Microsoft DP-600 - Implementing Analytics Solutions Using Microsoft Fabric

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.

To identify whether throttling is occurring, you should use the Monitoring hub (B). This provides a centralized place where you can monitor and manage the health, performance, and reliability of your data estate, and see if the compute resources are being throttled. References = The use of the Monitoring hub for performance management and troubleshooting is detailed in the Azure Synapse Analytics documentation.

To prevent new tables added to Lakehouse1 from being automatically added to the default semantic model, you should configure the semantic model settings. There should be an option within the settings of the semantic model to include or exclude new tables by default. By adjusting these settings, you can control the automatic inclusion of new tables.

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.

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.

The PoC is planned to be completed using a Fabric trial capacity, which implies that users involved in the PoC should be able to try paid features. However, this should be limited to specific security groups involved in the PoC to prevent the entire organization from accessing these features before the trial is proven successful (A). The ability for users to create Fabric items should also be enabled for specific security groups to ensure that only the relevant team members participating in the PoC can create items in the Fabric environment (E).

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.