Question # 1
| 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 # 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
| You need to ensure that Contoso can use version control to meet the data analytics requirements and the general requirements. What should you do? | | A. Store all the semantic models and reports in Data Lake Gen2 storage. | | B. Modify the settings of the Research workspaces to use a GitHub repository. | | C. Store all the semantic models and reports in Microsoft OneDrive. | | D. Modify the settings of the Research division workspaces to use an Azure Repos repository. |
B. Modify the settings of the Research workspaces to use a GitHub repository.
Question # 4
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 # 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 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 # 7
You have a Fabric tenant that contains a semantic model.
You need to prevent report creators from populating visuals by using implicit measures.
What are two tools that you can use to achieve the goal? Each correct answer presents a complete solution.
NOTE: Each correct answer is worth one point.
| | A. Microsoft Power BI Desktop | | B. Tabular Editor | | C. Microsoft SQL Server Management Studio (SSMS) | | D. DAX Studio |
A. Microsoft Power BI Desktop
B. Tabular Editor
Explanation:
Microsoft Power BI Desktop (A) and Tabular Editor (B) are the tools you can use to prevent report creators from using implicit measures. In Power BI Desktop, you can define explicit measures which can be used in visuals. Tabular Editor allows for advanced model editing, where you can enforce the use of explicit measures. References = Guidance on using explicit measures and preventing implicit measures in reports can be found in the Power BI and Tabular Editor official documentation.
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