You have a Fabric tenant.
You are creating a Fabric Data Factory pipeline.
You have a stored procedure that returns the number of active customers and their average sales for the current month.
You need to add an activity that will execute the stored procedure in a warehouse. The returned values must be available to the downstream activities of the pipeline.
Which type of activity should you add?
In a Fabric Data Factory pipeline, to execute a stored procedure and make the returned values available for downstream activities, the Lookup activity is used. This activity can retrieve a dataset from a data store and pass it on for further processing. Here's how you would use the Lookup activity in this context:
Add a Lookup activity to your pipeline.
Configure the Lookup activity to use the stored procedure by providing the necessary SQL statement or stored procedure name.
In the settings, specify that the activity should use the stored procedure mode.
Once the stored procedure executes, the Lookup activity will capture the results and make them available in the pipeline's memory.
Downstream activities can then reference the output of the Lookup activity.
You have a Fabric notebook that has the Python code and output shown in the following exhibit.
Which type of analytics are you performing?
The Python code and output shown in the exhibit display a histogram, which is a representation of the distribution of data. This kind of analysis is descriptive analytics, which is used to describe or summarize the features of a dataset. Descriptive analytics answers the question of 'what has happened' by providing insight into past data through tools such as mean, median, mode, standard deviation, and graphical representations like histograms.
You have an Azure Repos Git repository named Repo1 and a Fabric-enabled Microsoft Power Bl Premium capacity. The capacity contains two workspaces named Workspace! and Workspace2. Git integration is enabled at the workspace level.
You plan to use Microsoft Power Bl Desktop and Workspace! to make version-controlled changes to a semantic model stored in Repo1. The changes will be built and deployed lo Workspace2 by using Azure Pipelines.
You need to ensure that report and semantic model definitions are saved as individual text files in a folder hierarchy. The solution must minimize development and maintenance effort.
In which file format should you save the changes?
When working with Power BI Desktop and Git integration for version control, report and semantic model definitions should be saved in the PBIX format. PBIX is the Power BI Desktop file format that contains definitions for reports, data models, and queries, and it can be easily saved and tracked in a version-controlled environment. The solution should minimize development and maintenance effort, and saving in PBIX format allows for the easiest transition from development to deployment, especially when using Azure Pipelines for CI/CD (continuous integration/continuous deployment) practices.
You have a Microsoft Fabric tenant that contains a dataflow.
You are exploring a new semantic model.
From Power Query, you need to view column information as shown in the following exhibit.
Which three Data view options should you select? Each correct answer presents part of the solution. NOTE: Each correct answer is worth one point.
To view column information like the one shown in the exhibit in Power Query, you need to select the options that enable profiling and display quality and distribution details. These are: A. Enable column profile - This option turns on profiling for each column, showing statistics such as distinct and unique values. B. Show column quality details - It displays the column quality bar on top of each column showing the percentage of valid, error, and empty values. E. Show column value distribution - It enables the histogram display of value distribution for each column, which visualizes how often each value occurs.
You have a Fabric tenant that contains a Microsoft Power Bl report.
You are exploring a new semantic model.
You need to display the following column statistics:
* Count
* Average
* Null count
* Distinct count
* Standard deviation
Which Power Query function should you run?
The Table.Profile function in Power Query is used to generate column statistics such as count, average, null count, distinct count, and standard deviation. You can use this function as follows:
Invoke the Power Query Editor.
Apply the Table.Profile function to your table.
The result will be a table where each row represents a column from the original table, and each column in the result represents a different statistic such as those listed in the requirement.
