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Fabric DP-700: Implement and manage an analytics solution

Fabric DP-700 · Work through your exam, one topic at a time.

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Implement and manage an analytics solution

What this domain tests

You must choose the right Fabric item, wire it into a workspace and capacity, and lock it down. Know the difference between a lakehouse (Spark + Delta files, with a read-only SQL analytics endpoint), a warehouse (full T-SQL DDL/DML, no Spark), an eventhouse/KQL database (streaming, time-series), and a semantic model on top of any of them. OneLake is the single logical lake; shortcuts virtualize data (internal, ADLS Gen2, S3/GCS, Dataverse) without copying it.

Security, in layers

  • Workspace roles: Admin, Member, Contributor, Viewer. Contributors can create and publish items but cannot manage workspace permissions.
  • Item permissions and sharing: granting access to a single lakehouse or warehouse.
  • Granular data security (T-SQL): row-level security (RLS) via CREATE SECURITY POLICY, column-level security with GRANT/DENY, and dynamic data masking on warehouse columns.
  • Governance: sensitivity labels (Purview), endorsements (Promoted vs Certified), domains for data-mesh organization, and lineage view.

Lifecycle

Deployment pipelines (dev/test/prod with deployment rules and variable libraries) and Git integration move items between stages.

Traps

  1. Assuming RLS defined on a lakehouse's SQL analytics endpoint protects Spark readers - it does not; Spark reads the Delta files directly. Enforce RLS in the semantic model as well if needed.
  2. Confusing workspace role with item permission: a Viewer of the workspace may still be blocked from specific items.
  3. Thinking a lakehouse SQL endpoint allows UPDATE/DELETE - it is read-only.
  4. Forgetting that mirroring produces a read-only replica.

Rule of thumb

"Need T-SQL writes (INSERT/UPDATE/DELETE, stored procedures)? Warehouse. Need Spark, notebooks, and file-level control? Lakehouse. Need sub-second streaming queries? Eventhouse."