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DP-600 Practice Questions

Question # 1
You have a Fabric tenant that contains a semantic model. The model uses Direct Lake mode.

You suspect that some DAX queries load unnecessary columns into memory.

You need to identify the frequently used columns that are loaded into memory.

What are two ways to achieve the goal? Each correct answer presents a complete solution.

NOTE: Each correct answer is worth one point.
A. Use the Analyze in Excel feature.
B. Use the Vertipaq Analyzer tool.
C. Query the $system.discovered_STORAGE_TABLE_COLUMN-iN_SEGMeNTS dynamic management view (DMV).
D. Query the discover_hehory6Rant dynamic management view (DMV).


B. Use the Vertipaq Analyzer tool.
C. Query the $system.discovered_STORAGE_TABLE_COLUMN-iN_SEGMeNTS dynamic management view (DMV).

Explanation:

The Vertipaq Analyzer tool (B) and querying the $system.discovered_STORAGE_TABLE_COLUMNS_IN_SEGMENTS dynamic management view (DMV) (C) can help identify which columns are frequently loaded into memory. Both methods provide insights into the storage and retrieval aspects of the semantic model. References = The Power BI documentation on Vertipaq Analyzer and DMV queries offers detailed guidance on how to use these tools for performance analysis.


Question # 2
You have a Fabric warehouse that contains a table named Sales.Orders. Sales.Orders contains the following columns.



Question # 3
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 # 4
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.


Question # 5
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 # 6
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?
A. an Azure Data Factory pipeline that executes a dataflow to retrieve the minimum value of the OrderlD column in the destination lakehouse
B. an Azure Data Factory pipeline that executes a Stored procedure activity to retrieve the maximum value of the OrderlD column in the destination lakehouse
C. an Azure Data Factory pipeline that executes a dataflow to retrieve the maximum value of the OrderlD column in the destination lakehouse
D. an Azure Data Factory pipeline that executes a Stored procedure activity to retrieve the minimum value of the OrderiD column m the destination lakehouse


C. an Azure Data Factory pipeline that executes a dataflow to retrieve the maximum value of the OrderlD column in the destination lakehouse



Question # 7
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 # 8
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.


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Microsoft DP-600 Exam Dumps

Exam Name: Implementing Analytics Solutions Using Microsoft Fabric
Certification Name: Microsoft Certified:Fabric Analytics Engineer Associate

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  • Total Questions: 111
  • Last Updation Date: 16-Jan-2025

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