Granting and restricting access at the workspace level
Microsoft Fabric workspaces use role-based access control to manage who can view, edit, or administer items within a workspace, separate from item-level sharing permissions. Understanding the four built-in workspace roles and how they interact with Microsoft Entra ID (formerly Azure AD) security groups is essential for implementing least-privilege access in an analytics solution.
Must-know
- Fabric workspaces support four built-in roles: Admin, Member, Contributor, and Viewer, each granting progressively fewer permissions over workspace management and item operations.
- Admins can manage workspace access, delete the workspace, and change workspace settings (including OneLake data access rules and Git integration); Members can additionally manage access for Contributor and Viewer roles but not Admins.
- Contributors can create, edit, and delete items within the workspace but cannot manage workspace-level access or settings, while Viewers have read-only access to workspace content.
- Roles can be assigned to individual users, Microsoft Entra security groups, distribution lists, or Microsoft 365 groups, and using security groups is the recommended practice for scalable governance.
- Workspace roles grant access to all items in the workspace by default, but item-level sharing can grant more granular permissions (e.g., Read or ReadWrite) to specific reports, semantic models, or lakehouses without granting full workspace access.
- Workspace access control is separate from OneLake data access roles, which allow more granular, folder-level security within a lakehouse independent of the broader workspace role assignments.
A Fabric workspace contains a lakehouse and several published Power BI reports. The data engineering lead wants business analysts to browse and run the existing reports and query the lakehouse SQL analytics endpoint for read-only analysis, but must prevent the analysts from creating, editing, or deleting any item in the workspace. Which workspace role should be assigned to the analysts?
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.