DP-700 Microsoft Fabric Data Engineer Associate Practice Questions

60 exam-accurate questions with explanations

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Implement and manage an analytics solution

Contoso Retail has separate dev, test, and prod Fabric workspaces for a lakehouse solution. The team wants controlled promotion of notebooks, pipelines, and lakehouse items with approvals and minimal manual copying. What should you configure?\n

A A OneLake shortcut from dev to prod so all items are shared automatically

B A Dataflow Gen2 refresh schedule in each workspace

C Workspace Viewer access for data scientists in all environments

D Deployment pipelines for the workspaces and Git integration for source-controlled items check_circle

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Explanation

Deployment pipelines promote Fabric content across stages while Git tracks source-controlled changes. A shortcut shares data paths, not application lifecycle promotion or approvals.

Implement and manage an analytics solution

City Power Utility needs engineers to collaborate on Fabric notebooks using pull requests and branches. They also need to revert a bad change. Which capability best supports this requirement?\n

A Configure Git integration for the workspace and connect it to a repository branch check_circle

B Export every notebook manually before each release

C Use sensitivity labels on notebooks

D Create a semantic model refresh schedule

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Explanation

Git integration provides branching, history, and revert workflows. Manual export is error-prone and does not provide collaborative version control.

Implement and manage an analytics solution

Tailspin Toys stores customer profiles in a Fabric lakehouse. Analysts should query only approved tables, while engineers can update notebooks and pipelines. In this case, what is the best governance pattern?\n

A Give all analysts the workspace Admin role

B Combine workspace roles with item-level permissions and granular data permissions where required check_circle

C Put all data in one folder and rely only on naming conventions

D Disable OneLake access for the workspace

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Explanation

Fabric governance is layered: workspace roles control collaboration, item permissions control access, and granular permissions restrict data. Admin access gives excessive privileges.

Implement and manage an analytics solution

Northwind Health must prevent BI developers from seeing a sensitive column in a warehouse table while allowing normal query access to the rest of the table. Which feature should be used?\n

A A workspace deployment pipeline

B A Spark session setting

C Column-level security or dynamic data masking based on the access requirement check_circle

D A OneLake shortcut to a different workspace

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Explanation

Column-level security or masking protects sensitive columns during query access. Deployment pipelines move artifacts and do not enforce column visibility.

Implement and manage an analytics solution

Northwind Health wants users to discover trusted datasets and lakehouse items in Fabric. The data owner has validated the content and wants to mark it as reliable. What should be applied?\n

A Increase the Spark driver size

B Create a pipeline parameter

C Use KQL update policy on the table

D Endorse the item as promoted or certified according to the governance process check_circle

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Explanation

Endorsement communicates that content is trusted or certified for reuse. Spark sizing and pipeline parameters do not signal trust.

Implement and manage an analytics solution

City Power Utility must track who accessed or changed Fabric items that contain sales orders. Which capability is most appropriate?\n

A Microsoft Fabric audit logs check_circle

B Dataflow Gen2 computed tables only

C Spark broadcast joins

D Warehouse result-set caching

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Explanation

Audit logs are used to review user and activity events for governance and compliance. Computed tables transform data but do not provide audit history.

Implement and manage an analytics solution

Datum Insurance needs a low-code transformation that business data owners can maintain. The source is SQL Server, and the result must land in OneLake. Which Fabric item is usually the best fit?\n

A A KQL database function only

B Dataflow Gen2 check_circle

C A deployment pipeline

D A workspace domain

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Explanation

Dataflow Gen2 is the low-code data preparation option for ingesting and transforming data into Fabric destinations. KQL functions are not the typical low-code ingestion choice.

Implement and manage an analytics solution

Northwind Health has complex Python logic for parsing support tickets, using custom libraries and several joins. The process must be parameterized and called by a pipeline. Which item should implement the transformation?\n

A A sensitivity label

B A workspace role assignment

C A Fabric notebook invoked from a pipeline with parameters check_circle

D A manual CSV upload

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Explanation

Notebooks are appropriate for custom Python or PySpark transformations and can be orchestrated with pipeline parameters. Labels and role assignments do not transform data.

Implement and manage an analytics solution

Blue Yonder Logistics is implementing governance and lifecycle management for Fabric items that process customer profiles. The architecture review rejected manual steps and over-permissive access. Which two actions should you choose? Each correct answer presents part of the solution.\n

A Give all developers Admin access in production and copy notebooks manually

B Use workspace domains only and skip item-level permissions

C Store production logic in personal workspaces and export files before release

D Connect the development workspace to Git and promote validated content with deployment pipelines check_circle

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Explanation

Git plus deployment pipelines supports controlled lifecycle management. Admin access and manual copying are common traps because they bypass governance and repeatability.

Implement and manage an analytics solution

Litware Manufacturing needs a pipeline to pass the processing date and source folder dynamically to a notebook. Which design is best?\n

A Use pipeline parameters and dynamic expressions, then pass them to the notebook activity check_circle

B Hard-code the date in the notebook

C Create separate workspaces for each date

D Use only workspace Viewer permissions

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Explanation

Pipeline parameters and dynamic expressions support reusable orchestration. Hard-coding dates creates brittle runs and is a common exam trap.

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