Microsoft DP-600 - Implementing Analytics Solutions Using Microsoft Fabric

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, fulfill ing 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.

To meet the technical requireme nt 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).

Both a dataflo w (A) and a Stored procedure activity in a pipeline (B) are capable of creating and populating a date dimension table. A dataflow can perform the transformation needed to create the date dimension, and it aligns with the preference for using low-code tools for data ingestion when possible. A Stored procedure could be written to generate the necessary date dimension data and executed within a pipeline, which also adheres to the technical requirements for the PoC.

For ingesting customer data into the data store in the AnalyticsPOC workspace, a dataflow (D) should be recommended. Dataflows are designed within the Power B I 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. References = You can learn more about dataflows and their use in Power BI environments in Microsoft ' s Power BI documentation.

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 appropriat e pricing group. This view will standardize the pricing group logic across different databases and semantic models.