Grouping and governing workspaces with a Fabric domain
Microsoft Fabric domains let admins group workspaces by business area (e.g., Finance, Sales) to apply consistent governance, discoverability, and policy settings across those workspaces. Fabric or domain admins configure domain-level settings that cascade down to member workspaces, while workspace admins retain limited ability to override certain settings unless locked by the domain.
Must-know
- Only Fabric admins (or those granted domain admin rights) can create domains and assign workspaces to them in the Admin portal.
- Domains can have associated contributors who are permitted to move workspaces into or out of the domain, but only admins can create/delete domains themselves.
- Domain-level settings (such as default workspace capacity, sharing, and certain tenant switches) can be set to apply to all workspaces in the domain, and admins can choose to enforce them so workspace owners cannot override.
- Sub-domains can be created under a parent domain to allow more granular grouping and delegated administration within a larger business area.
- Workspaces can belong to only one domain at a time, and assigning a workspace to a domain does not change its existing permissions or content.
- Domain settings and workspace assignments are managed from the Admin portal's Domains section, and changes can take some time to propagate across the tenant.
A Fabric tenant admin creates a domain named "Finance" and assigns Priya as the domain admin. Priya wants to associate an existing workspace, "FP&A Reporting," with the Finance domain, but she does not currently have admin rights on that workspace. What must happen before Priya can complete this association from the domain settings?
What you have tried across DP-700's objectives, not a readiness score.
Implement and manage an analytics solution
- Tuning a workspace's Spark compute defaults and pool sizing
- Grouping and governing workspaces with a Fabric domain
- Setting per-workspace defaults for OneLake storage
- Standing up an Airflow job runtime inside a workspace
- Connecting a workspace to a Git repository
- Managing schema changes with a database project
- Promoting Fabric items across environments with a deployment pipeline
- Granting and restricting access at the workspace level
- Locking down who can open a single Fabric item
- Layering row, column, object, and file-level security rules
- Hiding sensitive column values behind a dynamic mask
- Classifying Fabric items with a sensitivity label
- Marking a trusted item as promoted or certified
- Reading a Fabric audit log to see who did what
- Securing data at the OneLake storage layer
- Picking the right build tool among a dataflow, a pipeline, and a notebook
- Kicking off a job on a schedule or in response to an event
- Chaining notebooks and pipelines together with parameters and dynamic expressions
Ingest and transform data
- Deciding between a full reload and an incremental load
- Shaping source data ahead of a dimensional-model load
- Landing a continuous stream of data into storage
- Matching a workload to the right Fabric data store
- Picking a transformation tool from dataflows, notebooks, KQL, or T-SQL
- Linking to external data without copying it via a OneLake shortcut
- Keeping a source database continuously replicated into Fabric
- Moving data into Fabric with a data pipeline
- Writing transform logic in PySpark, SQL, or KQL
- Flattening related tables into one wide, denormalized shape
- Rolling records up with group-by aggregations
- Dealing with duplicate rows, gaps, and data that arrives late
- Selecting the right engine for a real-time workload
- Weighing storage-in-place against a linked shortcut for a Real-Time Intelligence table
- Weighing an accelerated shortcut against a standard one for query speed
- Routing and reshaping live events with an Eventstream
- Handling a continuous flow of records with Spark's structured streaming
- Querying and reshaping event data with KQL
- Aggregating a stream over sliding or tumbling time windows
Monitor and optimize an analytics solution
- Watching an ingestion job's health and progress
- Watching a transformation job's health and progress
- Tracking whether a semantic model's refresh actually succeeded
- Setting up an alert to catch a failure early
- Tracking down why a pipeline run failed and fixing it
- Diagnosing why a dataflow run failed
- Debugging a notebook run that failed
- Troubleshooting a misbehaving Eventhouse
- Troubleshooting a misbehaving Eventstream
- Debugging a T-SQL statement that failed
- Fixing a broken or unreachable shortcut
- Speeding up a Lakehouse table with maintenance operations
- Making a slow pipeline run faster
- Tuning a Fabric warehouse for faster queries
- Improving throughput on real-time streaming components
- Tuning a Spark job to run faster and cheaper
- Making a slow query run faster
Coverage checked against the published exam guide on Aug 11, 2026.
These are independent practice questions, written against this certification's published exam guide. They are not the certification vendor's own questions, and not the real exam.