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Microsoft DP-700 Practice Test 3

Question 20

A user may read one child folder but should not gain access to its sibling folder. What OneLake behavior enables navigation safely?

  1. Assess the union of grants from both roles, after each role's own restrictions are evaluated.
  2. Enable supported OneLake shortcut caching in workspace settings and select a justified retention period.
  3. Assign a scoped OneLake ReadWrite role on the target folder, with necessary item read permissions.
  4. Allow parent traversal for the permitted child without granting sibling data access.

Explanation

Correct Answer

D:

OneLake can expose enough ancestor listing to reach an authorized child while retaining data restrictions on unrelated sibling paths. Validate that sibling file reads still fail.

Incorrect Answers

A:

OneLake uses grant-based role composition; a narrower role does not globally deny data allowed by a separate broader role.

B:

Workspace shortcut caching can reduce repeated remote reads, but it does not grant permission to traverse an allowed child folder or protect sibling folders. The stated problem is ancestor navigation with strict sibling access boundaries.

C:

A scoped ReadWrite role controls where a user can edit files; it is not required merely to traverse ancestors toward an authorized child. Preserve the child's read permission and avoid granting data rights to its siblings.

Question 21

A developer needs to write to one lakehouse folder yet has only Viewer-level workspace access. Which precise permission can enable that limited write?

  1. Grant the developer only workspace Viewer and item Read permissions and attempt the folder upload again.
  2. Enable supported OneLake shortcut caching in workspace settings and select a justified retention period.
  3. Assign a scoped OneLake ReadWrite role on the target folder, with necessary item read permissions.
  4. Check both OneLake role access to the shortcut and the external connection's source credentials.

Explanation

Correct Answer

C:

ReadWrite roles can grant scoped writes to otherwise read-only users; granting Contributor for the whole workspace would be unnecessarily broad. Test a file upload inside the folder and denial outside it.

Incorrect Answers

A:

Read-only workspace/item permissions do not confer the scoped OneLake ReadWrite capability needed for data writes to the allowed folder. Test a file upload inside the folder and denial outside it.

B:

Workspace OneLake caching stores eligible external shortcut files locally for subsequent reads and may reduce repeat egress.

D:

A visible shortcut is a reference, not a bypass of external storage authorization or Fabric data permissions.

Question 22

A security team configures row filtering on a OneLake role and then switches that role to ReadWrite. What constraint should they address?

  1. Use a Read-only role for row or column security and separate permitted write access appropriately.
  2. Keep the OneLake role at ReadWrite and add RLS filters to that same role.
  3. Enable supported OneLake shortcut caching in workspace settings and select a justified retention period.
  4. Check both OneLake role access to the shortcut and the external connection's source credentials.

Explanation

Correct Answer

A:

A OneLake ReadWrite role cannot include row-level or column-level security filters. Keep the protected table in a Read-only filtered role and separate any authorized write grant so it does not overlap and bypass the protected data through union of effective role grants.

Incorrect Answers

B:

Adding row-level conditions directly to a role configured for ReadWrite is unsupported. Grant-scoped write access and filtered read access must also be designed to avoid overlapping permissions that inadvertently expose restricted rows.

C:

Workspace OneLake caching stores eligible external shortcut files locally for subsequent reads and may reduce repeat egress. OneLake roles with ReadWrite cannot contain RLS or CLS restrictions, so the design cannot rely on that combination in one role. Inspect the role permissions and its configured row/column restrictions.

D:

A visible shortcut is a reference, not a bypass of external storage authorization or Fabric data permissions. OneLake roles with ReadWrite cannot contain RLS or CLS restrictions, so the design cannot rely on that combination in one role. Inspect the role permissions and its configured row/column restrictions.

Question 23

A user belongs to two OneLake roles, one granting a table and another granting an additional folder. How is effective access formed?

  1. Assess the union of grants from both roles, after each role's own restrictions are evaluated.
  2. Assign a scoped OneLake ReadWrite role on the target folder, with necessary item read permissions.
  3. Apply the intersection of the two unrelated OneLake Read grants so only objects common to both remain accessible.
  4. Enable supported OneLake shortcut caching in workspace settings and select a justified retention period.

