Question # 1
| What should you use to implement calculation groups for the Research division semantic models? | | A. DAX Studio | | B. Microsoft Power Bl Desktop | | C. the Power Bl service | | D. Tabular Editor |
C. the Power Bl service
Question # 2
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 # 3
| In Microsoft Fabric, which of the following is the most suitable for real-time analytics and data stream processing? | | A. Power BI | | B. Azure Stream Analytics | | C. Synapse Pipelines | | D. Databricks Notebooks |
B. Azure Stream Analytics
Question # 4
| 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 # 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 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 # 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.
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