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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 30

Question 290

Spark jobs are short on executor memory for their actual data volume. Which engineering decision is most suitable for this specific situation?

  1. Right-size executor resources in the attached environment.
  2. Use a skew-aware join plan or carefully designed salting.
  3. Use a suitable broadcast join.
  4. Tune shuffle partition count to match volume and resources.

Explanation

Correct Answer

A:

Sufficient compatible executor memory can reduce spills and failures.

Incorrect Answers

B:

Redistribution prevents one worker from becoming a straggler.

C:

Broadcasting small lookup data can avoid shuffling the large fact.

D:

Excess tiny tasks add scheduling overhead.

Question 291

Product price is missing on some ingested records and those defects need traceability. Which action best addresses the requirement?

  1. Create an external Amazon S3 shortcut with a valid connection.
  2. Join facts to reference rows by stable product key.
  3. Filter categories in Power Query before writing to the curated destination.
  4. Add a missing-value quality flag and quarantine affected rows.

Explanation

Correct Answer

D:

Explicit null handling preserves evidence without contaminating valid measures.

Incorrect Answers

A:

The shortcut maps cloud objects into OneLake without staging copies.

B:

Enrichment joins add trusted attributes from matching keys.

C:

A source-side logical filter prevents irrelevant rows reaching the output.

Question 292

In a Microsoft Fabric solution, tiny Delta files accumulate after microbatch writes and make lakehouse scans slow. Which configuration or implementation should the engineer choose?

  1. Use a suitable broadcast join.
  2. Run Delta OPTIMIZE compaction.
  3. Run VACUUM with a safe retention policy.
  4. Run OPTIMIZE to apply the changed clustering definition.

Explanation

Correct Answer

B:

Compaction combines small files into more efficient files.

Incorrect Answers

A:

Broadcasting small lookup data can avoid shuffling the large fact.

C:

VACUUM removes eligible unreferenced files.

D:

Changing metadata alone does not physically reorganize existing files.

Question 293

A team runs Apache Airflow DAGs only during occasional Fabric development sessions, and the workspace must avoid charges from a continuously running production pool while idle. Which single implementation most directly meets the stated requirement?

  1. Select the intended pool configuration in the notebook's environment Compute settings.
  2. Set the chosen published environment as the workspace default environment.
  3. Set spark.highConcurrency.max to 20 in the attached environment's Spark properties.
  4. Use the default Starter Apache Airflow pool for the intermittent development job.

Explanation

Correct Answer

D:

The Starter pool is suitable for intermittent jobs because it automatically deprovisions after an idle interval; Custom pools remain provisioned for sustained production usage. The direct validation would be: The development Airflow job starts when needed and its Starter pool is automatically shut down after the supported idle period.

Incorrect Answers

A:

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

B:

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

C:

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

Question 294

In a Microsoft Fabric solution, a business requires a human to inspect malformed records before a load can advance to a downstream quality gate. Which configuration or implementation should the engineer choose?

  1. Build a Fabric data pipeline with Copy and Notebook activities and failure handling.
  2. Separate validation and downstream load activities with a deliberate control condition.
  3. Create a time-zone-aware recurring schedule for the Fabric pipeline.
  4. Use a metadata inspection activity and If Condition branching.

Explanation

Correct Answer

B:

An explicit validation dependency prevents data promotion when quality fails.

Incorrect Answers

A:

Pipelines orchestrate multi-activity ingestion and dependent jobs.

C:

A schedule triggers runs based on configured dates, frequency, and time zone.

D:

Conditional pipeline control chooses different activities based on source state.

Question 295

A pipeline reports that all Copy activities completed, but finance totals in the target differ from source totals. What should the engineer implement first to satisfy the stated need?

  1. Inspect Copy activity input/output metrics and reconcile source and sink counts.
  2. Compare queued and execution durations in pipeline run monitoring.
  3. Inspect Eventhouse ingestion and database monitoring metrics.
  4. Add data-quality reconciliation metrics to the monitoring gate.