Explanation

Correct Answer

A:

OneLake uses grant-based role composition; a narrower role does not globally deny data allowed by a separate broader role. Inspect the user's effective access across both role memberships.

Incorrect Answers

B:

ReadWrite roles can grant scoped writes to otherwise read-only users; granting Contributor for the whole workspace would be unnecessarily broad.

C:

OneLake grant roles are additive for disjoint unrestricted objects, not an intersection. The table and additional folder can both be read unless a separate applicable restriction changes the effective result. Inspect the user's effective access across both role memberships.

D:

Workspace OneLake caching stores eligible external shortcut files locally for subsequent reads and may reduce repeat egress.

Question 24

An operator wants to purge an external-shortcut file cache from only one workspace before rebenchmarking a workload. What is the appropriate control?

  1. Use Reset cache on the OneLake tab of the affected workspace settings.
  2. Restart the Spark session associated with the benchmark notebook.
  3. Reduce shortcut cache retention to one day and rerun the benchmark immediately.
  4. Run a Delta VACUUM operation on the lakehouse table that consumes the shortcut.

Explanation

Correct Answer

A:

Reset cache clears the workspace shortcut cache; it does not delete underlying S3 or GCS source data. Read the source again and verify that cache warming restarts.

Incorrect Answers

B:

Restarting a notebook Spark session changes in-memory Spark state but does not provide workspace-scoped shortcut-cache eviction.

C:

Shorter retention controls future expiration; it does not implement a deliberate immediate purge of all current cached files. The scenario, however, is that an operator wants to purge an external-shortcut file cache from only one workspace before rebenchmarking a workload.

D:

Delta VACUUM cleans unreferenced Delta files, not OneLake's workspace cache of external shortcut files. In the current incident, an operator wants to purge an external-shortcut file cache from only one workspace before rebenchmarking a workload.

Question 25

An existing team has Python DAG dependencies and requires scheduled dependency-aware tasks, not a single SQL statement. Which Fabric facility best matches the orchestration style?

  1. Configure an Apache Airflow job and deploy the DAG in the intended Fabric workspace.
  2. Remove artificial dependencies between independent tasks so Airflow can schedule eligible work concurrently.
  3. Inspect the failed Airflow task instance logs and retry history.
  4. Configure a suitable custom Apache Airflow runtime pool in workspace Data Factory settings.

Explanation

Correct Answer

A:

Airflow uses DAGs and tasks for dependency-driven workflow execution, preserving a Python orchestration pattern instead of forcing it into a query editor. Inspect the DAG graph and trigger a controlled test run.

Incorrect Answers

B:

The dependency graph, not alphabetical task order, should represent true data requirements; false dependencies increase critical-path length.

C:

Task logs show the exception at the failing operation and separate recoverable retries from graph-level status.

D:

Custom Airflow pools permit controlled node sizing and an always-on runtime, while starter pools are oriented to intermittent development use.

Question 26

An Airflow workflow is imported but cannot locate its Python dependency module at runtime. What should the engineer package or configure?

  1. Remove artificial dependencies between independent tasks so Airflow can schedule eligible work concurrently.
  2. Supply a run-time parameter or DAG configuration value and read it in the relevant task.
  3. Provide the DAG's required Python dependencies in the Airflow job's supported environment configuration.
  4. Configure a suitable custom Apache Airflow runtime pool in workspace Data Factory settings.

Explanation

Correct Answer

C:

A DAG's source alone cannot satisfy missing imports; the execution environment must include libraries referenced by tasks. Rerun the failing task and inspect the import error.

Incorrect Answers

A:

The dependency graph, not alphabetical task order, should represent true data requirements; false dependencies increase critical-path length.

B:

Parameterizing the DAG separates execution-specific values from committed task definitions and avoids repetitive code changes.

D:

Custom Airflow pools permit controlled node sizing and an always-on runtime, while starter pools are oriented to intermittent development use.

Question 27

A batch DAG must wait for an upstream extraction task before starting reconciliation. Which Airflow mechanism expresses the required order?

