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DP-600 Practice Questions

Question # 1
You have a Fabric tenant that contains a Microsoft Power Bl report named Report 1. Report1 includes a Python visual. Data displayed by the visual is grouped automatically and duplicate rows are NOT displayed. You need all rows to appear in the visual. What should you do?

A. Reference the columns in the Python code by index.
B. Modify the Sort Column By property for all columns.
C. Add a unique field to each row.
D. Modify the Summarize By property for all columns.


A. Reference the columns in the Python code by index.

Explanation:

To ensure all rows appear in the Python visual within a Power BI report, option C, adding a unique field to each row, is the correct solution. This will prevent automatic grouping by unique values and allow for all instances of data to be represented in the visual.
References = For more on Power BI Python visuals and how they handle data, please refer to the Power BI documentation.


Question # 2
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 # 3
You have a Fabric tenant that contains a semantic model. The model contains 15 tables.

You need to programmatically change each column that ends in the word Key to meet the following requirements:

• Hide the column.
• Set Nullable to False.
• Set Summarize By to None
• Set Available in MDX to False.
• Mark the column as a key column.
What should you use?
A. Microsoft Power Bl Desktop
B. Tabular Editor
C. ALM Toolkit
D. DAX Studio


B. Tabular Editor

Explanation:

Tabular Editor is an advanced tool for editing Tabular models outside of Power BI Desktop that allows you to script out changes and apply them across multiple columns or tables. To accomplish the task programmatically, you would:

Open the model in Tabular Editor.

Create an Advanced Script using C# to iterate over all tables and their respective columns.

Within the script, check if the column name ends with 'Key'.

For columns that meet the condition, set the properties accordingly: IsHidden = true, IsNullable = false, SummarizeBy = None, IsAvailableInMDX = false.

Additionally, mark the column as a key column.

Save the changes and deploy them back to the Fabric tenant.

References: The ability to batch-edit properties using scripts in Tabular Editor is well-documented in the tool's official documentation and user community resources.



Question # 4
Which of the following components is most commonly used for data ingestion in Microsoft Fabric?
A. Power BI
B. Azure Synapse Analytics
C. Data Factory
D. Databricks


C. Data Factory



Question # 5
Which type of data store should you recommend in the AnalyticsPOC workspace?
A. a data lake
B. a warehouse
C. a lakehouse
D. an external Hive metaStore


C. a lakehouse



Question # 6
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 # 7
You have a Fabric warehouse that contains a table named Sales.Orders. Sales.Orders contains the following columns.



Question # 8
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



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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: 117
  • Last Updation Date: 7-Feb-2025

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