Marking a trusted item as promoted or certified
Endorsement in Microsoft Fabric lets item owners and workspace admins signal trust and quality for items in the Fabric portal, helping consumers discover reliable content. There are two endorsement levels, Promote and Certify, each with different permission requirements and visibility implications across workspaces and the OneLake data hub.
Must-know
- Promoting an item can be done by anyone with write permissions on that item, and it signals the item is useful and ready to share, but it does not guarantee quality review.
- Certifying an item requires elevated permissions granted by an admin (only specific security groups designated by a Fabric/Power BI admin can certify), signaling the item has been reviewed and meets organizational quality standards.
- Endorsement (Promote/Certify) badges appear next to items in workspaces, the OneLake data hub, and search results, making certified/promoted items easier to discover and trust.
- Certification is configured at the tenant level in the Fabric admin portal, where an admin specifies which security groups are allowed to certify items and can also link to a certification policy document.
- Endorsement status can be removed or changed later by users with sufficient permissions, and certification badges are visually distinct (e.g., a different icon) from promoted badges to indicate the higher trust level.
- Endorsement applies across most Fabric item types (reports, datasets/semantic models, lakehouses, notebooks, pipelines, etc.), but exact support can vary by item type and is subject to ongoing feature rollout.
Check this objectiveFree · always available
A senior analyst has write permissions on a semantic model stored in a Fabric workspace. She wants to signal to her team that the model is reliable by marking it as 'Promoted'. Which additional requirement must be satisfied before she can apply this endorsement?
Your objective map0 tried · 0 right · 54 untouched
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.