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Exam Code: DP-700

Exam Name Implementing Data Engineering Solutions Using Microsoft Fabric

Certification Provider: Microsoft

Corresponding Certification: Microsoft Certified: Fabric Data Engineer Associate

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

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Microsoft DP-700 Practice Test 3

Question 20

A source-control commit for a lakehouse is expected to include the Files folder's customer CSVs, but only metadata appears in Git. Which action best addresses the requirement?

  1. Inspect and correct dependency mapping independently of the newly created lakehouse.
  2. Deploy lakehouse metadata while treating table data as separately managed.
  3. Keep lakehouse data files in governed storage and version only supported lakehouse metadata in Git.
  4. Commit the environment configuration and notebook changes together when both are supported.

Explanation

Correct Answer

C:

Git integration does not version the table data or Files contents of a lakehouse.

Incorrect Answers

A:

Default deployment behavior may provision an empty lakehouse even when references are mapped elsewhere.

B:

Lakehouse deployment and Git operations track metadata and definitions, not lakehouse table contents.

D:

Co-versioning dependencies avoids deploying code without required runtime configuration.

Question 21

In a Microsoft Fabric solution, an operations team plans a deployment that changes multiple supported Fabric items and needs rollback traceability. Which configuration or implementation should the engineer choose?

  1. Use a SQL database project containing the warehouse schema definitions.
  2. Retain the external shortcut target when promoting the lakehouse definition.
  3. Tag and retain the source Git commit used for the release before promoting.
  4. Commit the environment configuration and notebook changes together when both are supported.

Explanation

Correct Answer

C:

A known commit provides an immutable reference to the item definitions being deployed.

Incorrect Answers

A:

Database projects capture deployable SQL object definitions in source control.

B:

External OneLake shortcut definitions keep their referenced external targets during supported deployment.

D:

Co-versioning dependencies avoids deploying code without required runtime configuration.

Question 22

In a Microsoft Fabric solution, a team wants a preview of which Fabric item definitions differ between deployment pipeline stages before releasing to production. Which configuration or implementation should the engineer choose?

  1. Use deployment pipeline dependency mapping for internal shortcut references.
  2. Use the deployment pipeline stage comparison to review pending item changes.
  3. Use environment-specific deployment configuration while keeping one versioned database project.
  4. Review the Git changes and differences for the affected supported Fabric items.

Explanation

Correct Answer

B:

Stage comparison identifies supported items that changed or are missing between stages.

Incorrect Answers

A:

Internal shortcuts can be remapped to corresponding items across supported stages.

C:

Separating deployment settings from schema definitions maintains one canonical source model.

D:

Git comparison exposes modified definitions prior to persistence.

Question 23

In a Microsoft Fabric solution, an external OneLake shortcut connects to a restricted storage account, and item sharing alone does not permit reading the target. Which configuration or implementation should the engineer choose?

  1. Provide a valid connection and source-side permissions for the external shortcut.
  2. Grant only the required permissions on that warehouse item.
  3. Check external collaboration and guest-access configuration in Microsoft Entra.
  4. Inspect the item's default reader security role before adding more grants.

Explanation

Correct Answer

A:

The target storage system and shortcut credentials must authorize the requested read.

Incorrect Answers

B:

Item-level sharing can give access to a specific Fabric resource rather than broad workspace membership.

C:

Guest identity settings can prevent OneLake role grants from being applied as expected.

D:

Fabric can create a default reader role for users with ReadAll, so effective data access may come from defaults.

Question 24

CSV amounts and dates are strings, but the curated Delta schema requires numeric and timestamp types. Which action best addresses the requirement?

  1. Create a shortcut in the lakehouse Files section.
  2. Run the logic in a Fabric PySpark notebook.
  3. Explicitly cast and parse the fields in PySpark.
  4. Parse files and write them to registered Delta tables.

Explanation

Correct Answer

C:

Typed transforms avoid storing measures as text.

Incorrect Answers

A:

Files shortcuts expose arbitrary formats without automatic table registration.

B:

Notebooks support custom distributed transformations and environments.

D:

Files storage is separate from registered lakehouse tables.

Question 25

High-cardinality customer filtering is common but static customer partitions create tiny directories. Which action best addresses the requirement?

  1. Tune supported ingestion batching policy for the feed.
  2. Reorder supported steps to preserve query folding.
  3. Tune shuffle partition count to match volume and resources.
  4. Apply supported liquid clustering on customer key.

