- (Topic 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:
EXPLAIN TABLE customer Does this meet the goal?
Correct Answer:
B
No, the EXPLAIN TABLE statement does not identify whether maintenance tasks were performed on a table. It shows the execution plan for a query. References = The usage and output of the EXPLAIN command can be found in the Spark SQL documentation.
- (Topic 2)
You have a Fabric workspace that contains a DirectQuery semantic model. The model queries a data source that has 500 million rows.
You have a Microsoft Power Bl report named Report1 that uses the model. Report! contains visuals on multiple pages.
You need to reduce the query execution time for the visuals on all the pages.
What are two features that you can use? Each correct answer presents a complete solution.
NOTE: Each correct answer is worth one point.
Correct Answer:
AB
User-defined aggregations (A) and query caching (C) are two features that can help reduce query execution time. User-defined aggregations allow precalculation of large datasets, and query caching stores the results of queries temporarily to speed up future queries. References = Microsoft Power BI documentation on performance optimization offers in-depth knowledge on these features.
- (Topic 2)
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?
Correct Answer:
C
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.
DRAG DROP - (Topic 2)
You are creating a dataflow in Fabric to ingest data from an Azure SQL database by using a T-SQL statement.
You need to ensure that any foldable Power Query transformation steps are processed by the Microsoft SQL Server engine.
How should you complete the code? To answer, drag the appropriate values to the correct targets. Each value may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content.
NOTE: Each correct selection is worth one point.
Solution:
You should complete the code as follows:
✑ Table
✑ NativeQuery
✑ EnableFolding
In Power Query, using Table before the SQL statement ensures that the result of the SQL query is treated as a table. NativeQuery allows a native database query to be passed through from Power Query to the source database. The EnableFolding option ensures that any subsequent transformations that can be folded will be sent back and executed at the source database (Microsoft SQL Server engine in this case).
Does this meet the goal?
Correct Answer:
A
- (Topic 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?
Correct Answer:
B
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.