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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 6

Question 50

A notebook's overall duration rises but it is unclear which cell or Spark stage is responsible. Which action best addresses the requirement?

  1. Inspect the dataflow refresh history and detailed step errors.
  2. Use notebook run history and Spark stage execution diagnostics.
  3. Read driver and executor logs from an unrelated notebook rather than the slow job’s individual stage timings.
  4. Compare queued and execution durations in pipeline run monitoring.

Explanation

Correct Answer

B:

Stage-level timing isolates expensive transformations within a notebook.

Incorrect Answers

A:

Refresh history identifies dataflow execution status and transformation failures.

C:

Unrelated notebook logs cannot establish which cell or Spark stage is lengthening this run. The keyed method inspects this notebook’s run history and per-stage execution diagnostics.

D:

Queue time and actual processing time represent different performance causes.

Question 51

Nested JSON adds a new field but old curated columns must remain aligned. Which engineering decision is most suitable for this specific situation?

  1. Introduce controlled schema evolution in the notebook parsing path.
  2. Use PySpark unionByName with controlled missing-field handling.
  3. Use controlled parallel Copy branches or ForEach concurrency.
  4. Specify an explicit schema and bad-date handling for ingest.

Explanation

Correct Answer

A:

Explicit evolution maps new attributes without misplacing old values.

Incorrect Answers

B:

Name-based union aligns columns without relying on position.

C:

Independent loads can overlap when source and sink throughput permit.

D:

Explicit parsing controls data types and malformed values.

Question 52

A warehouse load must handle null customer identifiers instead of allowing unrelated rows to collapse under one blank key. Which engineering decision is most suitable for this specific situation?

  1. Apply delete change events to the target using the source's business keys.
  2. Recompute or adjust affected aggregates using the changed underlying business keys.
  3. Backfill only the missing business-date partition with idempotent processing.
  4. Route rows with missing mandatory business keys to a quarantine table.

Explanation

Correct Answer

D:

Quarantine preserves invalid input for review without corrupting deduplication.

Incorrect Answers

A:

Delete propagation is needed in addition to insert/update loading when the target mirrors current state.

B:

Corrections must flow into derived measures, not only the detail table.

C:

A targeted backfill repairs the gap without unnecessarily reloading unrelated dates.

Question 53

A third-party API throttles extraction requests with 429 responses during bulk ingestion. Which engineering decision is most suitable for this specific situation?

  1. Remove unnecessary collect() and use distributed transformations or bounded sampling.
  2. Refresh the source connection's authorized credentials and rerun the Copy activity.
  3. Reduce request concurrency or add bounded backoff retries to the source Copy logic.
  4. Correct the Eventhouse ingestion mapping to the intended timestamp column and type.

Explanation

Correct Answer

C:

Rate limiting requires pacing and retriable handling rather than unlimited parallel calls.

Incorrect Answers

A:

Moving all large data to the driver can exhaust memory and defeat distributed processing.

B:

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

D:

Ingestion mapping aligns payload paths with typed target fields.

Question 54

In a Microsoft Fabric solution, a notebook and its dependent environment are both deployed to another workspace using a Fabric deployment pipeline. Which configuration or implementation should the engineer choose?

  1. Deploy the notebook and supported dependent environment together to preserve auto-binding.
  2. Use environment-specific deployment configuration while keeping one versioned database project.
  3. Review the Git changes and differences for the affected supported Fabric items.
  4. Commit the environment configuration and notebook changes together when both are supported.

Explanation

Correct Answer

A:

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

Incorrect Answers

B:

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

C:

Git comparison exposes modified definitions prior to persistence.

D:

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

Question 55

An auditor finds OneLake role changes performed by multiple admins without a record of who changed access. Which engineering decision is most suitable for this specific situation?

  1. Configure a partial mask tailored to the supported string column.
  2. Check external collaboration and guest-access configuration in Microsoft Entra.
  3. Assign a managed Microsoft Entra group to the OneLake security role.
  4. Review supported audit events for role and permission changes.

Explanation

Correct Answer

D:

Audit logs provide available governance activity history for investigation.

Incorrect Answers

A:

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

B:

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

C:

OneLake security resolves supported Entra group membership for role grants.

Question 56

In a Microsoft Fabric solution, a customer master must return IDs absent from a transactions DataFrame. Which configuration or implementation should the engineer choose?

  1. Use GROUP BY customer with HAVING COUNT condition.
  2. Use a PySpark left anti join on customer ID.
  3. Run the logic in a Fabric PySpark notebook.
  4. Apply Power Query Merge on the selected customer key.

Explanation

Correct Answer

B:

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

Incorrect Answers

A:

HAVING filters groups after aggregation.

C:

Notebooks support custom distributed transformations and environments.

D:

Merge combines records according to its join condition.

Question 57

A notebook's relative lakehouse path fails after deploying to another workspace. What should the engineer implement first to satisfy the stated need?

  1. Reference a valid supported Delta table in Tables or use Files for arbitrary formats.
  2. Inspect evaluated dynamic expressions and correct the runtime source path.
  3. Attach and publish the environment containing the required compatible library.
  4. Rebind the notebook to the intended target-stage default lakehouse and resolve the path.

Explanation

Correct Answer

D:

Notebook references depend on an effective lakehouse binding in each environment.

Incorrect Answers

A:

Tables shortcuts require supported tabular data; Files shortcuts can expose non-Delta files.

B:

A wrong path can fail even when the connection itself is valid.

C:

Notebook sessions resolve available libraries from their effective environment.

Question 58

Repeated analytics queries aggregate millions of native KQL rows by device and day. Which action best addresses the requirement?

  1. Create an Eventstream route into an Eventhouse KQL table.
  2. Create a suitable KQL materialized view for the frequently reused aggregation.
  3. Ingest events into an Eventhouse native table instead of querying only an external shortcut.
  4. Use deduplication keyed on event ID within a defined streaming state policy.

Explanation

Correct Answer

B:

Materialized views maintain precomputed results for supported KQL aggregation patterns.

Incorrect Answers

A:

Eventstream can ingest and route streaming data to a KQL destination.

C:

Native Eventhouse ingestion supports indexed real-time analytics over event records.

D:

Keyed streaming deduplication limits duplicate business contributions.

Question 59

A teammate merged a new notebook definition into the repository, but the linked Fabric development workspace still shows the older version. Which engineering decision is most suitable for this specific situation?

  1. Update the Git-connected workspace from the repository branch.
  2. Use deployment pipeline dependency mapping for internal shortcut references.
  3. Deploy the reviewed database project to the target warehouse.
  4. Configure an appropriate notebook deployment rule on the target stage.

Explanation

Correct Answer

A:

Git update applies incoming committed definitions to the connected workspace.

Incorrect Answers

B:

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

C:

A database project deployment applies the versioned SQL schema to the destination.

D:

Target-stage deployment rules can override notebook bindings such as the default lakehouse.

Question 60

A lakehouse ingestion process must wait for both independent dimension-load branches before updating facts. Which action best addresses the requirement?

  1. Configure the fact load with dependencies on both dimension-load completion paths.
  2. Configure an event-based trigger filtered to the required source path and file type.
  3. Create two schedules on the pipeline with their respective parameter values.
  4. Place both notebook activities on parallel branches after the staging success dependency.

Explanation

Correct Answer

A:

A join dependency ensures all required upstream activity results exist first.

Incorrect Answers

B:

Storage event triggers respond to events rather than a fixed clock schedule.

C:

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

D:

Parallel branches enable independent work once common prerequisites are met.