Explanation

Correct Answer

D:

Clustering improves locality without a directory for each value.

Incorrect Answers

A:

Suitable batching balances ingest overhead with freshness.

B:

Folding pushes selective work into the source.

C:

Excess tiny tasks add scheduling overhead.

Question 26

In a Microsoft Fabric solution, a Copy activity reports success but the destination contains fewer rows than the source. Which configuration or implementation should the engineer choose?

  1. Open the pipeline run in the Fabric monitoring experience and inspect its activity runs.
  2. Inspect Eventstream run metrics and destination-specific health.
  3. Inspect Copy activity input/output metrics and reconcile source and sink counts.
  4. Add data-quality reconciliation metrics to the monitoring gate.

Explanation

Correct Answer

C:

Copy statistics reveal transfer counts and possible skipped or rejected rows.

Incorrect Answers

A:

Activity-level monitoring identifies failing steps and their diagnostic output.

B:

Stream-level monitoring can reveal throughput and routing failures.

D:

Transport success does not prove that transformed business totals are correct.

Question 27

A team grants a person item-level Read access to one report but they still cannot browse unrelated items. Which action best addresses the requirement?

  1. Reduce broad workspace access and rely on narrowly scoped item and data roles.
  2. Create a OneLake security role granting Read on the selected tables.
  3. Apply the approved sensitivity label to the item.
  4. Maintain the specific item share rather than granting workspace Contributor.

Explanation

Correct Answer

D:

Item access is narrower than workspace roles and avoids unnecessary creation and modification rights.

Incorrect Answers

A:

Workspace access can confer item-wide rights that undermine fine-grained data access goals.

B:

OneLake roles scope data permissions within the item and deny access to ungranted objects.

C:

Sensitivity labels classify and govern sensitive information independently of workspace membership.

Question 28

In a Fabric workspace, the notebook high-concurrency option was disabled. Two compatible notebooks run under the same identity with the same default lakehouse, Spark configuration, and libraries, and repeatedly incur independent Spark startup overhead. Which single implementation most directly meets the stated requirement?

  1. Disable item-level compute customization in the workspace Spark settings.
  2. Enable diagnostic events in workspace OneLake settings and select the approved diagnostic lakehouse.
  3. Enable the For notebooks high-concurrency option under the workspace Spark settings.
  4. Set the chosen published environment as the workspace default environment.

Explanation

Correct Answer

C:

High-concurrency mode allows compatible interactive notebooks to attach to a shared running Spark session instead of creating an independent Spark application for each notebook. The direct validation would be: The two compatible notebooks attach to the same active high-concurrency Spark session without starting a second Spark application.

Incorrect Answers

A:

Turning off item overrides makes notebooks inherit the workspace default pool configuration.

B:

The OneLake settings tab allows workspace administrators to route supported OneLake diagnostic events to a designated lakehouse.

D:

Workspace default can inherit a selected environment's compute and library configuration.

Question 29

An organization needs a warehouse security policy keyed to each analyst's login name rather than a static list of rows. Which engineering decision is most suitable for this specific situation?

  1. Grant only the required permissions on that warehouse item.
  2. Use column-level access controls to deny the Salary column to analysts.
  3. Configure a partial mask tailored to the supported string column.
  4. Define an RLS predicate that evaluates the current user context.

Explanation

Correct Answer

D:

Identity-aware filtering maps each caller to authorized rows at query time.

Incorrect Answers

A:

Item-level sharing can give access to a specific Fabric resource rather than broad workspace membership.

B:

Column-level security restricts permission to access specific columns rather than merely displaying masked values.

C:

Partial masking preserves selected characters while concealing the remainder of a string.

Question 30

A dataflow refresh is triggered as part of a nightly pipeline but must finish before downstream notebook processing. Which engineering decision is most suitable for this specific situation?

  1. Build a Fabric data pipeline with Copy and Notebook activities and failure handling.
  2. Invoke the reusable pipeline through an Execute Pipeline activity.
  3. Place both notebook activities on parallel branches after the staging success dependency.
  4. Place the dataflow activity upstream of the notebook in the pipeline dependency graph.

Explanation

Correct Answer

D:

Explicit dependencies prevent processing before upstream dataflow completion.

Incorrect Answers

A:

Pipelines orchestrate multi-activity ingestion and dependent jobs.

B:

Child pipelines allow shared orchestration logic without cloning all activities.

C:

Parallel branches enable independent work once common prerequisites are met.