Locking down who can open a single Fabric item
Microsoft Fabric supports item-level access control, allowing permissions to be assigned directly on individual items (such as a lakehouse, warehouse, semantic model, or report) within a workspace, rather than only through broad workspace roles. This enables granular sharing scenarios where a user needs access to a specific item without being granted access to the entire workspace or all its contents.
Must-know
- Item-level permissions are separate from workspace roles (Admin, Member, Contributor, Viewer); a user can have item access without any workspace role.
- Sharing an item (e.g., a lakehouse or report) grants the recipient access only to that item, and you can specify additional granular permissions such as Read, ReadAll, or Build depending on the item type.
- The ReadAll permission on a lakehouse or warehouse grants read access to the underlying data via SQL analytics endpoint and Spark, while Read alone may only allow viewing item metadata without data access.
- Item permissions can be managed via the Manage permissions pane in the Fabric portal, and also programmatically through Fabric REST APIs.
- Granting Build permission on a semantic model allows the recipient to create new reports from it without exposing the report author's other workspace content.
- Item-level access controls work alongside workspace roles and OneLake data access controls, so effective access is the union of workspace role permissions, item-level shares, and any OneLake-level security rules applied to the underlying data.
A data engineer manages a Fabric lakehouse containing multiple tables. The finance team must be able to query only the Invoices and Payments tables, whether they connect through the SQL analytics endpoint, a notebook, or a Power BI report, while other engineering teams keep full access to the lakehouse for transformation work. The engineer wants one security definition that is enforced consistently across all of these access paths. What should the engineer configure?
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.