Question # 1
You have a Fabric tenant that uses a Microsoft tower Bl Premium capacity. You need to enable scale-out for a semantic model. What should you do first? | A. At the semantic model level, set Large dataset storage format to Off. | B. At the tenant level, set Create and use Metrics to Enabled. | C. At the semantic model level, set Large dataset storage format to On. | D. At the tenant level, set Data Activator to Enabled. |
C. At the semantic model level, set Large dataset storage format to On.
Explanation:
To enable scale-out for a semantic model, you should first set Large dataset storage format to On (C) at the semantic model level. This configuration is necessary to handle larger datasets effectively in a scaled-out environment. References = Guidance on configuring large dataset storage formats for scale-out is available in the Power BI documentation.
Question # 2
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 # 3
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 # 4
You have a Fabric warehouse that contains a table named Sales.Orders. Sales.Orders contains the following columns.
|
Question # 5
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 # 6
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 # 7
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.
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