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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 13

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.

Question 121

An engineer deploys a notebook with a Git reference to a shared library but forgets to commit the attached environment changes. What should the engineer implement first to satisfy the stated need?

  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. Commit the environment configuration and notebook changes together when both are supported.
  4. Commit or discard intentional local differences before applying the remote update.

Explanation

Correct Answer

C:

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

Incorrect Answers

A:

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

B:

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

D:

Resolving local divergence before Git update prevents accidental overwrite.

Question 122

In a Microsoft Fabric solution, a shortcut's acceleration caches years of data although queries inspect only recent days. Which configuration or implementation should the engineer choose?

  1. Tune supported caching and ingestion settings around the hot window.
  2. Reduce acceleration cache period to the needed window.
  3. Reduce the filtered set before a necessary global sort.
  4. Tune supported ingestion batching policy for the feed.

Explanation

Correct Answer

B:

Cache policy limits how much external history is accelerated.

Incorrect Answers

A:

Keeping active data efficient reduces cold reads.

C:

Global sorting can trigger non-scalable processing.

D:

Suitable batching balances ingest overhead with freshness.

Question 123

A KQL shortcut only needs to cache recent changes for seven days, not the full history. Which engineering decision is most suitable for this specific situation?

  1. Define a KQL update policy to transform ingestion into a target native table.
  2. Set the query acceleration policy's cache period to seven days.
  3. Use summarize count() by application and bin(timestamp, 1m).
  4. Apply an event-time watermark that covers the accepted lateness policy.

Explanation

Correct Answer

B:

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

Incorrect Answers

A:

Update policies can transform or route data at ingestion to suitable native destinations.

C:

KQL bin defines discrete time buckets for grouping.

D:

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

Question 124

Repeated KQL dashboards compute the same grouped summary over native Eventhouse tables. What should the engineer implement first to satisfy the stated need?

  1. Tune supported ingestion batching policy for the feed.
  2. Schedule safe table maintenance respecting retention and rollback.
  3. Create an appropriate materialized view on the native table.
  4. Choose these frequent predicate keys for liquid clustering.

Explanation

Correct Answer

C:

Precomputed aggregates reduce repeated full computations.

Incorrect Answers

A:

Suitable batching balances ingest overhead with freshness.

B:

Cleanup reclaims eligible old storage without corrupting retained versions.

D:

Cluster-column selection should reflect query workload.

Question 125

A batch ingest should process every file name returned by a preceding metadata query rather than hard-code ten copies. Which engineering decision is most suitable for this specific situation?

  1. Use Lookup followed by ForEach with each item passed to the child activity.
  2. Set bounded retry settings and a retry interval for the Copy activity.
  3. Use a metadata inspection activity and If Condition branching.
  4. Use null-safe optional chaining when referencing event-only trigger fields.

Explanation

Correct Answer

A:

ForEach iterates over a list generated by an upstream activity.

Incorrect Answers

B:

Retries can recover transient throttling without resubmitting the entire pipeline manually.

C:

Conditional pipeline control chooses different activities based on source state.

D:

Manual invocations may lack TriggerEvent values, so defensive expressions avoid failures during testing.

Question 126

A KQL query has become slow and support needs to compare execution characteristics across revisions. What should the engineer implement first to satisfy the stated need?

  1. Open the pipeline run in the Fabric monitoring experience and inspect its activity runs.
  2. Inspect Eventhouse ingestion and database monitoring metrics.
  3. Inspect notebook run diagnostics and Spark driver/executor logs.
  4. Inspect KQL query diagnostics or query statistics for representative runs.

Explanation

Correct Answer

D:

Query metrics show performance beyond final row counts.

Incorrect Answers

A:

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

B:

Ingestion monitoring distinguishes source outages from slow arrival or indexing.

C:

Spark diagnostic logs expose runtime failures beyond the visible notebook status.

Question 127

A time-critical file flow must start when a matching path receives an event, rather than waste compute polling for new objects. Which engineering decision is most suitable for this specific situation?

  1. Configure a storage-event trigger with its subject path filter.
  2. Configure failure notifications on the scheduled pipeline run.
  3. Use null-safe optional chaining when referencing event-only trigger fields.
  4. Define pipeline parameters and reference them with dynamic content expressions.

Explanation

Correct Answer

A:

An event trigger reacts to supported storage events without repeated scan jobs.

Incorrect Answers

B:

Scheduled pipelines support configured failure notifications distinct from manual invocation.

C:

Manual invocations may lack TriggerEvent values, so defensive expressions avoid failures during testing.

D:

Parameters make the same pipeline definition reusable with different runtime values.

Question 128

A Spark DataFrame repeats event_id after redelivery and should keep one key instance. Which engineering decision is most suitable for this specific situation?

  1. Repair the external connection and source-side access rights.
  2. Run the logic in a Fabric PySpark notebook.
  3. Choose a Fabric lakehouse for file-oriented analytical storage.
  4. Use dropDuplicates on the stable event_id column.

Explanation

Correct Answer

D:

Keyed DataFrame deduplication eliminates repeated identifiers.

Incorrect Answers

A:

Shortcut metadata visibility does not grant source storage authorization.

B:

Notebooks support custom distributed transformations and environments.

C:

Lakehouses combine OneLake files, Spark Delta tables, and a SQL endpoint.

Question 129

A Dataflow Gen2 refresh fails only when its on-premises gateway is offline. Which action best addresses the requirement?

  1. Restore gateway availability or select a valid supported gateway connection.
  2. Reference a valid supported Delta table in Tables or use Files for arbitrary formats.
  3. Correct the T-SQL syntax using the Warehouse-supported grammar.
  4. Renew the supported source connection authentication and confirm the stream is running.

Explanation

Correct Answer

A:

A gateway-dependent source requires an operational gateway path.

Incorrect Answers

B:

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

C:

Invalid SQL grammar fails during parsing before meaningful execution.

D:

Expired credentials break source ingestion before downstream transformations execute.

Question 130

The same expensive Spark DataFrame is recomputed repeatedly by different downstream actions. What should the engineer implement first to satisfy the stated need?

  1. Cache or persist the reusable intermediate only when justified.
  2. Reduce acceleration cache period to the needed window.
  3. Filter by device and recent timestamp before heavy parsing.
  4. Inspect an estimated execution plan with supported SHOWPLAN tooling.

Explanation

Correct Answer

A:

Reuse can avoid repeated upstream transformations.

Incorrect Answers

B:

Cache policy limits how much external history is accelerated.

C:

Early KQL filtering reduces rows processed by expensive functions.

D:

Estimated plans show likely operators before full execution.