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)
You need to recommend a solution to prepare the tenant for the PoC.
Which two actions should you recommend performing from the Fabric Admin portal? Each correct answer presents part of the solution.
NOTE: Each correct answer is worth one point.
Which type of data store should you recommend in the AnalyticsPOC workspace?
You have a Fabric tenant that contains a lakehouse. You plan to use a visual query to merge two tables.
You need to ensure that the query returns all the rows that are present in both tables. Which type of join should you use?
You have a Microsoft Power Bl semantic model.
You need to identify any surrogate key columns in the model that have the Summarize By property set to a value other than to None. The solution must minimize effort.
What should you use?
You are implementing a medallion architecture in a single Fabric workspace.
You have a lakehouse that contains the Bronze and Silver layers and a warehouse that contains the Gold layer.
You create the items required to populate the layers as shown in the following table.
You need to ensure that the layers are populated daily in sequential order such that Silver is populated only after Bronze is complete, and Gold is populated only after Silver is complete. The solution must minimize development effort and complexity.
What should you use to execute each set of items? To answer, drag the appropriate options to the correct items. Each option 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 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?
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?
You have a Fabric tenant that contains a warehouse named Warehouse1. Warehouse1 contains a fact table named FactSales that has one billion rows. You run the following T-SQL statement.
CREATE TABLE test.FactSales AS CLONE OF Dbo.FactSales;
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 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?
