You have a table that contains sales data and approximately 1,000 rows.
You need to identify outliers in the table. Which type of visualization should you use?

You are building a Power Bl report that uses data from an Azure SQL database named erp1.
You Import the following tables.

You need to perform the following analyses:
* Orders sold over time that include a measure of the total order value
* Orders by attributes of products sold
The solution must minimize update times when interacting with visuals in the report. What should you do first?

You need to create the On-Time Shipping report.
The report must include a visualization that shows the percentage of late orders. Which type of visualization should you create?

Note: This question is part of a series of questions that use the same scenario. For your convenience, the scenario is repeated in each question. Each question presents a different goal and answer choices, but the text of the scenario is the same in each question in this series.
You have a Microsoft SQL Server database that contains the following tables.

The following columns contain date information:
- Date[Month] in the mmyyyy format
- Date[Date_ID] in the ddmmyyyy format
- Date[Date_name] in the mm/dd/yyyy format
- Monthly_returns[Month_ID] in the mmyyyy format
The Order table contains more than one million rows.
The Store table has a relationship to the Monthly_returns table on the Store_ID column. This is the only relationship between the tables.
You plan to use Power BI Desktop to create an analytics solution for the data.
You need to create a relationship between the Order table and the Store table on the Store_ID column.
What should you do before you create the relationship?

You have a Power Bl query named Sates that imports the columns shown in the following table.

Usees only use the dale part of the Sales.Date field. Only rows with a Status of Finished are used in analysis.
You need to reduce the load times of the query without affecting the analysis.
Which two actions achieve this goal? Each correct answer presents a complete solution.
NOTL Each correct selection is worth one pant.