Connecting a workspace to a Git repository
Microsoft Fabric supports Git integration so workspaces can be connected to a Git repository (Azure DevOps or GitHub) for version control of supported items. Once connected, changes made in the Fabric workspace can be committed to the repo, and changes in the repo can be synced back into the workspace, enabling collaboration, history tracking, and branching workflows. This integration is configured per workspace via Workspace settings > Git integration.
Must-know
- Git integration is configured at the workspace level under Workspace settings, where you connect to either Azure DevOps or GitHub, specify the organization/repo, branch, and folder path.
- Each Fabric item is serialized into source control as a folder containing platform-specific definition files (e.g., .platform, .pbir, .json), not as a single binary export.
- Not all Fabric item types support Git integration; unsupported item types will not sync and must be managed manually until support is added.
- Users need appropriate permissions both in Fabric (workspace Admin/Member role, depending on action) and in the Git provider (repo read/write access) to connect and sync a workspace.
- The workspace shows sync status (Git diff) indicating uncommitted changes, and you must explicitly commit workspace changes to the repo or update the workspace from the repo: sync is not fully automatic.
- Branching out to a new workspace lets teams create isolated dev/test branches connected to separate Git branches, supporting a branch-per-environment or feature-branch workflow before merging back.
A Fabric tenant administrator has already enabled the Git integration tenant switch. A data engineer who holds the Contributor role in a workspace tries to connect that workspace to an Azure Repos repository, but the Git integration option in the workspace settings cannot be selected. What is the most likely cause?
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.