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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 23

Question 220

A scheduled ingestion should run on two distinct times each day and needs different runtime parameter values for each window. Which action best addresses the requirement?

  1. Expose the business date as a pipeline parameter and supply it at invocation.
  2. Create two schedules on the pipeline with their respective parameter values.
  3. Configure failure notifications on the scheduled pipeline run.
  4. Inspect the pipeline's individual activity runs and their error outputs.

Explanation

Correct Answer

B:

Multiple schedules can invoke the same pipeline with different times and supplied parameters.

Incorrect Answers

A:

A runtime parameter separates job logic from each run's effective processing date.

C:

Scheduled pipelines support configured failure notifications distinct from manual invocation.

D:

Activity-level results expose the step and message causing an orchestration failure.

Question 221

A complex ingestion algorithm requires custom PySpark logic, stateful joins, and versioned Python packages. Which action best addresses the requirement?

  1. Place the dataflow activity upstream of the notebook in the pipeline dependency graph.
  2. Build a Fabric data pipeline with Copy and Notebook activities and failure handling.
  3. Run the transformation in a Fabric Spark notebook.
  4. Place both notebook activities on parallel branches after the staging success dependency.

Explanation

Correct Answer

C:

A notebook offers programmatic Spark transformations that go beyond a visual mapping.

Incorrect Answers

A:

Explicit dependencies prevent processing before upstream dataflow completion.

B:

Pipelines orchestrate multi-activity ingestion and dependent jobs.

D:

Parallel branches enable independent work once common prerequisites are met.

Question 222

Two small lookup datasets need a visual join on stable customer identity. What should the engineer implement first to satisfy the stated need?

  1. Use Delta MERGE keyed by order ID.
  2. Use GROUP BY customer with HAVING COUNT condition.
  3. Use a PySpark left anti join on customer ID.
  4. Apply Power Query Merge on the selected customer key.

Explanation

Correct Answer

D:

Merge combines records according to its join condition.

Incorrect Answers

A:

MERGE handles matched updates and unmatched inserts.

B:

HAVING filters groups after aggregation.

C:

The anti join returns left rows with no right-side match.

Question 223

A high-concurrency session must support twenty compatible notebooks, but only the default session sharing limit is applied. Which engineering decision is most suitable for this specific situation?

  1. Choose the required supported Fabric Spark runtime in the environment and publish it.
  2. Configure executor sizing in a dedicated environment attached only to that notebook.
  3. Set spark.highConcurrency.max to 20 in the attached environment's Spark properties.
  4. Publish a Fabric environment with the common library and attach it to the notebooks.

Explanation

Correct Answer

C:

That property can raise the high-concurrency session's supported workload limit within the documented bounds.

Incorrect Answers

A:

The runtime version is configured on a Fabric environment and applied when a new session starts.

B:

A workload-specific environment can override compute sizes when the workspace permits customization.

D:

Environment items centrally define reusable libraries and Spark runtime settings.

Question 224

In a Microsoft Fabric solution, devices send delayed events with an event-created timestamp different from ingestion time. Which configuration or implementation should the engineer choose?

  1. Apply an event-time watermark that covers the accepted lateness policy.
  2. Use a stateful streaming join with a valid event-time bound and watermark policy.
  3. Base time-series grouping on the business event timestamp.
  4. Use KQL make-series with the intended time range and step.

Explanation

Correct Answer

C:

Event-time semantics reflect when the event occurred, not when ingestion received it.

Incorrect Answers

A:

Watermarks bound the amount of late data retained in streaming state.

B:

Bounded joins limit held state and match related events by key and time.

D:

Make-series constructs regular time bins and can represent empty intervals.

Question 225

An internal OneLake shortcut refers to another lakehouse in Development and the corresponding lakehouse exists in Test. What should the engineer implement first to satisfy the stated need?

