Question # 1
| You need to implement the date dimension in the data store. The solution must meet the technical requirements. What are two ways to achieve the goal? Each correct answer presents a complete solution. NOTE: Each correct selection is worth one point. | | A. Populate the date dimension table by using a dataflow. | | B. Populate the date dimension table by using a Stored procedure activity in a pipeline. | | C. Populate the date dimension view by using T-SQL. | | D. Populate the date dimension table by using a Copy activity in a pipeline. |
A. Populate the date dimension table by using a dataflow.
B. Populate the date dimension table by using a Stored procedure activity in a pipeline.
Question # 2
You need to implement the date dimension in the data store. The solution must meet the technical requirements.
What are two ways to achieve the goal? Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point.
| | A. Populate the date dimension table by using a dataflow. | | B. Populate the date dimension table by using a Stored procedure activity in a pipeline. | | C. Populate the date dimension view by using T-SQL. | | D. Populate the date dimension table by using a Copy activity in a pipeline. |
A. Populate the date dimension table by using a dataflow.
B. Populate the date dimension table by using a Stored procedure activity in a pipeline.
Explanation:
Both a dataflow (A) and a Stored procedure activity in a pipeline (B) are capable of creating and populating a date dimension table. A dataflow can perform the transformation needed to create the date dimension, and it aligns with the preference for using low-code tools for data ingestion when possible. A Stored procedure could be written to generate the necessary date dimension data and executed within a pipeline, which also adheres to the technical requirements for the PoC.
Question # 3
You have a Fabric tenant tha1 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:
REFRESH TABLE customer
Does this meet the goal?
| | A. Yes | | B. No |
B. No
Explanation:
No, the REFRESH TABLE statement does not provide information on whether maintenance tasks were performed. It only updates the metadata of a table to reflect any changes on the data files. References = The use and effects of the REFRESH TABLE command are explained in the Spark SQL documentation.
Question # 4
| You need to design a semantic model for the customer satisfaction report. Which data source authentication method and mode should you use? To answer, select the appropriate options in the answer area. NOTE: Each correct selection is worth one point.
|
Question # 5
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 |
B. No
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 # 6
You are the administrator of a Fabric workspace that contains a lakehouse named Lakehouse1. Lakehouse1 contains the following tables:
Table1: A Delta table created by using a shortcut
Table2: An external table created by using Spark
Table3: A managed table
You plan to connect to Lakehouse1 by using its SQL endpoint. What will you be able to do after connecting to Lakehouse1? | | A. ReadTable3. | | B. Update the data Table3. | | C. ReadTable2. | | D. Update the data in Table1. |
D. Update the data in Table1.
Question # 7
| You have a Microsoft Power Bl semantic model that contains measures. The measures use multiple calculate functions and a filter function.
You are evaluating the performance of the measures.
In which use case will replacing the filter function with the keepfilters function reduce execution time? | | A. when the filter function uses a nested calculate function | | B. when the filter function references a column from a single table that uses Import mode | | C. when the filter function references columns from multiple tables | | D. when the filter function references a measure |
A. when the filter function uses a nested calculate function
Explanation:
The KEEPFILTERS function modifies the way filters are applied in calculations done through the CALCULATE function. It can be particularly beneficial to replace the FILTER function with KEEPFILTERS when the filter context is being overridden by nested CALCULATE functions, which may remove filters that are being applied on a column. This can potentially reduce execution time because KEEPFILTERS maintains the existing filter context and allows the nested CALCULATE functions to be evaluated more efficiently.
References: This information is based on the DAX reference and performance optimization guidelines in the Microsoft Power BI documentation.
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