Classifying Fabric items with a sensitivity label
Sensitivity labels in Microsoft Fabric let you classify and protect data items (like lakehouses, warehouses, semantic models, and reports) with the same labels used across Microsoft 365, driven by Microsoft Purview Information Protection. Labels can be applied manually by users or automatically via admin policies, and they help enforce data protection and compliance requirements as data flows through the analytics lifecycle.
Must-know
- Sensitivity labels must first be created and published in the Microsoft Purview compliance portal before they are available in Fabric.
- In Fabric, the tenant admin must enable the 'Information protection for Power BI content' (or equivalent Fabric) setting in the admin portal for labels to appear.
- Labels can be applied manually by item owners/editors from the workspace or item settings, or applied automatically through default labeling and auto-labeling policies.
- When a label is applied to an upstream item (e.g., a lakehouse or dataset), it can automatically flow downstream to dependent items like reports and dashboards built on that data, depending on inheritance settings.
- Removing or downgrading a sensitivity label to a less restrictive one may require justification text or additional permissions, depending on label policy configuration.
- Sensitivity labels applied in Fabric can also enforce protection when data is exported (e.g., to Excel or PDF), encrypting the file according to the label's protection settings if configured.
A data engineer applies the sensitivity label "Highly Confidential" to a lakehouse in Microsoft Fabric. Later, a semantic model is created directly from that lakehouse, and a Power BI report is built directly on that semantic model. No one manually changes any labels along the way. What should the data engineer expect regarding the sensitivity label on the final report?
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.