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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 12

Question 110

Dense device telemetry must support near-real-time filtering and KQL investigation. Which action best addresses the requirement?

  1. Land the events in an Eventhouse KQL database.
  2. Use summarize max(timestamp) by device_id.
  3. Configure a supported mirrored database.
  4. Select a OneLake shortcut instead of database mirroring.

Explanation

Correct Answer

A:

Eventhouse supports high-throughput time-series ingestion and KQL.

Incorrect Answers

B:

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

C:

Mirroring propagates ongoing source changes into analytical tables.

D:

Shortcuts virtualize object access while mirroring replicates changes.

Question 111

In a Microsoft Fabric solution, a pipeline queue grows during peak hours even though downstream jobs individually complete quickly. Which configuration or implementation should the engineer choose?

  1. Use notebook run history and Spark stage execution diagnostics.
  2. Compare queued and execution durations in pipeline run monitoring.
  3. Aggregate pipeline run outcomes by date and failure type.
  4. Add data-quality reconciliation metrics to the monitoring gate.

Explanation

Correct Answer

B:

Queue time and actual processing time represent different performance causes.

Incorrect Answers

A:

Stage-level timing isolates expensive transformations within a notebook.

C:

Trend monitoring reveals recurrent errors not visible from a single run.

D:

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

Question 112

Notebook startup times are high when users run lightweight concurrent transformations, but identities and Spark setups already match. Which action best addresses the requirement?

  1. Select the intended pool configuration in the notebook's environment Compute settings.
  2. Publish a Fabric environment with the common library and attach it to the notebooks.
  3. Select an existing high-concurrency session from the notebook Run session picker.
  4. Publish the environment and restart the notebook's Spark session to pick up changes.

Explanation

Correct Answer

C:

Attaching to a compatible active session avoids starting a fresh Spark session.

Incorrect Answers

A:

Environment compute configuration can specify pool resources when workspace overrides are allowed.

B:

Environment items centrally define reusable libraries and Spark runtime settings.

D:

Environment edits must be published and a new session must load the updated settings.

Question 113

Region and transaction date are the most selective repeated filters on a Delta fact. Which engineering decision is most suitable for this specific situation?

  1. Choose these frequent predicate keys for liquid clustering.
  2. Push selective filters before the expensive join.
  3. Push WHERE and aggregate logic into the Warehouse SQL query.
  4. Use a predicate on the physical partition column and preserve pruning.

Explanation

Correct Answer

A:

Cluster-column selection should reflect query workload.

Incorrect Answers

B:

Earlier predicates reduce processed rows.

C:

Server-side filtering reduces network and client load.

D:

An eligible filter allows partition pruning.

Question 114

In a Microsoft Fabric solution, a semantic model that serves reports appears stale despite yesterday's scheduled refresh. Which configuration or implementation should the engineer choose?

  1. Check its refresh history and reported completion or error details.
  2. Inspect the dataflow refresh history and detailed step errors.
  3. Check Eventhouse ingestion freshness and the dashboard query time range separately.
  4. Use notebook run history and Spark stage execution diagnostics.

Explanation

Correct Answer

A:

Semantic model refresh monitoring distinguishes successful updates from attempted refreshes.

Incorrect Answers

B:

Refresh history identifies dataflow execution status and transformation failures.

C:

A stale visualization may result from query filtering rather than ingestion failure.

D:

Stage-level timing isolates expensive transformations within a notebook.

Question 115

A KQL query needs error totals for each application and each minute. Which action best addresses the requirement?

  1. Use a hopping window with ten-minute size and two-minute hop.
  2. Set the query acceleration policy's cache period to seven days.
  3. Base time-series grouping on the business event timestamp.
  4. Use summarize count() by application and bin(timestamp, 1m).

Explanation

Correct Answer

D:

KQL bin defines discrete time buckets for grouping.

Incorrect Answers

A:

Hopping windows overlap to give rolling aggregates at regular steps.

B:

The policy controls a window of eligible external Delta data relative to modification time.

C:

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

Question 116

A Real-Time dashboard is displaying yesterday's readings even though its underlying event source is active. Which action best addresses the requirement?

  1. Inspect Eventhouse ingestion and database monitoring metrics.
  2. Configure an appropriate Fabric real-time alert through the supported alert experience.
  3. Read refresh error details rather than treating attempted refresh time as success.
  4. Check Eventhouse ingestion freshness and the dashboard query time range separately.

Explanation

Correct Answer

D:

A stale visualization may result from query filtering rather than ingestion failure.

Incorrect Answers

A:

Ingestion monitoring distinguishes source outages from slow arrival or indexing.

B:

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

C:

Refresh attempts and successful completion are separate events.

Question 117

A Dataflow Gen2 refresh fails to authenticate to its on-premises source. What should the engineer implement first to satisfy the stated need?

  1. Renew the shortcut's external connection credentials without recreating its logical target.
  2. Handle conversion errors explicitly and route malformed values for review.
  3. Refresh the source connection's authorized credentials and rerun the Copy activity.
  4. Repair the dataflow connection and gateway credentials for the authorized data source.

Explanation

Correct Answer

D:

Credential failures in Power Query prevent refresh even when destination permissions are correct.

Incorrect Answers

A:

A shortcut depends on valid credentials to access external objects.

B:

Power Query type conversion can fail when source values violate the expected type.

C:

Authentication failure occurs before data transfer when credentials are no longer valid.

Question 118

Raw lakehouse Files JSON needs to become structured queryable data. Which engineering decision is most suitable for this specific situation?

  1. Use Delta transactional writes and retry genuine write conflicts appropriately.
  2. Write T-SQL GROUP BY country with SUM(revenue).
  3. Create a Tables shortcut to the source Delta table.
  4. Parse files and write them to registered Delta tables.

Explanation

Correct Answer

D:

Files storage is separate from registered lakehouse tables.

Incorrect Answers

A:

Delta transaction logs protect supported concurrent operations.

B:

SQL aggregation forms one group per country.

C:

A table shortcut exposes the referenced Delta data for queries.

Question 119

A Fabric workspace currently tracks a feature branch but the team must synchronize only the mainline release branch. Which engineering decision is most suitable for this specific situation?

  1. Switch the workspace Git connection to the intended release branch with the relevant permissions.
  2. Use environment-specific deployment configuration while keeping one versioned database project.
  3. Retain the external shortcut target when promoting the lakehouse definition.
  4. Deploy the notebook and supported dependent environment together to preserve auto-binding.

Explanation

Correct Answer

A:

A connected workspace follows a configured repository and branch rather than every branch automatically.

Incorrect Answers

B:

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

C:

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

D:

Notebook deployment can rebind attached environments and lakehouses when dependencies exist in the stage.

Question 120

A project requires live storage references but no physical replication into Fabric. What should the engineer implement first to satisfy the stated need?

  1. Land the events in an Eventhouse KQL database.
  2. Run the logic in a Fabric PySpark notebook.
  3. Select a OneLake shortcut instead of database mirroring.
  4. Configure a supported mirrored database.

Explanation

Correct Answer

C:

Shortcuts virtualize object access while mirroring replicates changes.

Incorrect Answers

A:

Eventhouse supports high-throughput time-series ingestion and KQL.

B:

Notebooks support custom distributed transformations and environments.

D:

Mirroring propagates ongoing source changes into analytical tables.