How does Fabric categorize user experiences for data work, and what roles correspond to Data Engineer, Data Scientist, and Data Analyst?

• A. Fabric provides dedicated workspaces: Data Engineering (ETL/ELT and pipelines), Data Science (notebooks and experiments), and Data Analytics (Power BI and explorations). Data Engineer builds pipelines and data models; Data Scientist develops ML models; Data Analyst consumes data via BI dashboards
• B. Fabric uses Data Governance, Data Science, and Data Visualization workspaces
• C. Fabric uses Data Storage, Data Science, and Data Visualization
• D. Fabric uses Data Processing, Data Science, and Data Visualization

Answer: (A)

Fabric organizes data work into three dedicated workspaces: Data Engineering for building ETL/ELT pipelines and data models; Data Science for notebooks and experiments used to develop ML models; and Data Analytics for consuming data through Power BI dashboards and exploratory analysis. In this setup, the Data Engineer focuses on constructing pipelines and data models that prepare data for analysis. The Data Scientist concentrates on developing and validating machine learning models using notebooks and experiments. The Data Analyst uses BI dashboards and explorations to derive insights from the data. The other options describe groupings like governance, storage, or processing that don’t match how Fabric structures these experiences and roles, so they don’t fit as well.