Microsoft DP-700 Practice Test 4

Question 30

After reassigning a workspace to a Fabric-enabled capacity, which observation most directly verifies that the capacity change addressed its earlier Fabric item limitation?

Verify that an old Power BI report has the same title.
Review the effective workspace type/capacity and confirm the supported Fabric lakehouse creation experience is available to an authorized user.
Count the number of sensitivity labels defined across the tenant.
Check that a scheduled Airflow DAG was edited last week.

Explanation

Correct Answer

This checks both configuration and practical feature availability in the workspace, rather than inferring success from an unrelated workload.

Incorrect Answers

Report titles are unrelated to which Fabric capacity currently hosts the workspace.

Label configuration is an independent governance feature and cannot prove workspace capacity assignment.

Historical DAG edit timestamps do not establish whether the workspace now supports lakehouse creation.

Question 31

Two production Spark jobs share a workspace. One must retain Python package v1 for compatibility while a new transformation requires v2; both are scheduled nightly. What setup avoids affecting the established job?

Publish distinct Fabric environments with their appropriate package versions and explicitly attach the corresponding environment to each job.
Update the workspace default environment to v2 and let both jobs inherit it.
Place both jobs in a single Dataflow Gen2 without any package configuration.
Use the same environment but rename one job to mention v1.

Explanation

Correct Answer

Per-job environment attachment keeps dependency sets isolated. Updating the workspace-wide default could unexpectedly alter another job using Workspace default.

Incorrect Answers

Changing a shared default would make the v1-dependent job inherit an incompatible package version.

A Dataflow changes the execution engine and does not preserve independent scheduled PySpark package dependencies.

Item names do not select dependency versions; both jobs would still load the identical published environment.

Question 32

A legacy notebook fails immediately after the workspace default Fabric environment is upgraded. Its owner never explicitly attached an environment; the notebook still uses Workspace default. Why did the change affect that notebook?

The notebook was automatically assigned to a new OneLake domain when the environment was published.
Because the notebook inherits the workspace environment default, changing the published default can change its runtime and libraries.
A default lakehouse mount forced the Python interpreter to downgrade.
A SQL warehouse query planner always changes the Spark runtime when a notebook reads a table.

Explanation

Correct Answer

Notebooks that use Workspace default intentionally follow the workspace-level environment selection; isolation requires attaching a separate environment to the legacy notebook.

Incorrect Answers

Environment publication does not create automatic domain assignments that change package versions.

Lakehouse mounts select storage paths, not the published package/runtime version delivered by Workspace default.

Warehouse query optimization is independent of Spark environment inheritance.

Question 33

After separating a legacy Spark job and a modern Spark job into two published Fabric environments, which verification most strongly shows the shared workspace default is no longer a source of version regression?

Confirm that both jobs produce the same number of output rows.
Confirm both jobs are listed under the same workspace domain.
Start fresh sessions for both jobs and confirm each attaches its assigned environment and resolves the expected distinct package versions.
Verify that both notebooks reference the same OneLake shortcut.

Explanation

Correct Answer

Inspecting both runtime attachments and resolved versions verifies isolation at actual execution time, where package conflicts would otherwise surface.

Incorrect Answers

Identical counts could occur with different incompatible library versions and do not show which environment each job used.

Governance grouping does not establish per-job dependency isolation.

Sharing a data source is independent of which Spark environment and package versions a job loads.

Question 34

A development team must reproduce a Fabric environment’s Spark runtime, compute properties, and managed libraries in another workspace from reviewed source-control changes. Which TWO steps are necessary in the documented Git-based environment workflow?

Integrate the source Fabric workspace with a supported Git repository so the environment’s library and compute definitions are version controlled.
Copy only a notebook result table into the destination workspace.
Synchronize the approved definitions to the destination environment and apply/publish them, resolving any workspace-specific custom-pool references.
Assume custom Spark pool IDs copied from the source workspace automatically resolve in every other workspace.
Change only the Fabric domain label of the destination workspace.

Explanation

Correct Answers

Fabric Git integration serializes supported environment library and Spark compute settings into repository files that can be reviewed and synchronized.

Synced definitions must become effective in the destination environment; workspace-scoped custom pool identifiers may require remapping before the published configuration works.

Incorrect Answers

A data table does not contain the environment runtime, compute, and managed library configuration that is required here.

Custom pool identifiers are scoped to their originating workspace; unchanged cross-workspace IDs can fail and need remapping or removal.

Domain governance labels do not serialize, synchronize or publish environment Spark configuration.

Question 35

A Fabric environment is restored from Git into a different workspace. The synced YAML retains the original custom Spark pool identifier, but no matching pool exists in the destination. The session cannot use the intended compute. What caused this?

Git integration cannot track any runtime or Spark compute properties.
The destination environment must use a lakehouse with the same name as the Git branch.
Custom pools are global Fabric tenant objects and therefore the source ID could not be the issue.
A custom pool ID from the source workspace was reused even though pool definitions are workspace-scoped.