Explanation

Correct Answer

D:

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

Incorrect Answers

A:

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

B:

Queue time and actual processing time represent different performance causes.

C:

Ingestion monitoring distinguishes source outages from slow arrival or indexing.

Question 296

Copy throughput worsens after too many parallel requests hit an API rate limit. What should the engineer implement first to satisfy the stated need?

  1. Tune supported ingestion batching policy for the feed.
  2. Coalesce appropriately before writing.
  3. Use high-concurrency Spark sessions for compatible workloads.
  4. Lower source concurrency to a sustainable rate.

Explanation

Correct Answer

D:

Excess parallelism causes throttling and retries.

Incorrect Answers

A:

Suitable batching balances ingest overhead with freshness.

B:

Reducing output partitions prevents excessive small files.

C:

Session reuse reduces startup overhead.

Question 297

In a Microsoft Fabric solution, a folder of arbitrary CSVs should remain accessible as files, not be misregistered as Delta. Which configuration or implementation should the engineer choose?

  1. Explicitly cast and parse the fields in PySpark.
  2. Parse files and write them to registered Delta tables.
  3. Create a shortcut in the lakehouse Files section.
  4. Create a pipeline Copy activity into the destination.

Explanation

Correct Answer

C:

Files shortcuts expose arbitrary formats without automatic table registration.

Incorrect Answers

A:

Typed transforms avoid storing measures as text.

B:

Files storage is separate from registered lakehouse tables.

D:

Copy handles scalable source-to-target data movement.

Question 298

An Apache Airflow job in a production Fabric workspace runs frequent business-critical DAGs and must avoid the automatic idle shutdown of the default Starter pool. Which single implementation most directly meets the stated requirement?

  1. Select the intended pool configuration in the notebook's environment Compute settings.
  2. Enable diagnostic events in workspace OneLake settings and select the approved diagnostic lakehouse.
  3. Enable high-concurrency mode for pipeline-running notebooks in workspace Spark settings.
  4. Create and assign a Custom Apache Airflow pool through the workspace Data Factory Apache Airflow Runtime Settings.

Explanation

Correct Answer

D:

Custom Airflow pools remain provisioned and support sizing, additional nodes, and autoscale; a Starter pool automatically shuts down after an idle period. The direct validation would be: The production DAG uses the configured Custom pool, which remains running through an idle period without being automatically shut down as a Starter pool.

Incorrect Answers

A:

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

B:

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

C:

The pipeline-specific setting permits compatible notebook activities invoked by pipelines to share a Spark session.

Question 299

In a Microsoft Fabric solution, several terabytes must be moved from a supported source with little transformation. Which configuration or implementation should the engineer choose?

  1. Use controlled parallel Copy branches or ForEach concurrency.
  2. Create a Tables shortcut to the source Delta table.
  3. Create a pipeline Copy activity into the destination.
  4. Enable Fast Copy when compatible with source and destination.

Explanation

Correct Answer

C:

Copy handles scalable source-to-target data movement.

Incorrect Answers

A:

Independent loads can overlap when source and sink throughput permit.

B:

A table shortcut exposes the referenced Delta data for queries.

D:

Fast Copy parallelizes eligible movements.

Question 300

A warehouse release has different connection-dependent settings between Test and Production, but the same T-SQL schema objects. Which engineering decision is most suitable for this specific situation?

  1. Commit the environment configuration and notebook changes together when both are supported.
  2. Use environment-specific deployment configuration while keeping one versioned database project.
  3. Configure a Fabric deployment pipeline with stages assigned to the three workspaces.
  4. Use deployment pipeline dependency mapping for internal shortcut references.

Explanation

Correct Answer

B:

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

Incorrect Answers

A:

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

C:

Deployment pipelines promote supported Fabric items across staged workspaces.

D:

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