Microsoft Fabric Analytics Engineer Associate DP-600
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Litware, Inc. is a manufacturing company with offices all over North America. The company’s analytics team comprises data engineers, analytics engineers, data analysts, and data scientists. For the past three years, Litware has been using a Microsoft Power BI tenant but has not yet enabled any Fabric capacities or features. They now plan to enable Fabric features in their current tenant for a proof of concept (PoC).
As part of this PoC, a Fabric trial capacity will be utilized, and three workspaces will be created: AnalyticsPOC, DataEngPOC, and DataSciPOC. The data store established in the AnalyticsPOC workspace must support several requirements: read access using T-SQL or Python, the ability to handle both semi-structured and unstructured data, and row-level security (RLS) for users executing T-SQL queries. Additionally, files that data engineers load to OneLake will be stored in the Parquet format, adhering to Delta Lake specifications.
Which type of data store should you recommend for the AnalyticsPOC workspace?
Explanation:
A lakehouse is the most suitable option for the AnalyticsPOC workspace because it supports semi-structured and unstructured data, read access by using T-SQL or Python, and row-level security (RLS) for users executing T-SQL queries. Additionally, the files will be stored in the Parquet format and will meet Delta Lake specifications, which aligns well with the lakehouse architecture. Other options such as a data lake or a warehouse do not fully meet the requirements, and an external Hive metastore is not relevant in this context.