  1. Use environment-specific deployment configuration while keeping one versioned database project.
  2. Configure a Fabric deployment pipeline with stages assigned to the three workspaces.
  3. Commit the changed workspace items through the Fabric Git integration.
  4. Use deployment pipeline dependency mapping for internal shortcut references.

Explanation

Correct Answer

D:

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

Incorrect Answers

A:

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

B:

Deployment pipelines promote supported Fabric items across staged workspaces.

C:

A workspace-to-Git commit records supported item definition changes in the connected repository.

Question 226

In a Microsoft Fabric solution, warehouse SQL queries time out at busy periods and the engineer needs currently active request details. Which configuration or implementation should the engineer choose?

  1. Inspect supported SQL dynamic management views for requests and sessions.
  2. Configure an appropriate Fabric real-time alert through the supported alert experience.
  3. Inspect the current run's activity state and dependency graph.
  4. Inspect KQL query diagnostics or query statistics for representative runs.

Explanation

Correct Answer

A:

SQL DMVs expose active workload state needed for investigation.

Incorrect Answers

B:

Alerts watch incoming query or event conditions without manually polling dashboards.

C:

Activity states reveal which step has begun, is pending, or is blocked.

D:

Query metrics show performance beyond final row counts.

Question 227

A team wants two notebooks to share a high-concurrency Spark session, but they run using separate user identities. Which action best addresses the requirement?

  1. Publish a Fabric environment with the common library and attach it to the notebooks.
  2. Run eligible workloads under the same execution identity without sharing users' credentials.
  3. Set the chosen published environment as the workspace default environment.
  4. Disable item-level compute customization in the workspace Spark settings.

Explanation

Correct Answer

B:

Session sharing is bounded by user identity and cannot cross a user boundary.

Incorrect Answers

A:

Environment items centrally define reusable libraries and Spark runtime settings.

C:

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

D:

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

Question 228

In a Microsoft Fabric solution, an Eventhouse table is not receiving an expected event feed and support needs ingestion observability. Which configuration or implementation should the engineer choose?

  1. Inspect Eventstream run metrics and destination-specific health.
  2. Add data-quality reconciliation metrics to the monitoring gate.
  3. Inspect Eventhouse ingestion and database monitoring metrics.
  4. Inspect KQL query diagnostics or query statistics for representative runs.

Explanation

Correct Answer

C:

Ingestion monitoring distinguishes source outages from slow arrival or indexing.

Incorrect Answers

A:

Stream-level monitoring can reveal throughput and routing failures.

B:

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

D:

Query metrics show performance beyond final row counts.

Question 229

A large feed requires custom Python parsing, Spark joins, and project-specific libraries. What should the engineer implement first to satisfy the stated need?

  1. Select a OneLake shortcut instead of database mirroring.
  2. Run the logic in a Fabric PySpark notebook.
  3. Use summarize max(timestamp) by device_id.
  4. Explicitly cast and parse the fields in PySpark.

Explanation

Correct Answer

B:

Notebooks support custom distributed transformations and environments.

Incorrect Answers

A:

Shortcuts virtualize object access while mirroring replicates changes.

C:

Group-wise max returns each device's latest time.

D:

Typed transforms avoid storing measures as text.

Question 230

In a Microsoft Fabric solution, a pipeline writes new inventory records in batches but a failure can happen between writing data and advancing its watermark. Which configuration or implementation should the engineer choose?

  1. Use a timestamp watermark and MERGE changed records by stable customer key.
  2. Use event-time processing with a configured lateness or watermark policy.
  3. Write the batch with stable keys using idempotent MERGE semantics.
  4. Commit the watermark only after the target batch has completed successfully.

Explanation

Correct Answer

D:

Separating checkpoint advancement from incomplete writes prevents silent data loss.

Incorrect Answers

A:

Timestamp-based detection with key-based merge handles updates as well as inserts.

B:

Event-time watermarks allow controlled late-arriving contributions before state is finalized.

C:

Keyed merge can safely reprocess the same source changes.