Free Microsoft DP-600 Practice Test & Real Exam Questions
You have a Fabric warehouse named Warehouse1 that contains a table named dbo.Product. dbo.Product contains the following columns.
You need to use a T-SQL query to add a column named PriceRange to dbo.Product. The column must categorize each product based on UnitPrice. The solution must meet the following requirements:
* If UnitPrice is 0, PriceRange is " Not for resale " .
* If UnitPrice is less than 50, PriceRange is " Under $50 " .
* If UnitPrice is between 50 and 250, PriceRange is " Under $250 " .
* In all other instances, PriceRange is " $250+ " .
How should you complete the query? 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.
You need to use a T-SQL query to add a column named PriceRange to dbo.Product. The column must categorize each product based on UnitPrice. The solution must meet the following requirements:
* If UnitPrice is 0, PriceRange is " Not for resale " .
* If UnitPrice is less than 50, PriceRange is " Under $50 " .
* If UnitPrice is between 50 and 250, PriceRange is " Under $250 " .
* In all other instances, PriceRange is " $250+ " .
How should you complete the query? 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.
Correct Answer:
Explanation:
Comprehensive Detailed Explanation
We need to create a computed column PriceRange based on the UnitPrice column in the dbo.Product table.
Step 1: Requirements
If UnitPrice = 0 # " Not for resale "
If UnitPrice < 50 # " Under $50 "
If UnitPrice > = 50 AND UnitPrice < 250 # " Under $250 "
Otherwise # " $250+ "
This matches a CASE expression in T-SQL.
Step 2: CASE Expression Structure
The syntax is:
CASE
WHEN condition THEN result
WHEN condition THEN result
ELSE result
END
Step 3: Apply to Problem
SELECT
Item,
UnitPrice,
PriceRange = CASE
WHEN UnitPrice = 0 THEN ' Not for resale '
WHEN UnitPrice < 50 THEN ' Under $50 '
WHEN UnitPrice > = 50 AND UnitPrice < 250 THEN ' Under $250 '
ELSE ' $250+ '
END
FROM [Warehouse1].[dbo] .[Product];
Step 4: Why This is Correct
CASE starts the conditional evaluation.
ELSE handles the default branch ($250+).
END closes the expression.
Meets all requirements exactly.
References
CASE expression in T-SQL
Computed columns in T-SQL
You create a semantic model by using Microsoft Power Bl Desktop. The model contains one security role named SalesRegionManager and the following tables:
* Sales
* SalesRegion
* Sales Ad dress
You need to modify the model to ensure that users assigned the SalesRegionManager role cannot see a column named Address in Sales Address.
Which three actions should you perform in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.
* Sales
* SalesRegion
* Sales Ad dress
You need to modify the model to ensure that users assigned the SalesRegionManager role cannot see a column named Address in Sales Address.
Which three actions should you perform in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.
Correct Answer:
Explanation:
To ensure that users assigned the SalesRegionManager role cannot see the Address column in the SalesAddress table, follow these steps in sequence:
Open the model in Tabular Editor.
Select the Address column in SalesAddress.
Set Object Level Security to None for SalesRegionManager.
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?
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?
Correct Answer: E
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You have an Azure Data Lake Storage Gen2 account named storage! that contains a Parquet file named sales.
parquet.
You have a Fabric tenant that contains a workspace named Workspace1.
Using a notebook in Workspace1, you need to load the content of the file to the default lakehouse. The solution must ensure that the content will display automatically as a table named Sales in Lakehouse explorer.
How should you complete the code? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
parquet.
You have a Fabric tenant that contains a workspace named Workspace1.
Using a notebook in Workspace1, you need to load the content of the file to the default lakehouse. The solution must ensure that the content will display automatically as a table named Sales in Lakehouse explorer.
How should you complete the code? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Correct Answer:
Explanation:
Step 1 - Read the Parquet file into a DataFrame
df = spark.read.parquet( " abfss://[email protected]/files/sales.parquet " ) This correctly loads the Parquet data into Spark.
