of SCD should you use for each table? To answer, drag the appropriate SCD types to the correct tables. Each SCD type may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content.
NOTE: Each correct selection is worth one point.

NO.68 You have a Fabric tenant that contains a new semantic model in OneLake.
You use a Fabric notebook to read the data into a Spark DataFrame.
You need to evaluate the data to calculate the min, max, mean, and standard deviation values for all the string and numeric columns.
Solution: You use the following PySpark expression:
df .sumary ()
Does this meet the goal?

Yes

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.

NO.69 You have a Fabric workspace named Workspace1 that contains a data flow named Dataflow1. Dataflow1 contains a query that returns the data shown in the following exhibit.

You need to transform the date columns into attribute-value pairs, where columns become rows.
You select the VendorlD column.
Which transformation should you select from the context menu of the VendorlD column?

Group by

Unpivot columns

Unpivot other columns

Split column

Remove other columns

NO.70 Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution.
After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear in the review screen.
You have a Fabric tenant that contains a semantic model named Model1.
You discover that the following query performs slowly against Model1.

You need to reduce the execution time of the query.
Solution: You replace line 4 by using the following code:

Does this meet the goal?

Yes

Topic 1, Litware. Inc. Case Study
Overview
Litware. Inc. is a manufacturing company that has offices throughout North America. The analytics team at Litware contains data engineers, analytics engineers, data analysts, and data scientists.
Existing Environment
litware has been using a Microsoft Power Bl tenant for three years. Litware has NOT enabled any Fabric capacities and features.
Fabric Environment
Litware has data that must be analyzed as shown in the following table.

The Product data contains a single table and the following columns.

The customer satisfaction data contains the following tables:
* Survey
* Question
* Response
For each survey submitted, the following occurs:
* One row is added to the Survey table.
* One row is added to the Response table for each question in the survey.
The Question table contains the text of each survey question. The third question in each survey response is an overall satisfaction score. Customers can submit a survey after each purchase.
User Problems
The analytics team has large volumes of data, some of which is semi-structured. The team wants to use Fabric to create a new data store.
Product data is often classified into three pricing groups: high, medium, and low. This logic is implemented in several databases and semantic models, but the logic does NOT always match across implementations.
Planned Changes
Litware plans to enable Fabric features in the existing tenant. The analytics team will create a new data store as a proof of concept (PoC). The remaining Litware users will only get access to the Fabric features once the PoC is complete. The PoC will be completed by using a Fabric trial capacity.
The following three workspaces will be created:
* AnalyticsPOC: Will contain the data store, semantic models, reports, pipelines, dataflows, and notebooks used to populate the data store
* DataEngPOC: Will contain all the pipelines, dataflows, and notebooks used to populate Onelake
* DataSciPOC: Will contain all the notebooks and reports created by the data scientists The following will be created in the AnalyticsPOC workspace:
* A data store (type to be decided)
* A custom semantic model
* A default semantic model
* Interactive reports
The data engineers will create data pipelines to load data to OneLake either hourly or daily depending on the data source. The analytics engineers will create processes to ingest transform, and load the data to the data store in the AnalyticsPOC workspace daily. Whenever possible, the data engineers will use low-code tools for data ingestion. The choice of which data cleansing and transformation tools to use will be at the data engineers’ discretion.
All the semantic models and reports in the Analytics POC workspace will use the data store as the sole data source.
Technical Requirements
The data store must support the following:
* Read access by using T-SQL or Python
* Semi-structured and unstructured data
* Row-level security (RLS) for users executing T-SQL queries
Files loaded by the data engineers to OneLake will be stored in the Parquet format and will meet Delta Lake specifications.
Data will be loaded without transformation in one area of the AnalyticsPOC data store. The data will then be cleansed, merged, and transformed into a dimensional model.
The data load process must ensure that the raw and cleansed data is updated completely before populating the dimensional model.
The dimensional model must contain a date dimension. There is no existing data source for the date dimension. The Litware fiscal year matches the calendar year. The date dimension must always contain dates from 2010 through the end of the current year.
The product pricing group logic must be maintained by the analytics engineers in a single location. The pricing group data must be made available in the data store for T-SQL queries and in the default semantic model. The following logic must be used:
* List prices that are less than or equal to 50 are in the low pricing group.
* List prices that are greater than 50 and less than or equal to 1,000 are in the medium pricing group.
* List pnces that are greater than 1,000 are in the high pricing group.
Security Requirements
Only Fabric administrators and the analytics team must be able to see the Fabric items created as part of the PoC. Litware identifies the following security requirements for the Fabric items in the AnalyticsPOC workspace:
* Fabric administrators will be the workspace administrators.
* The data engineers must be able to read from and write to the data store. No access must be granted to datasets or reports.
* The analytics engineers must be able to read from, write to, and create schemas in the data store. They also must be able to create and share semantic models with the data analysts and view and modify all reports in the workspace.
* The data scientists must be able to read from the data store, but not write to it. They will access the data by using a Spark notebook.
* The data analysts must have read access to only the dimensional model objects in the data store. They also must have access to create Power Bl reports by using the semantic models created by the analytics engineers.
* The date dimension must be available to all users of the data store.
* The principle of least privilege must be followed.
Both the default and custom semantic models must include only tables or views from the dimensional model in the data store. Litware already has the following Microsoft Entra security groups:
* FabricAdmins: Fabric administrators
* AnalyticsTeam: All the members of the analytics team
* DataAnalysts: The data analysts on the analytics team
* DataScientists: The data scientists on the analytics team
* Data Engineers: The data engineers on the analytics team
* Analytics Engineers: The analytics engineers on the analytics team
Report Requirements
The data analysis must create a customer satisfaction report that meets the following requirements:
* Enables a user to select a product to filter customer survey responses to only those who have purchased that product
* Displays the average overall satisfaction score of all the surveys submitted during the last 12 months up to a selected date
* Shows data as soon as the data is updated in the data store
* Ensures that the report and the semantic model only contain data from the current and previous year
* Ensures that the report respects any table-level security specified in the source data store
* Minimizes the execution time of report queries

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Microsoft DP-600 Exam Syllabus Topics:

Topic	Details
Topic 1	Prepare and serve data: In this topic, questions about creating objects in a lakehouse or warehouse, copying data, transforming data, and optimizing performance appear.
Topic 2	Plan, implement, and manage a solution for data analytics: Planning a data analytics environment, implementing and managing a data analytics environment are discussed in this topic. It also focuses on managing the analytics development lifecycle.
Topic 3	Explore and analyze data: It also deals with performing exploratory analytics. Moreover, the topic delves into query data by using SQL.
Topic 4	Implement and manage semantic models: The topic delves into designing and building semantic models, and optimizing enterprise-scale semantic models.

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