PL-300 Exam Dumps : Microsoft Power BI Data Analyst

Box 1: The Viewer role to the workspace

The Viewer role gives a read-only experience to its users. They can view dashboards, reports, or workbooks in the workspace, but can’t browse the datasets or dataflows. Use the Viewer role wherever you would previously use a classic workspace set to “Members can only view Power BI content”.

Box 2: Build

The analysts must be able to build new reports from the dataset that contains the profit and loss data.

Scenario: The reports must be made available to the board from powerbi.com.

The analysts responsible for each business unit must see all the data the board sees, except the profit and loss data, which must be restricted to only their business unit's data. The analysts must be able to build new reports from the dataset that contains the profit and loss data, but any reports that the analysts build must not be included in the quarterly reports for the board. The analysts must not be able to share the quarterly reports with anyone.

"The analysts responsible for each business unit must see all the data the board sees, except the profit and loss data, which must be restricted to only their business unit's data. The analysts must be able to build new reports from the dataset that contains the profit and loss data" => one dataset and two separate workspaces Reason: All data can be imported into one dataset also if these are two logical models. Shared dimensions can be reconsumed in both models. Reports and additional materials can be shared to the board with an app. The "profit and loss" data model needs RLS for the analysts and the analysts must have just read access to the original workspace. In a separate workspace with contributer (or more rights) they can create new reports (with live connection to the dataset). It is also stated that the new reports mustn't be shared so therefore no need to include them into the app. Import vs. DirectQuery: Due to RLS requirements an imported dataset is needed. It is not possible with file sources and Sharepoint lists.

Adding an index key and normalizing casing in the data source would help resolve issues with undefined values and errors in a case-sensitive column. The index key can provide a unique identifier, and normalizing casing ensures consistency in the data, which would likely prevent the errors you encountered in the Power BI semantic model.