Free Microsoft DP-600 Korean Practice Test & Real Exam Questions

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Explanation:

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.

Explanation:
First blank: CALCULATE
Second blank: _LYSales
Step 1 - Defining the variable
We want to calculate sales for the same period in the prior year. The correct function here is CALCULATE, because it allows applying a filter context modification (with SAMEPERIODLASTYEAR).
So:
VAR _LYSales =
CALCULATE ( [Total Sales], SAMEPERIODLASTYEAR ( ' Orders ' [Order Date] ) ) Step 2 - Returning the value We then need to RETURN the value of the variable. From the dropdown options, we select _LYSales.
So:
EVALUATE
VAR _LYSales =
CALCULATE ( [Total Sales], SAMEPERIODLASTYEAR ( ' Orders ' [Order Date] ) ) RETURN ROW ( " LastYearSales " , _LYSales ) Note: In XMLA DAX queries, you usually need a table expression, so wrapping with ROW or SELECTCOLUMNS is common, but based on the provided answer box, just returning _LYSales is sufficient here.
Final Answer:
First blank: CALCULATE
Second blank: _LYSales

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Explanation:

The Spark engine will read only the ' SalesOrderNumber ' , ' OrderDate ' , ' CustomerName ' , ' UnitPrice ' columns from Sales_raw.csv. - Yes Removing the partition will reduce the execution time of the query. - No Adding inferSchema= ' true ' to the options will increase the execution time of the query. - Yes The code specifies the selection of certain columns, which means only those columns will be read into the DataFrame. Partitions in Spark are a way to optimize the execution of queries by organizing the data into parts that can be processed in parallel. Removing the partition could potentially increase the execution time because Spark would no longer be able to process the data in parallel efficiently. The inferSchema option allows Spark to automatically detect the column data types, which can increase the execution time of the initial read operation because it requires Spark to read through the data to infer the schema.

Explanation:
For the HighestSellingPrice, you should use the GREATEST function to find the highest value from the given price columns. However, T-SQL does not have a GREATEST function as found in some other SQL dialects, so you would typically use a CASE statement or an IIF statement with nested MAX functions. Since neither of those are provided in the options, you should select MAX as a placeholder to indicate the function that would be used to find the highest value if combining multiple MAX functions or a similar logic was available.
For the TradePrice, you should use the COALESCE function, which returns the first non-null value in a list.
The COALESCE function is the correct choice as it will return AgentPrice if it ' s not null; if AgentPrice is null, it will check WholesalePrice, and if that is also null, it will return ListPrice.
The complete code with the correct SQL functions would look like this:
SELECT ProductID,
MAX(ListPrice, WholesalePrice, AgentPrice) AS HighestSellingPrice, -- MAX is used as a placeholder COALESCE(AgentPrice, WholesalePrice, ListPrice) AS TradePrice FROM Sales.Products Select MAX for HighestSellingPrice and COALESCE for TradePrice in the answer area.

Explanation:

We are analyzing the query:
SELECT e.[WWI Employee ID],
e.Employee,
e.[Preferred Name],
gdr.[WWI Employee ID] AS [Direct Report ID] ,
gdr.Employee AS [Direct Report]
FROM Dimension.Employee AS e
OUTER APPLY Dimension.GetDirectReports(e.[Employee Key]) AS gdr;
Key concepts:
APPLY operator: Used to join a table with a table-valued function.
OUTER APPLY ensures all rows from the left (Employee) are returned, even if the function returns no rows (similar to LEFT JOIN).
Table-valued function (TVF): Returns a set of rows (unlike scalar functions, which return a single value).
The function executes once per row from the left table, not once for the whole query.
Evaluating each statement:
" Dimension.GetDirectReports is a scalar T-SQL function. "
No # It must be a table-valued function, because OUTER APPLY requires a table expression.
" The Dimension.GetDirectReports function will run only once when the query runs. " No # With APPLY, the function runs for each row of Dimension.Employee.
" The output rows will include at least one row for each row in the Dimension.Employee table. " Yes # Because of OUTER APPLY, every employee row is included, even if there are no direct reports (in that case, the columns from gdr will be NULL).
Final Answer:
Scalar function # No
Runs once per query # No
At least one row per Employee # Yes
References:
APPLY operator in T-SQL
Table-valued functions

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Explanation:

We need to enable version control for a Fabric workspace (Workspace1) and ensure that Model1 (a semantic model) is added to an Azure DevOps repository.
Key facts:
Workspace1 is in Pro mode. To enable Git integration, the workspace must be assigned to a Fabric capacity.
Git integration steps in Fabric:
Assign the workspace to a Fabric capacity.
Connect the workspace to a Git provider (e.g., Azure DevOps or GitHub).
Sync the workspace with the repository (to push artifacts like semantic models).
Branch policies and deployment pipelines are not needed for simply enabling version control.
Correct Sequence:
Assign Workspace1 to a Fabric capacity.
Connect Workspace1 to a Git provider.
Sync Workspace1 with the repository.
Final Answer:
Step 1 # Assign Workspace1 to a Fabric capacity
Step 2 # Connect Workspace1 to a Git provider
Step 3 # Sync Workspace1 with the repository
References:
Microsoft Fabric Git integration
Enable Git in Fabric workspace
This ensures Model1 will be version-controlled in the linked DevOps repository.

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.