10 beginner questions on the DP-700 skills measured: workspace roles, deployment pipelines and Git integration (Implement and manage an analytics solution); OneLake shortcuts, Delta tables, Eventstream and notebooks (Ingest and transform data); and Activator alerts and Delta table maintenance (Monitor and optimize an analytics solution).

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  1. A business analyst needs to view items and query lakehouse data through the SQL analytics endpoint in a Fabric workspace, but must not create, edit or delete any items. Which workspace role follows least privilege?

    • A. Admin
    • B. Member
    • C. Contributor
    • D. Viewer ✔ correct

    Answer: D. Viewer can view content and connect to the SQL analytics endpoint, but cannot write, delete or run items. Contributor can create and modify items, Member can also add people, and Admin can manage the workspace.

    Source: learn.microsoft.com/en-us/fabric/fundamentals/roles-workspaces · Domain: Implement and manage an analytics solution

  2. A data engineering team wants to promote Fabric items from a development workspace to test and then to production in a controlled way. Which Fabric feature is designed for this?

    • A. OneLake shortcuts
    • B. Deployment pipelines ✔ correct
    • C. Mirroring
    • D. Eventstream

    Answer: B. Deployment pipelines move content through stages such as development, test and production, and let you compare stages before deploying. Shortcuts and mirroring bring data in; they do not promote items.

    Source: learn.microsoft.com/en-us/fabric/cicd/deployment-pipelines/intro-to-deployment-p · Domain: Implement and manage an analytics solution

  3. Fabric Git integration lets you connect a workspace to a Git repository in Azure DevOps or GitHub for version control of supported items.

    • A. True ✔ correct
    • B. False

    Answer: A. True. Git integration connects a workspace to a branch in Azure DevOps or GitHub, so you can commit changes, update from the repository and track the history of supported items.

    Source: learn.microsoft.com/en-us/fabric/cicd/git-integration/intro-to-git-integration · Domain: Implement and manage an analytics solution

  4. A company keeps files in Azure Data Lake Storage Gen2 and wants a Fabric lakehouse to use them without copying the data into OneLake. What should the data engineer create?

    • A. A OneLake shortcut ✔ correct
    • B. A pipeline Copy activity
    • C. A Dataflow Gen2 that loads every file
    • D. A new warehouse with the same tables

    Answer: A. A OneLake shortcut is a pointer, like a symbolic link, to data in another location such as ADLS Gen2 or Amazon S3, so it can be used without making a copy. A Copy activity or Dataflow Gen2 would duplicate the data.

    Source: learn.microsoft.com/en-us/fabric/onelake/onelake-shortcuts · Domain: Ingest and transform data

  5. Which open table format does a Fabric lakehouse use for its managed tables, giving ACID transactions and time travel?

    • A. CSV files with a header row
    • B. Apache Avro
    • C. Delta Lake ✔ correct
    • D. JSON documents

    Answer: C. Lakehouse tables are Delta Lake tables (Parquet files plus a transaction log), which provide ACID transactions, schema enforcement and time travel. Only Delta tables appear in the SQL analytics endpoint.

    Source: learn.microsoft.com/en-us/fabric/data-engineering/lakehouse-overview · Domain: Ingest and transform data

  6. An organization wants to capture a continuous stream of IoT events, apply simple no-code transformations and route the results to an eventhouse in Real-Time Intelligence. Which Fabric item should it use?

    • A. A Dataflow Gen2 scheduled every hour
    • B. An Eventstream ✔ correct
    • C. A deployment pipeline
    • D. A paginated report

    Answer: B. Eventstream ingests real-time events, transforms them with a no-code editor and routes them to destinations such as an eventhouse or lakehouse. An hourly dataflow is batch, not streaming.

    Source: learn.microsoft.com/en-us/fabric/real-time-intelligence/event-streams/overview · Domain: Ingest and transform data

  7. A data engineer wants to transform lakehouse data in a Fabric notebook. Which two languages can the engineer use in notebook cells? (Select TWO)

    • A. PySpark (Python) ✔ correct
    • B. Spark SQL ✔ correct
    • C. Power Fx
    • D. VBA
    • E. MDX

    Answer: A, B. Fabric notebooks run Apache Spark code in Python (PySpark), Scala, Spark SQL and R. Power Fx is the Power Apps formula language, VBA is for Office macros and MDX queries multidimensional models.

    Source: learn.microsoft.com/en-us/fabric/data-engineering/lakehouse-overview · Domain: Ingest and transform data

  8. A data engineer wants Fabric to send a Microsoft Teams message automatically whenever the temperature in a streaming IoT feed rises above a threshold, without writing code. Which Fabric item should the engineer configure?

    • A. A OneLake shortcut
    • B. A deployment pipeline
    • C. Activator ✔ correct
    • D. The SQL analytics endpoint

    Answer: C. Activator is the no-code detection engine in Fabric: it watches data such as eventstreams, evaluates rules like a threshold, and triggers actions such as Teams messages, emails or pipeline runs. Shortcuts, deployment pipelines and the SQL analytics endpoint do not raise alerts.

    Source: learn.microsoft.com/en-us/fabric/real-time-intelligence/data-activator/activator · Domain: Monitor and optimize an analytics solution

  9. Queries on a lakehouse Delta table have become slow because frequent small loads created many small Parquet files. Which maintenance operation should the engineer run first?

    • A. OPTIMIZE, to compact small files into larger ones ✔ correct
    • B. VACUUM, to remove unreferenced old files
    • C. DROP TABLE, then reload all history
    • D. DESCRIBE HISTORY, to list recent changes

    Answer: A. OPTIMIZE (bin-compaction, optionally with V-Order) merges small files into larger ones for more efficient reads. VACUUM deletes files no longer referenced by the Delta log; it does not compact files.

    Source: learn.microsoft.com/en-us/fabric/data-engineering/lakehouse-table-maintenance · Domain: Monitor and optimize an analytics solution

  10. Running VACUUM on a Delta table compacts small Parquet files into larger files to speed up queries.

    • A. True
    • B. False ✔ correct

    Answer: B. False. VACUUM removes files that the Delta log no longer references and that are older than the retention threshold (seven days by default). Compaction is done by OPTIMIZE.

    Source: learn.microsoft.com/en-us/fabric/data-engineering/lakehouse-table-maintenance · Domain: Monitor and optimize an analytics solution

Original practice questions written by xOps School (DevOpsSchool Private Limited) from the public Microsoft exam objectives (DP-700 skills measured as of 2026-10-19), checked 2026-10-25. They are not actual exam questions. xOps School is not affiliated with or endorsed by Microsoft; exam names are trademarks of their owners. Official exam page: learn.microsoft.com/en-us/credentials/certifications/fabric-data-engin.

Last modified: Sunday, 25 October 2026, 9:57 PM