Get DP-600 Dumps : Verified Implementing Analytics Solutions Using Microsoft Fabric

• Data Engineers: Read All SQL analytics endpoint data
• Data Analysts: Read All Apache Spark
• Data Scientists: Read All SQL analytics endpoint data

The permissions for the data store in the AnalyticsPOC workspace should align with the principle of least privilege:

• Data Engineers need read and write access but not to datasets or reports.
• Data Analysts require read access specifically to the dimensional model objects and the ability to create Power BI reports.
• Data Scientists need read access via Spark notebooks. These settings ensure each role has the necessary permissions to fulfill their responsibilities without exceeding their required access level.

To meet the technical requirement that data loading activities must ensure the raw and cleansed data is updated completely before populating the dimensional model, you would need a mechanism that allows for ordered execution. A pipeline in Microsoft Fabric with dependencies set between activities can ensure that activities are executed in a specific sequence. Once set up, the pipeline can be scheduled to run at the required intervals (hourly or daily depending on the data source).

For the semantic model design required for the customer satisfaction report, the choices for data source authentication method and mode should be made based on security and performance considerations as per the case study provided.

Authentication method: The data should be accessed securely, and given that row-level security (RLS) is required for users executing T-SQL queries, you should use an authentication method that supports RLS. Service principal authentication is suitable for automated and secure access to the data, especially when the access needs to be controlled programmatically and is not tied to a specific user's credentials.

Mode: The report needs to show data as soon as it is updated in the data store, and it should only contain data from the current and previous year. DirectQuery mode allows for real-time reporting without importing data into the model, thus meeting the need for up-to-date data. It also allows for RLS to be implemented and enforced at the data source level, providing the necessary security measures.

Based on these considerations, the selections should be:

• Authentication method: Service principal authentication
• Mode: DirectQuery

A lakehouse (C) should be recommended for the AnalyticsPOC workspace. It combines the capabilities of a data warehouse with the flexibility of a data lake. A lakehouse supports semi-structured and unstructured data and allows for T-SQL and Python read access, fulfilling the technical requirements outlined for Litware. References = For further understanding, Microsoft's documentation on the lakehouse architecture provides insights into how it supports various data types and analytical operations.

• The measure should use the AVERAGE function to calculate the average value.
• It should reference the Response Value column from the 'Survey' table.
• The 'Number of months' should be used to define the period for the average calculation.

To calculate the average overall satisfaction score using DAX, you would need to use the AVERAGE function on the response values related to satisfaction questions. The DATESINPERIOD function will help in calculating the rolling average over the last 12 months.

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