This is the latest May 2025 update of the Microsoft DP-600 dumps, featuring 126 exam questions with answers. It includes a variety of question types that reflect the real exam (see the table below) and provides PDF and VCE simulation tools, so you can choose based on your study habits. If you’re interested, check out the latest Microsoft DP-600 dumps here: https://www.leads4pass.com/dp-600.html. Try it out, and we’re confident it will lead you to success!

Total Questions:	126 Q&A
Single & Multiple Choice:	85 Q&A
Drag Drop:	10 Q&A
Hotspot:	31 Q&A
Testlet:	2 Q&A

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Practice Microsoft DP-600 dumps online

Number of exam questions	Last shared	Related
15 (Free)	How to Pass Microsoft DP-600 Exam: Detailed Analysis Solutions	PL-300,DP-900

Question 1:

You have a Fabric tenant that contains a lakehouse named lakehouse1. Lakehouse1 contains a table named Table1.

You are creating a new data pipeline.

You plan to copy external data to Table1. The schema of the external data changes regularly.

You need the copy operation to meet the following requirements:

Replace Table1 with the schema of the external data.

Replace all the data in Table1 with the rows in the external data.

You add a Copy data activity to the pipeline. What should you do for the Copy data activity?

A. From the Source tab, add additional columns.

B. From the Destination tab, set Table action to Overwrite.

C. From the Settings tab, select Enable staging

D. From the Source tab, select Enable partition discovery

E. From the Source tab, select Recursively

Correct Answer: B

Explanation: For the Copy data activity, from the Destination tab, setting Table action to Overwrite (B) will ensure that Table1 is replaced with the schema and rows of the external data, meeting the requirements of replacing both the schema and data of the destination table.

References: Information about Copy data activity and table actions in Azure Data Factory, which can be applied to data pipelines in Fabric, is available in the Azure Data Factory documentation.

Question 2:

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?

A. Yes

B. No

Correct Answer: A

Explanation: 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.

Question 3:

You are analyzing customer purchases in a Fabric notebook by using PySpanc You have the following DataFrames:

You need to join the DataFrames on the customer_id column. The solution must minimize data shuffling. You write the following code.

Which code should you run to populate the results DataFrame?

A)

B)

C)

D)

A. Option A

B. Option B

C. Option C

D. Option D

Correct Answer: A

Explanation: The correct code to populate the results DataFrame with minimal data shuffling is Option A. Using the broadcast function in PySpark is a way to minimize data movement by broadcasting the smaller DataFrame (customers) to each node in the cluster. This is ideal when one DataFrame is much smaller than the other, as in this case with customers.

References: You can refer to the official Apache Spark documentation for more details on joins and the broadcast hint.

Question 4:

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?

A. the Capacity settings

B. the Monitoring hub

C. dynamic management views (DMVs)

D. the Microsoft Fabric Capacity Metrics app

Correct Answer: B

Explanation: 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.

Question 5:

You have source data in a folder on a local computer.

You need to create a solution that will use Fabric to populate a data store. The solution must meet the following requirements:

Support the use of dataflows to load and append data to the data store.

Ensure that Delta tables are V-Order optimized and compacted automatically.

Which type of data store should you use?

A. a lakehouse

B. an Azure SQL database

C. a warehouse

D. a KQL database

Correct Answer: A

Explanation: A lakehouse (A) is the type of data store you should use. It supports dataflows to load and append data and ensures that Delta tables are Z-Order optimized and compacted automatically.

References: The capabilities of a lakehouse and its support for Delta tables are described in the lakehouse and Delta table documentation.

Question 6:

You have the source data model shown in the following exhibit.

The primary keys of the tables are indicated by a key symbol beside the columns involved in each key.

You need to create a dimensional data model that will enable the analysis of order items by date, product, and customer.

What should you include in the solution? To answer, select the appropriate options in the answer area.

NOTE: Each correct selection is worth one point.

Hot Area:

Correct Answer:

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

Question 7:

You have a Fabric tenant that contains a takehouse named lakehouse1. Lakehouse1 contains a Delta table named Customer.

When you query Customer, you discover that the query is slow to execute. You suspect that maintenance was NOT performed on the table.

You need to identify whether maintenance tasks were performed on Customer.

Solution: You run the following Spark SQL statement:

DESCRIBE HISTORY customer

Does this meet the goal?

A. Yes

B. No

Correct Answer: A

Explanation: Yes, the DESCRIBE HISTORY statement does meet the goal. It provides information on the history of operations, including maintenance tasks, performed on a Delta table.

References: The functionality of the DESCRIBE HISTORY statement can be verified in the Delta Lake documentation.

Question 8:

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.

Select and Place:

Correct Answer:

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.

Question 9:

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?

A. Yes

B. No

Correct Answer: B

Explanation: 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.

Question 10:

You have a Fabric warehouse that contains a table named Staging.Sales. Staging.Sales contains the following columns.

You need to write a T-SQL query that will return data for the year 2023 that displays ProductID and ProductName arxl has a summarized Amount that is higher than 10,000. Which query should you use?

A. Option A

B. Option B

C. Option C

D. Option D

Correct Answer: B

Explanation: 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.

Question 11:

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?

A. data categories

B. Data view

C. Model view

D. DAX query view

Correct Answer: C

Explanation: 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.

References: Microsoft Power BI Desktop documentation outlines the functionalities available in Model view for managing semantic models.

Question 12:

You have a Fabric tenant that contains a warehouse. The warehouse uses row-level security (RLS). You create a Direct Lake semantic model that uses the Delta tables and RLS of the warehouse. When users interact with a report built from the model, which mode will be used by the DAX queries?

A. DirectQuery

B. Dual

C. Direct Lake

D. Import

Correct Answer: A

Explanation: 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.

Question 13:

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.

Hot Area:

Correct Answer:

You should use CREATE VIEW to make the pricing group logic available for TSQL 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.

Question 14:

You need to create a data loading pattern for a Type 1 slowly changing dimension (SCD).

Which two actions should you include in the process? Each correct answer presents part of the solution.

NOTE: Each correct answer is worth one point.

A. Update rows when the non-key attributes have changed.

B. Insert new rows when the natural key exists in the dimension table, and the non-key attribute values have changed.

C. Update the effective end date of rows when the non-key attribute values have changed.

D. Insert new records when the natural key is a new value in the table.

Correct Answer: AD

Explanation: For a Type 1 SCD, you should include actions that update rows when non- key attributes have changed (A), and insert new records when the natural key is a new value in the table (D).

A Type 1 SCD does not track historical data, so you always overwrite the old data with the new data for a given key.

References: Details on Type 1 slowly changing dimension patterns can be found in data warehousing literature and Microsoft\’s official documentation.

Question 15:

You have a Fabric tenant that contains a lakehouse named Lakehouse1. Lakehouse1 contains a table named Nyctaxi_raw. Nyctaxi_raw contains the following columns.

You create a Fabric notebook and attach it to lakehouse1.

You need to use PySpark code to transform the data. The solution must meet the following requirements:

Correct Answer:

Add the pickupDate column: .withColumn(“pickupDate”,

df[“pickupDateTime”].cast(“date”))

Filter the DataFrame: .filter(“fareAmount > 0 AND fareAmount < 100”)

In PySpark, you can add a new column to a DataFrame using the .withColumn method, where the first argument is the new column name and the second argument is the expression to generate the content of the new column. Here, we use

the .cast(“date”) function to extract only the date part from a timestamp. To filter the DataFrame, you use the .filter method with a condition that selects rows where fareAmount is greater than 0 and less than 100, thus ensuring only positive

values less than 100 are included.

Final:

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