Explanation

Correct Answer

Environment Git settings can preserve a source instance_pool_id. When synchronized elsewhere, it must refer to a destination custom pool or be removed to use the default starter pool.

Incorrect Answers

Microsoft documents environment Git representation of runtime and Spark compute settings; the specific problem is a nonportable pool reference.

A lakehouse display name is not a required mapping for a Spark custom pool identifier.

Pool definitions are workspace-scoped, so an ID from the source workspace is not automatically a valid destination pool.

Question 36

A team synchronizes a reviewed Fabric environment commit into a destination workspace and adjusts its pool reference. Which evidence confirms that the deployed environment configuration is really operational?

Check the destination’s published environment runtime/library settings and start a Spark session that resolves its supported pool and packages.
Verify only that the Git commit hash exists in the source repository.
Check the Fabric domain icon attached to the destination workspace.
Confirm that a warehouse query produces the same row count before and after Git synchronization.

Explanation

Correct Answer

The actual published destination definition and a successful session validate the effective runtime and pool mapping; a Git sync alone might leave changes staged or an unusable pool reference.

Incorrect Answers

Version history proves that code was committed, not that its environment settings were applied and operable in the destination workspace.

Domain membership is unrelated to whether the environment definition was published and its custom pool ID is valid.

A warehouse row count cannot verify the Spark runtime, library inventory, or environment compute mapping in the destination.

Question 37

A data engineering team edits Fabric notebooks in a development workspace and wants peer-reviewed version history before changes reach production. Which capability should the team configure?

Export only completed notebook cell outputs to CSV.
Set the workspace capacity to the largest available SKU.
Apply a sensitivity label to the default lakehouse.
Connect the workspace to a supported Git repository and use reviewed branches and commits for notebook definitions.

Explanation

Correct Answer

Fabric Git integration tracks supported workspace item definitions, including notebooks. Branch-based review creates an auditable change path before a release workflow promotes the approved version.

Incorrect Answers

CSV results preserve data from runs, not the notebook definition and change history needed for peer-reviewed source control.

Compute capacity affects available resources, not code review or version history for notebook definitions.

Sensitivity labels classify and protect content but do not create versioned notebook change records or branch review.

Question 38

A team frequently changes Fabric notebook definitions directly in its workspace but has no Git connection. After a bad edit, developers cannot identify a reviewed prior version. What is the primary lifecycle gap?

The team forgot to enable OneLake V-Order on a Delta table.
The Fabric domain has no dedicated Eventhouse.
The notebooks have too few Spark executors to remember prior edits.
Notebook definitions were not under the supported workspace Git integration and versioned review workflow.

Explanation

Correct Answer

Without the Git-backed definition history, the team lacks the branch/commit lineage needed to compare and reliably restore a previously reviewed notebook definition.

Incorrect Answers

V-Order optimizes storage layout; it does not track notebook source-code versions or approvals.

Domain data organization and Eventhouse ingestion do not substitute for notebook source-control history.

Compute sizing is unrelated to revision history of notebook code.

Question 39

A team commits a Fabric notebook change to a feature branch and later merges it after review. Which check best confirms the intended item definition was propagated through source control rather than only edited locally?

Check whether the notebook returned the same number of rows as yesterday.
Inspect the committed notebook definition in the target branch and confirm the connected workspace reports it synchronized to the intended commit.
Count the members in the workspace domain.
Inspect the SQL warehouse statistics age.

Explanation

Correct Answer

A reviewed commit plus matching workspace synchronization establishes the version of the notebook definition applied to the workspace; local unsynced edits would not pass both checks.

Incorrect Answers

Identical outputs do not prove source-code identity or that a specific approved commit was synchronized.

Domain membership says nothing about notebook source control synchronization or code version.

Query statistics influence planning and cannot establish a notebook Git revision.

Question 40

A Fabric data warehouse team wants CREATE TABLE and VIEW definitions to be reviewed as individual SQL files and deployed incrementally from source control. Which approach fits the supported warehouse lifecycle?

Use Eventstream to copy warehouse query logs into a lakehouse.
Create a Power BI dashboard for each SQL object.
Connect the warehouse workspace to Git so Fabric represents the warehouse schema as a database project containing SQL object definitions.
Manually copy all table data into a new workspace after every definition edit.

Explanation

Correct Answer

Fabric warehouse Git integration serializes schema into a database project with per-object .sql files; synchronized updates use schema-difference deployment rather than merely transferring query results.

Incorrect Answers

Eventstream telemetry is not a database schema project and does not version table/view definitions.

Reports are consumer artifacts, not authoritative source-controlled SQL schema objects.

A physical data copy is not the SQL database project workflow for source-controlled schema changes.

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Pass Your Microsoft DP-700 Exam Easy!

Question 30

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Question 31

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Question 32

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Question 33

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Question 34

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Question 35

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Question 36

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Question 37

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Question 38

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Question 39

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Question 40

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Implementing Data Engineering Solutions Using Microsoft Fabric (DP-700) Practice Tests