Microsoft DP-700 Practice Test 3

Question 20

An authorized Fabric domain contributor can view the Analytics workspace but cannot assign it to a domain. The user has the workspace Member role, not Admin. Which limitation is responsible?

Only a KQL database administrator can assign Fabric workspaces to domains.
The user must publish an environment item before domain assignment is available.
A domain contributor must also have the Admin role in the workspace they want to assign.
Domain contributors can modify only workspaces containing exactly one lakehouse.

Explanation

Correct Answer

The two permissions serve different scopes: domain contributor authorization alone does not replace the required workspace administration privilege for assignment.

Incorrect Answers

KQL database permission controls Eventhouse data, not workspace-domain membership.

Spark environment publishing has no bearing on domain-contributor permission checks.

The documented requirement is the relevant workspace Admin role; membership is not restricted by a one-item rule.

Question 21

A domain contributor has been delegated access for Finance. What is the clearest validation that the contributor’s permissions are correctly scoped?

Confirm the contributor can associate a workspace they administer with Finance, but cannot associate a workspace where they lack the Admin role.
Confirm the contributor can change every tenant-level security policy.
Confirm the contributor can edit all warehouse tables in Finance.
Confirm that the contributor can publish any unrelated Spark environment.

Explanation

Correct Answer

This checks both parts of the delegation model: the assigned domain contribution and the independently required workspace administration rights.

Incorrect Answers

Domain contributor status is not equivalent to Fabric tenant administrator and should not imply unrestricted tenant controls.

Domain contributor authorization does not grant blanket data-plane modification rights across associated workspaces.

Publishing permissions are item/workspace-specific and do not prove appropriate domain-assignment scope.

Question 22

A batch ingestion tool outside Fabric must address a lakehouse folder through OneLake without relying on a notebook’s default lakehouse mount. Which path convention should the engineer use?

Use the local relative notebook path /lakehouse/default/Files regardless of the tool location.
Use a warehouse SQL statement as a file path.
Use a supported OneLake DFS or ABFSS URI containing the workspace and the lakehouse item plus its Files or Tables path.
Use the display name of a Fabric domain as the sole root path.

Explanation

Correct Answer

OneLake exposes ADLS-compatible endpoint paths. The workspace and item identifiers, with the correct item type or GUID syntax, identify the actual remote folder for external clients.

Incorrect Answers

The notebook default mount is local to a configured notebook runtime; it is not an external storage URI for arbitrary clients.

A T-SQL query is not an ADLS-compatible file URI and cannot address arbitrary OneLake folder contents.

Domains group workspaces for governance; OneLake file paths require workspace and item targeting, not just a domain label.

Question 23

An external Spark job reads a OneLake ABFSS path. The workspace was renamed, and the script had hard-coded the old workspace display name rather than immutable IDs. Why can the path now fail?

The warehouse has automatically disabled all PySpark libraries.
Name-based OneLake references depend on the current workspace and item path tokens; GUID-based references avoid display-name drift.
Domain membership changes Spark runtime versions for external tools.
OneLake can never be addressed through an ABFSS URI.

Explanation

Correct Answer

OneLake resolves the workspace and item components of the URI. A renamed name-based component can break that lookup, whereas GUIDs remain stable through renaming.

Incorrect Answers

The failure concerns resolving a remote OneLake path, not availability of local Python libraries.

Workspace-domain governance is independent of the external client’s ABFSS path resolution.

Microsoft documents ABFSS and HTTPS/DFS endpoint formats for OneLake access.

Question 24

A pipeline runs in multiple Fabric workspaces, and a deployment may rename both the workspace and lakehouse. Which TWO design decisions give the most reliable OneLake path and validation?

Use only the display name of the Fabric domain in the file URI.
Build an approved OneLake DFS or ABFSS reference using the workspace and item GUIDs instead of their display names.
Rely on /lakehouse/default/Files in an external client without notebook attachment.
Rename the target lakehouse every time a pipeline is deployed.
Validate the exact Files or Tables path and effective permissions from the target run identity.

Explanation

Correct Answers

Microsoft supports GUID-based references for workspace and item identifiers; they remain stable under rename and avoid name-token ambiguity.

A valid identifier is not sufficient if the subpath is wrong or the caller lacks data access. An actual read from the target identity verifies both boundaries.

Incorrect Answers

Domain labels do not replace the workspace and item components required in OneLake paths.

That relative mount is a notebook concept and is not a portable OneLake endpoint for independent clients.

Renaming assets is unnecessary for stable GUID-based addressing and may create additional broken name-based references.

Question 25

A Fabric engineering team plans to operate Apache Airflow DAGs as a managed Fabric workload. Production orchestration needs an always-on configurable pool rather than a starter pool that is deprovisioned when idle. Which setup should the workspace administrator use?

