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You have a Microsoft Power Bl report named Report1 that uses a Fabric semantic model. Users discover that Report1 renders slowly. You open Performance analyzer and identify that a visual named Orders By Date is the slowest to render. The duration breakdown for Orders By Date is shown in the following table. What will provide the greatest reduction in the rendering duration of Report1?
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You need to refresh the Orders table of the Online Sales department. The solution must meet the semantic model requirements. What should you include in the solution?
HOTSPOT You have a Fabric tenant that contains lakehouse named Lakehousel. Lakehousel contains a Delta table with eight columns. You receive new data that contains the same eight columns and two additional columns. You create a Spark DataFrame and assign the DataFrame to a variable named df. The DataFrame contains the new data. You need to add the new data to the Delta table to meet the following requirements: * Keep all the existing rows. * Ensure that all the new data is added to the table. How should you complete the code? To answer, select the appropriate options in the answer area.
HOTSPOT 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.
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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?
You have a Fabric tenant that 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: DESCRIBE HISTORY customer Does this meet the goal?
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?

Question 9 - DP-600 discussion

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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.
Answers
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.
Answers
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.
Answers
C.
Populate the date dimension view by using T-SQL.
D.
Populate the date dimension table by using a Copy activity in a pipeline.
Answers
D.
Populate the date dimension table by using a Copy activity in a pipeline.
Suggested answer: A, B

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.

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asked 02/10/2024
Achref hellal
33 questions
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