Microsoft DP-700 Practice Test 1

Question 1

A Fabric workspace has published environment Shared-ETL with an approved Spark runtime and libraries. A notebook still uses Workspace default, which currently points to workspace Spark settings rather than Shared-ETL. What should the administrator change so notebooks using Workspace default inherit Shared-ETL?

Pin the target lakehouse as the notebook default.
Create a OneLake shortcut to the environment item.
Grant the notebook viewer permission to Shared-ETL without changing its environment selection.
Enable the environment-backed workspace default, select published Shared-ETL, and save the Spark environment setting.

Explanation

Correct Answer

The Set default environment control determines whether Workspace default resolves to a named environment; selecting Shared-ETL supplies its published runtime and library configuration to jobs using Workspace default.

Incorrect Answers

Pinning a lakehouse determines the notebook data mount, not the Spark environment or its runtime and installed libraries.

OneLake shortcuts resolve data paths; they do not change which Spark environment is assigned to a notebook session.

Read permission permits an environment to be attached, but it does not automatically replace the effective Workspace default selection.

Question 2

A notebook is configured to use Workspace default. Its execution uses the workspace Spark configuration even though an environment named Shared-ETL was published yesterday. The administrator has never enabled Set default environment. What explains the behavior?

The notebook must have a default lakehouse before it can inherit any Spark setting.
Publishing an environment instantly rewrites every notebook to attach it explicitly.
Workspace default uses workspace-level Spark settings while the environment-backed default toggle remains off.
All environment defaults are chosen exclusively through Git repository settings.

Explanation

Correct Answer

Publishing Shared-ETL does not automatically select it; the workspace default remains a separate Spark-settings path until an administrator turns on the environment-backed default and chooses an item.

Incorrect Answers

A default lakehouse is a storage reference and is not a prerequisite for choosing Spark runtime configuration.

Publishing activates the environment configuration itself, but does not silently update notebooks or the workspace default binding.

Git can version environment definitions; the effective workspace default is configured through workspace Spark settings, not a repository-level switch.

Question 3

An administrator has just selected a published environment as the Fabric workspace default. An operations reviewer wants evidence that a new notebook session actually inherits the environment rather than legacy workspace Spark settings. Which check most directly confirms this?

Count how many shortcuts exist in the notebook default lakehouse.
Open a new notebook session using Workspace default and inspect its effective environment, runtime, and installed library versions.
Check whether a workspace domain label is visible in OneLake catalog.
Review the row count in an unrelated warehouse table.

Explanation

Correct Answer

The session-level settings and resolved libraries demonstrate the configuration that the Spark engine actually started with; a workspace selection alone does not prove a session inherited it.

Incorrect Answers

The number of storage shortcuts is unrelated to Spark runtime inheritance, which is controlled by environment selection.

Domain classification governs discovery and ownership, not the effective runtime attached to a notebook session.

Warehouse data contents cannot establish which Spark environment a notebook session loaded.

Question 4

Five scheduled PySpark notebooks require the same tested versions of pandas and a private internal wheel. The team wants configuration that is reusable and controlled without editing each notebook to install packages at runtime. What should it do?

Install packages interactively in the first notebook and rely on Spark to propagate them to other notebooks.
Create an Eventhouse database with one table per Python package.
Add the packages to one Fabric environment, publish its library configuration, and attach that environment to the notebooks.
Change the default lakehouse name to match the package folder.

Explanation

Correct Answer

A Fabric environment carries public and custom libraries as governed dependencies; publishing it and attaching it to each notebook creates a repeatable shared package definition.

Incorrect Answers

A session-local installation affects its own process; it is not a shared published dependency contract for scheduled runs.

An Eventhouse stores and queries telemetry with KQL; tables do not install Python modules into Spark executors.

Lakehouse naming only changes data references, not managed Spark package installation or version selection.

Question 5

Two notebooks use the same published Fabric environment. A developer installs a newer library only in a live notebook session and the second scheduled notebook still imports the older version. What is the most likely cause?

The scheduled notebook must be moved to a different Fabric domain to inherit the change.
OneLake automatically prohibits a scheduled notebook from loading any custom Python library.
An Eventstream destination is required for library versions to be shared.
The developer changed session-local dependencies rather than publishing an updated shared environment and running new sessions on it.

Explanation

Correct Answer

Interactive package changes are not the published environment library inventory. Shared scheduled jobs need a published environment update and sessions that load that updated configuration.

Incorrect Answers

Domain assignment affects ownership and discovery; it does not synchronize Python packages between notebook runtimes.

Custom packages are supported through Fabric environments; the relevant distinction is published configuration versus session-local modification.

Eventstream routes streaming events; it is not a package manager or dependency publication mechanism.

Question 6

After the engineering team publishes a shared Fabric environment with pinned package versions, which observation best demonstrates that both a daytime notebook and a nightly Spark job use the same dependency set?

