Microsoft Updated DP-600 Exam Questions and Answers by kamil

The two languages you can use to perform model scoring in a Fabric notebook using the predict function are Spark SQL (option C) and PySpark (option D). These are both part of the Apache Spark ecosystem and are supported for machine learning tasks in a Fabric environment. References = You can find more information about model scoring and supported languages in the context of Fabric notebooks in the official documentation on Azure Synapse Analytics.

To identify surrogate key columns with the "Summarize By" property set to a value other than "None," the Best Practice Analyzer in Tabular Editor is the most efficient tool. The Best Practice Analyzer can analyze the entire model and provide a report on all columns that do not meet a specified best practice, such as having the "Summarize By" property set correctly for surrogate key columns. Here's how you would proceed:

Open your Power BI model in Tabular Editor.

Go to the Advanced Scripting window.

Write or use an existing script that checks the "Summarize By" property of each column.

Execute the script to get a report on the surrogate key columns that do not have their "Summarize By" property set to "None".

You can then review and adjust the properties of the columns directly within the Tabular Editor.

References: The functionality of the Best Practice Analyzer in Tabular Editor is documented in the community and learning resources for Power BI.

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).