Implementing Analytics Solutions Using Microsoft Fabric Practice Tests and PDFs

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. AVERAGE fTuon cctailocnu loante t hthee r easvpeornasgee voavleureasl rl eslaattiesdfa tcot isoant issfcaocrteio uns qinuge sDtAioXn,s y. oTuhe w DoAuTldES nINeePdE RtoIO uDs efu tnhcet ion will help in calculating the rolling average over the last 12 months.

You should use CREATE VIEW to make the pricing group logic available for T-SQL queries. The CASE statement should be used to determine the pricing group based on the list price. The T-SQL statement should create a view that classifies products into pricing groups based on the list price. The CASE statement is the correct conditional logic to assign each product to the appropriate pricing group. This view will standardize the pricing group logic across different databases and semantic models.

For ingesting customer data into the data store in the AnalyticsPOC workspace, a dataflow (D) should be recommended. Dataflows are designed within the Power BI service to ingest, cleanse, transform, and load data into the Power BI environment. They allow for the low-code ingestion and transformation of data as needed by Litware's technical requirements. Reference = You can learn more about dataflows and their use in Power BI environments in Microsoft's Power BI documentation.

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. Reference = For further understanding, Microsoft's documentation on the lakehouse architecture provides insights into how it supports various data types and analytical operations.

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 res