  1. Configure a suitable custom Apache Airflow runtime pool in workspace Data Factory settings.
  2. Inspect the failed Airflow task instance logs and retry history.
  3. Configure an Apache Airflow job and deploy the DAG in the intended Fabric workspace.
  4. Declare the explicit upstream/downstream task dependencies in the DAG.

Explanation

Correct Answer

D:

Airflow schedules runnable tasks from the DAG dependency graph; task names alone do not establish ordering. Check graph view to confirm reconciliation has the extraction dependency.

Incorrect Answers

A:

Custom Airflow pools permit controlled node sizing and an always-on runtime, while starter pools are oriented to intermittent development use.

B:

Task logs show the exception at the failing operation and separate recoverable retries from graph-level status.

C:

Airflow uses DAGs and tasks for dependency-driven workflow execution, preserving a Python orchestration pattern instead of forcing it into a query editor.

Question 28

A production Fabric Airflow workflow needs an always-on runtime with adjustable nodes rather than a development-oriented starter pool. Which workspace setting should change?

  1. Configure a suitable custom Apache Airflow runtime pool in workspace Data Factory settings.
  2. Increase the workspace Spark starter-pool executor count for the Airflow DAG.
  3. Change the DAG schedule to run every five minutes so the starter pool is kept active.
  4. Keep the Airflow starter pool and add retries to all tasks in the DAG.

Explanation

Correct Answer

A:

Custom Airflow pools permit controlled node sizing and an always-on runtime, while starter pools are oriented to intermittent development use. Inspect the selected runtime pool and production DAG scheduling behavior.

Incorrect Answers

B:

The Spark session pool is not the Airflow runtime-pool control; modifying Spark does not create an always-on customizable Airflow pool. That is a separate improvement from solving a case in which a production Fabric Airflow workflow needs an always-on runtime with adjustable nodes rather than a development-oriented starter pool.

C:

A more frequent DAG schedule is a workload decision, not an explicit always-on runtime with selectable node size and autoscaling. Such a change is legitimate for its own purpose, but a production Fabric Airflow workflow needs an always-on runtime with adjustable nodes rather than a development-oriented starter pool.

D:

Retries handle task failures but retain starter-pool sizing and auto-deprovisioning behavior. The target requirement is different: a production Fabric Airflow workflow needs an always-on runtime with adjustable nodes rather than a development-oriented starter pool.

Question 29

An Airflow job has retried a task after a transient API failure; operations need to see the actual exception. Where should they begin?

  1. Supply a run-time parameter or DAG configuration value and read it in the relevant task.
  2. Configure an Apache Airflow job and deploy the DAG in the intended Fabric workspace.
  3. Provide the DAG's required Python dependencies in the Airflow job's supported environment configuration.
  4. Inspect the failed Airflow task instance logs and retry history.

Explanation

Correct Answer

D:

Task logs show the exception at the failing operation and separate recoverable retries from graph-level status. Use the task's recorded run ID to retrieve the exact error.

Incorrect Answers

A:

Parameterizing the DAG separates execution-specific values from committed task definitions and avoids repetitive code changes.

B:

Airflow uses DAGs and tasks for dependency-driven workflow execution, preserving a Python orchestration pattern instead of forcing it into a query editor.

C:

A DAG's source alone cannot satisfy missing imports; the execution environment must include libraries referenced by tasks.

Question 30

A DAG should accept a per-run reporting date without editing its Python source every day. Which approach supports that flexibility?

  1. Configure an Apache Airflow job and deploy the DAG in the intended Fabric workspace.
  2. Configure a suitable custom Apache Airflow runtime pool in workspace Data Factory settings.
  3. Inspect the failed Airflow task instance logs and retry history.
  4. Supply a run-time parameter or DAG configuration value and read it in the relevant task.

Explanation

Correct Answer

D:

Parameterizing the DAG separates execution-specific values from committed task definitions and avoids repetitive code changes. Run two dates and confirm the task consumes each configured value.

Incorrect Answers

A:

Airflow uses DAGs and tasks for dependency-driven workflow execution, preserving a Python orchestration pattern instead of forcing it into a query editor.

B:

Custom Airflow pools permit controlled node sizing and an always-on runtime, while starter pools are oriented to intermittent development use.

C:

Task logs show the exception at the failing operation and separate recoverable retries from graph-level status.

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