Managing schema changes with a database project
Database projects let you define a Fabric Warehouse or SQL database's schema (tables, views, procedures, etc.) as source-controlled .sql files, typically authored in Azure Data Studio or VS Code with the SQL Database Projects extension. This enables offline schema development, versioning in Git, and repeatable deployment through build/publish or CI/CD pipelines instead of making ad hoc changes directly in the Fabric portal.
Must-know
- A database project is built as a data-tier application package (similar to a .dacpac) that is then published to a target Fabric Warehouse or Fabric SQL database.
- Only a subset of the T-SQL surface area supported by Fabric Warehouse/SQL database is valid in a project, so features unsupported in Fabric (e.g., certain constraints or data types) will fail schema validation or publish.
- Schema compare functionality can detect drift between the project definition and the live database, letting you generate an incremental update script rather than a full redeploy.
- Database projects integrate with Git repositories connected to a Fabric workspace, so schema changes can be committed, reviewed via pull requests, and promoted across dev/test/prod workspaces.
- Publish/deploy actions can be automated in CI/CD pipelines (e.g., Azure DevOps or GitHub Actions) using command-line build and publish tools, enabling automated schema deployment as part of a release process.
- Database projects target Fabric Warehouse and Fabric SQL database items; they are not used to manage Lakehouse table schemas, which are Delta-based and managed differently.
A data engineer wants to manage the schema of a Fabric warehouse using version control, so that every schema change is tracked in Git and reviewed through a pull request before it reaches production. Which approach satisfies this requirement?
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.