Configure a Custom Pool under workspace Data Factory > Apache Airflow Runtime Settings and select it for the Airflow workload.
Increase the Spark notebook executor count and assume Airflow inherits it.
Add a OneLake shortcut to a DAG source directory and leave Airflow on starter pool.
Assign the workspace to a Finance domain to force DAG workers to stay online.

Explanation

Correct Answer

Fabric Airflow job settings distinguish the default starter pool from a customizable always-on pool with node sizing and concurrency controls suitable for ongoing production DAG operation.

Incorrect Answers

Apache Airflow runtime pools have their own workspace Data Factory settings; changing Spark executors does not configure the Airflow pool.

The shortcut can expose data but does not change Airflow compute uptime, autoscale, or worker allocation.

Domain classification does not manage Airflow pool lifetime or compute sizing.

Question 26

A Fabric Airflow job becomes unavailable after a period of inactivity. Its workspace still uses the default Starter Pool, and the team expected the runtime to stay on continuously. What explains the stop?

The starter Airflow pool can shut down after an idle period; an appropriately configured custom pool is the always-on option.
Every Fabric Airflow job needs a Spark library published every twenty minutes.
The workspace domain has expired and must be recreated.
A lakehouse Files folder missing a table named airflow_pool caused the runtime to stop.

Explanation

Correct Answer

Airflow Starter Pool prioritizes on-demand development use and deprovisions when idle, while a custom pool is designed for persistent capacity until manually paused.

Incorrect Answers

Airflow pool lifecycle is separate from publishing notebook Spark libraries; frequent library publication is not required.

Fabric domain assignment is not an Airflow compute-lifetime timer.

Fabric pool lifecycle is managed by workspace Data Factory settings, not by the presence of a specially named table.

Question 27

A workspace admin changes Apache Airflow to a custom runtime pool for production DAGs. Which check best verifies that the intended workspace configuration is active?

Inspect the SQL warehouse auto-statistics state.
Confirm an unrelated Spark notebook uses a different Python environment.
Inspect the Airflow runtime settings for the selected custom pool and confirm a new job run uses that pool with expected worker capacity.
Check that the workspace has a certified endorsement badge.

Explanation

Correct Answer

The effective pool selection and a real DAG run demonstrate the intended Airflow compute boundary; the label alone is not proof of executed worker capacity.

Incorrect Answers

Warehouse query optimization settings do not control Apache Airflow worker pools.

Spark notebooks and Airflow jobs have distinct runtime settings, so the notebook observation is not proof of Airflow pool assignment.

Governance endorsement describes asset discoverability/trust, not Airflow runtime pool selection.

Question 28

A workspace currently uses a Power BI shared workspace type and its owner cannot create the required Fabric lakehouse item. An eligible Fabric capacity is available. What should the authorized administrator do?

Change the workspace type and assign the workspace to the eligible Fabric-enabled capacity.
Change only the workspace name to contain the word Fabric.
Publish a custom Spark Python environment while remaining on unsupported shared capacity.
Create a KQL function in an unrelated Eventhouse.

Explanation

Correct Answer

Fabric item creation depends on compatible workspace licensing/capacity. Workspace settings allow an authorized admin to reassign the workspace to a Fabric-capable capacity.

Incorrect Answers

Renaming a workspace does not change its capacity type or enable item creation rights.

Publishing a library definition cannot substitute for a workspace assigned to capacity that supports the Fabric workload.

A KQL function cannot modify workspace capacity or its eligibility to host lakehouse items.

Question 29

A data engineer has a role allowing workspace item management but does not see the required Fabric item creation experience. The workspace is on a Power BI-only shared type, not an eligible Fabric capacity. Which root issue should be addressed?

A OneLake folder with a matching workspace name has not been created manually.
The workspace has no domain contributor role.
The workspace capacity/workspace type is not configured for the requested Fabric item workload.
The notebook lacks a row-level security filter.

Explanation

Correct Answer

Fabric features depend on an eligible capacity assignment. Permissions to manage existing items do not by themselves enable unsupported Fabric workloads on shared capacity.

Incorrect Answers

OneLake storage is managed by Fabric; a manually created folder is not the eligibility prerequisite for item creation.

Domain delegation handles governance grouping and does not determine capacity compatibility for lakehouse creation.

Data access filters cannot cause the Fabric item type to disappear from a workspace using an ineligible capacity.

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.

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)
Microsoft DP-700 Practice Test 4 - 10 Questions (Last Reviewed 31-Oct-2026)
Microsoft DP-700 Practice Test 5 - 10 Questions (Last Reviewed 30-Oct-2026)

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Question 20

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Question 21

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Question 22

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Question 23

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Question 24

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Question 25

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Question 26

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Question 27

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Question 28

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Question 29

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Question 30

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