DP-900 Describe an analytics workload on Azure Practice Question

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

• ✗

  Azure Data Studio

  Why it's wrong here

  Azure Data Studio is a modern cross-platform tool focused on querying and administering SQL Server, Azure SQL Database, and similar engines. Although its SQL Notebooks can render simple charts, it lacks the interactive visual canvas, data modeling, and one-click publishing that a business analyst needs for dashboards. It is designed for developers and DBAs writing T-SQL, not for delivering self-service visual analytics.

• ✓

  Power BI Desktop

  Why this is correct

  Power BI Desktop is the correct answer because it provides a complete self-service business intelligence workflow. You can connect to sources like Azure Synapse Analytics, transform data with Power Query, build rich interactive visuals on a drag-and-drop canvas, and create measures using DAX. You then publish the report to the Power BI service for sharing, collaboration, scheduled refresh, and security, making it perfectly suited for an analyst creating interactive visualizations.

• ✗

  SQL Server Management Studio (SSMS)

  Why it's wrong here

  SQL Server Management Studio is a Windows-based administrative tool for managing SQL Server instances, writing T-SQL queries, and performing server-level tasks. Its output is static grid results with no interactive cross-filtering, drill-down, or visual composition, and it has no mechanism to publish a shareable dashboard. This product targets database engineers and administrators, not business users who need visual analytics.

• ✗

  Azure Machine Learning Studio

  Why it's wrong here

  Azure Machine Learning Studio is a cloud platform for building, training, and deploying machine learning models using notebooks, automated ML, and a drag-and-drop Designer for ML pipelines. Though its visual interface resembles a canvas, it is used to construct predictive workflows with data preparation and model scoring, exposing endpoints and metrics rather than interactive business reports. Its intended audience is data scientists and ML engineers, not business analysts creating dashboards.