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Answer: Utilize SQL Server Data Tools (SSDT) to create an Analysis Services project, design the semantic model with scalability and reusability in mind, and deploy it to an Analysis Services server for shared access across the organization.
Option C is the best approach because it leverages SQL Server Data Tools (SSDT) for designing a scalable and reusable semantic model, which is then deployed to an Analysis Services server. This method supports high scalability, complies with data governance through centralized management, and minimizes costs by utilizing existing infrastructure. It also allows for the model to be shared across different reports and tools within the organization, ensuring adaptability for various analytical scenarios.
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As a Microsoft Fabric Analytics Engineer Associate, you are tasked with creating a shared semantic model that is reusable and adaptable for various analytical scenarios within your organization. The model must support high scalability, comply with internal data governance policies, and minimize costs. Which of the following approaches BEST meets these requirements? Choose one option.
A
Develop the semantic model in Power BI Desktop, focusing on creating a comprehensive data model, and then share the .pbix file with stakeholders for their use in Power BI Desktop.
B
Design the semantic model in Power BI Desktop, publish it to a shared workspace in Power BI Service, and configure row-level security to ensure compliance with data governance policies.
C
Utilize SQL Server Data Tools (SSDT) to create an Analysis Services project, design the semantic model with scalability and reusability in mind, and deploy it to an Analysis Services server for shared access across the organization.
D
Create a new server in Azure Analysis Services, design the semantic model to be cost-effective and scalable, and deploy it to the server for organization-wide access.