Microsoft DP-600 Actual Free Exam Questions & Community Discussion
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.
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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
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Which type of data store should you recommend in the AnalyticsPOC workspace?
Correct Answer: C
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You have a Fabric eventhouse that contains a KQL database. The database contains a table named TaxiData that stores the following data.
You need to create a column named FirstPickupDateTime that will contain the first value of each hour from tpep_pickup_datetime partitioned by payment_type.
NOTE: Each correct selection is worth one point.
You need to create a column named FirstPickupDateTime that will contain the first value of each hour from tpep_pickup_datetime partitioned by payment_type.
NOTE: Each correct selection is worth one point.
Correct Answer:
Explanation:
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
You have a Fabric tenant that contains three users named User1, User2, and User3. The tenant contains a security group named Group 1. User1 and User3 are members of Group " 1.
The tenant contains the workspaces shown in the following table.
The tenant contains the domains shown in the following table.
User1 creates a new workspace named Workspace3.
You assign Domain! as the default domain of Group1.
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.
The tenant contains the workspaces shown in the following table.
The tenant contains the domains shown in the following table.
User1 creates a new workspace named Workspace3.
You assign Domain! as the default domain of Group1.
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:
You have a Fabric tenant.
You plan to create a Fabric notebook that will use Spark DataFrames to generate Microsoft Power Bl visuals.
You run 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 plan to create a Fabric notebook that will use Spark DataFrames to generate Microsoft Power Bl visuals.
You run 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 code embeds an existing Power BI report. - No
The code creates a Power BI report. - Yes
The code displays a summary of the DataFrame. - Yes
The code provided seems to be a snippet from a SQL query or script which is neither creating nor embedding a Power BI report directly. It appears to be setting up a DataFrame for use within a larger context, potentially for visualization in Power BI, but the code itself does not perform the creation or embedding of a report.
Instead, it ' s likely part of a data processing step that summarizes data.
References =
Introduction to DataFrames - Spark SQL
Power BI and Azure Databricks
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 lakehouse named Lakehousel. Lakehousel 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 DETAIL customer
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 lakehouse named Lakehousel. Lakehousel 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 DETAIL customer
Does this meet the goal?
Correct Answer: B
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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 two workspaces named Woritspace1 and Workspace2. Workspace1 contains a lakehouse named Lakehouse1. Workspace2 contains a lakehouse named Lakehouse2. Lakehouse!
contains a table named dbo.Sales. Lakehouse2 contains a table named dbo.Customers.
You need to ensure that you can write queries that reference both dbo.Sales and dbo.Customers in the same SQL query without making additional copies of the tables.
What should you use?
contains a table named dbo.Sales. Lakehouse2 contains a table named dbo.Customers.
You need to ensure that you can write queries that reference both dbo.Sales and dbo.Customers in the same SQL query without making additional copies of the tables.
What should you use?
Correct Answer: C
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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 the total amount of sales from the same period last year.
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.
You need to write a DAX query that will be executed by using the XMLA endpoint. The query must return the total amount of sales from the same period last year.
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.
Correct Answer:
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.
First blank: CALCULATE
Second blank: _LYSales
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