Step 2 - Write into the Lakehouse as a managed table
If we want the result to be registered as a Lakehouse table and automatically appear in Lakehouse Explorer, we must:
Write the data in delta format (because Fabric Lakehouse tables are Delta tables).
Save the table under the tables folder, not files.
So the correct code is:
df.write.mode( " overwrite " ).format( " delta " ).saveAsTable( " tables/sales " ) Final Answer:
Format: delta
SaveAsTable Path: tables/sales
References:
Lakehouse tables in Microsoft Fabric
Save DataFrame as Delta Table in Spark
# Answer Selection:
First dropdown # delta
Second dropdown # tables/sales
You have a Fabric tenant that contains a machine learning model registered in a Fabric workspace. You need to use the model to generate predictions by using the predict function in a fabric notebook. Which two languages can you use to perform model scoring? Each correct answer presents a complete solution. NOTE:
Each correct answer is worth one point.
Each correct answer is worth one point.
Correct Answer: A,D
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Explanation: Only visible for Pass4Leader members. You can sign-up / login (it's free).
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?
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?
Correct Answer: B
Vote an answer
Explanation: Only visible for Pass4Leader members. You can sign-up / login (it's free).
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?
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?
Correct Answer: A
Vote an answer
Explanation: Only visible for Pass4Leader members. You can sign-up / login (it's free).
You have the following KQL query.
For each of the following statements, select Yes if the statement is true. Otherwise, select No. NOTE: Each correct selection is worth one point.
For each of the following statements, select Yes if the statement is true. Otherwise, select No. NOTE: Each correct selection is worth one point.
Correct Answer:
Explanation:
We are analyzing this KQL query:
Sales
| where Status != " Cancelled "
| where OrderDate > = ago(30d)
| summarize TotalSales = sum(SalesAmount) by ProductCategory
| where TotalSales > 0
Statement 1:
" The query excludes sales that have a Status of Cancelled. "
Yes # where Status != " Cancelled " filters them out.
Statement 2:
" The query calculates the total sales of each product category for the last 30 days. " Yes # where OrderDate > = ago(30d) ensures only last 30 days.
summarize TotalSales = sum(SalesAmount) by ProductCategory groups by category.
Statement 3:
" The query includes product categories that have had zero sales during the last 30 days. " No # The final filter where TotalSales > 0 excludes categories with zero sales.
Final Answer:
Excludes Cancelled sales # Yes
Calculates total sales by product category for last 30 days # Yes
Includes product categories with zero sales # No
References:
KQL where operator
KQL summarize operator
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.
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.
Correct Answer:
Explanation:
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
You have a Fabric tenant that contains a lakehouse.
You are using a Fabric notebook to save a large DataFrame by using the following code.
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.
You are using a Fabric notebook to save a large DataFrame by using the following code.
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.
Correct Answer:
Explanation:
The results will form a hierarchy of folders for each partition key. - Yes The resulting file partitions can be read in parallel across multiple nodes. - Yes The resulting file partitions will use file compression. - No Partitioning data by columns such as year, month, and day, as shown in the DataFrame write operation, organizes the output into a directory hierarchy that reflects the partitioning structure. This organization can improve the performance of read operations, as queries that filter by the partitioned columns can scan only the relevant directories. Moreover, partitioning facilitates parallelism because each partition can be processed independently across different nodes in a distributed system like Spark. However, the code snippet provided does not explicitly specify that file compression should be used, so we cannot assume that the output will be compressed without additional context.
References =
DataFrame write partitionBy
Apache Spark optimization with partitioning
You have a Fabric warehouse that contains a table named Sales.Orders. Sales.Orders contains the following columns.
You need to write a T-SQL query that will return the following columns.
How should you complete the code? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
You need to write a T-SQL query that will return the following columns.
How should you complete the code? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Correct Answer:
Explanation:
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.