Start fresh sessions for both workloads with the same published environment attached, then inspect their resolved package names and versions.
Verify that both outputs are written to the same lakehouse folder.
Confirm that both workloads have identical names in the monitoring hub.
Check that the two workloads are in the same Fabric domain.

Explanation

Correct Answer

Matching package inventories in new sessions tied to the same environment version demonstrate actual dependency reproducibility in both notebook and job execution contexts.

Incorrect Answers

A shared storage destination does not establish which Python packages each Spark session installed.

Run names or monitoring labels do not determine installed package versions or the effective environment.

Domain membership is a governance grouping and cannot prove library alignment across Spark runtimes.

Question 7

An administrator needs all new Fabric Spark workloads using Workspace default to launch from an appropriately sized default pool, while keeping item-level customization disabled. Which configuration should be changed?

Set OneLake read permissions on every lakehouse table.
Configure the workspace default Spark pool and its sizing/autoscale controls in workspace Spark settings.
Assign a domain contributor role to every notebook author.
Use dynamic data masking on each warehouse column.

Explanation

Correct Answer

With item-level compute overrides disabled, Spark sessions inherit workspace default pool configuration. Admins can select or configure the default pool without requiring per-notebook environment overrides.

Incorrect Answers

OneLake permissions authorize storage access; they cannot size Spark drivers, executors, or the default pool.

Domain contributor rights apply to workspace-domain association and do not set Spark compute capacity parameters.

Dynamic data masking changes displayed SQL values for eligible users, not Spark resource allocation.

Question 8

A contributor tries to set a larger Spark executor size through a Fabric environment. Its Compute section is disabled, and the workspace admin has explicitly turned off customization of compute for items. Why can the contributor not apply this setting?

The contributor must create an Eventhouse first to enable Spark executors.
Disabling item-level compute customization forces Spark jobs to use workspace default compute and prevents environment-level overrides.
The environment has to contain a OneLake shortcut before its Compute section can be edited.
Spark compute is always configured only through deployment pipelines.

Explanation

Correct Answer

The workspace setting is an administrative gate. When disabled, an environment item cannot override driver/executor compute properties, regardless of a contributor editing a notebook.

Incorrect Answers

Eventhouse is a KQL data store and has no role in enabling workspace Spark item-level compute customization.

OneLake storage links are independent of Spark pool override permissions in an environment.

Deployment pipelines can promote supported configurations, but the item-level customization toggle governs whether an environment may define compute overrides.

Question 9

A workspace admin updates the default Spark pool autoscale settings. What is the strongest validation that a newly submitted notebook without a custom environment picks up the intended compute policy?

Open the warehouse SQL endpoint and count catalog objects.
Confirm that the environment resource-file pane lists the expected number of files.
Review a notebook output created before the admin changed pool settings.
Start a new notebook session and inspect its selected pool and effective Spark executor/autoscale configuration.

Explanation

Correct Answer

A fresh Spark session reflects newly effective workspace defaults; checking its actual pool and execution settings tests the compute policy rather than merely the admin form.

Incorrect Answers

Warehouse SQL metadata cannot verify the selected Spark pool or executor settings for a notebook.

An environment resource file count is unrelated to default Spark pool sizing and autoscale configuration.

Historical notebook outputs could have been produced using the old pool; they do not verify new-session configuration.

Question 10

An analytics team has a stable workspace default Spark runtime, but one compatibility-test notebook must use a newer runtime and associated packages without changing other notebooks. Which TWO actions satisfy that requirement?

Create or configure a Fabric environment with the required supported runtime and package versions, and publish it.
Replace the workspace default environment with the test environment for every notebook.
Change only the test notebook’s default lakehouse.
Attach that published environment explicitly to the compatibility-test notebook and start a fresh session.
Assign the compatibility-test notebook to a separate Fabric domain while leaving its runtime selection unchanged.

Explanation

Correct Answers

An environment item isolates a chosen Spark runtime and libraries; publishing applies the configuration needed by a new session.

A notebook-level environment selection overrides Workspace default for that notebook, leaving other workloads on the original workspace setting.

Incorrect Answers

A workspace-wide switch would change unrelated notebooks using Workspace default, violating the requirement to isolate the test.

The default lakehouse controls which data is mounted; it does not select a Spark runtime or library set.

Domain association is a governance choice at workspace level, not a runtime override for a single notebook.

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

Microsoft DP-700 Practice Test 1 - 10 Questions (Last Reviewed 03-Nov-2026)
Microsoft DP-700 Practice Test 2 - 10 Questions (Last Reviewed 02-Nov-2026)
Microsoft DP-700 Practice Test 3 - 10 Questions (Last Reviewed 01-Nov-2026)

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Question 1

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Question 2

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Question 3

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Question 9

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Question 10

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