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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 19

Question 180

In a Microsoft Fabric solution, a pipeline is called for multiple countries and needs its source path to vary without editing its JSON each time. Which configuration or implementation should the engineer choose?

  1. Configure failure notifications on the scheduled pipeline run.
  2. Define pipeline parameters and reference them with dynamic content expressions.
  3. Create two schedules on the pipeline with their respective parameter values.
  4. Reference the current loop item through the pipeline expression builder.

Explanation

Correct Answer

B:

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

Incorrect Answers

A:

Scheduled pipelines support configured failure notifications distinct from manual invocation.

C:

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

D:

ForEach item expressions bind an activity's dynamic value to the current iteration.

Question 181

An Eventhouse receives many tiny ingestion batches and throughput is unstable. Which engineering decision is most suitable for this specific situation?

  1. Tune supported ingestion batching policy for the feed.
  2. Tune supported caching and ingestion settings around the hot window.
  3. Tune shuffle partition count to match volume and resources.
  4. Configure supported partitioned source reads and controlled parallelism.

Explanation

Correct Answer

A:

Suitable batching balances ingest overhead with freshness.

Incorrect Answers

B:

Keeping active data efficient reduces cold reads.

C:

Excess tiny tasks add scheduling overhead.

D:

Parallel partition reads can improve throughput if the source supports them.

Question 182

Several eligible notebooks waste time creating separate Spark sessions. Which engineering decision is most suitable for this specific situation?

  1. Move aggregation into distributed Spark expressions.
  2. Use high-concurrency Spark sessions for compatible workloads.
  3. Lower source concurrency to a sustainable rate.
  4. Assign a smaller compatible environment or pool size.

Explanation

Correct Answer

B:

Session reuse reduces startup overhead. The result addresses the requirement stated in this case.

Incorrect Answers

A:

Distributed aggregation avoids huge driver memory use.

C:

Excess parallelism causes throttling and retries.

D:

Right-sizing reduces unused capacity while retaining required latency.

Question 183

In a Microsoft Fabric solution, a Viewer with a lakehouse ReadAll item permission sees tables even though no custom OneLake role was created by an administrator. Which configuration or implementation should the engineer choose?

  1. Maintain the specific item share rather than granting workspace Contributor.
  2. Reduce broad workspace access and rely on narrowly scoped item and data roles.
  3. Inspect the item's default reader security role before adding more grants.
  4. Review supported audit events for role and permission changes.

Explanation

Correct Answer

C:

Fabric can create a default reader role for users with ReadAll, so effective data access may come from defaults.

Incorrect Answers

A:

Item access is narrower than workspace roles and avoids unnecessary creation and modification rights.

B:

Workspace access can confer item-wide rights that undermine fine-grained data access goals.

D:

Audit logs provide available governance activity history for investigation.

Question 184

In a Microsoft Fabric solution, a team reruns manual ZORDER commands whenever the table's preferred filter columns evolve. Which configuration or implementation should the engineer choose?

  1. Adopt supported declarative liquid clustering.
  2. Reorder supported steps to preserve query folding.
  3. Push the filter before the groupBy shuffle.
  4. Enable supported query acceleration.

Explanation

Correct Answer

A:

Liquid clustering persists chosen columns in table metadata.

Incorrect Answers

B:

Folding pushes selective work into the source.

C:

Earlier row reduction lowers shuffle volume.

D:

Acceleration caches eligible external data for faster KQL queries.

Question 185

In a Microsoft Fabric solution, a high-volume stream should be persisted for real-time analytics rather than continuously reread remote Parquet files. Which configuration or implementation should the engineer choose?

  1. Record both event timestamp and ingestion timestamp and compare their lag.
  2. Choose Eventhouse native ingestion for the hot telemetry layer.
  3. Define a KQL update policy to transform ingestion into a target native table.
  4. Create an Eventstream route into an Eventhouse KQL table.

Explanation

Correct Answer

B:

Native real-time tables avoid the remote-file limitations of an external-only shortcut.

Incorrect Answers

A:

Tracking both times reveals transport delay independently of source behavior.

C:

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

D:

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

Question 186

Administrators want to stop all workspace notebooks from selecting arbitrary per-item Spark sizes during a cost-control period. Which action best addresses the requirement?

  1. Set the chosen published environment as the workspace default environment.
  2. Review the workspace's current domain assignment in the Fabric domain settings.
  3. Disable item-level compute customization in the workspace Spark settings.
  4. Enable notebook high-concurrency mode in the workspace Spark settings.

Explanation

Correct Answer

C:

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

Incorrect Answers

A:

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

B:

Inspecting the existing association identifies the correct scope before reassigning governance ownership.

D:

Compatible notebooks can attach to a running high-concurrency Spark session instead of starting separate sessions.

Question 187

The source is an authorized S3 bucket and files should be referenced in a lakehouse. Which engineering decision is most suitable for this specific situation?

  1. Partition data at a sensible date granularity for typical filters.
  2. Parse files and write them to registered Delta tables.
  3. Create an external Amazon S3 shortcut with a valid connection.
  4. Create a pipeline Copy activity into the destination.

Explanation

Correct Answer

C:

The shortcut maps cloud objects into OneLake without staging copies.

Incorrect Answers

A:

Appropriate partitioning enables pruning of irrelevant data files.

B:

Files storage is separate from registered lakehouse tables.

D:

Copy handles scalable source-to-target data movement.

Question 188

A department requests a one-time replay of a missed calendar day after a loader outage. Which action best addresses the requirement?

  1. Backfill only the missing business-date partition with idempotent processing.
  2. Re-read a bounded overlap window and deduplicate or merge by business key.
  3. Route rows with missing mandatory business keys to a quarantine table.
  4. Partition and aggregate by business event date, while tracking arrival time separately.

Explanation

Correct Answer

A:

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

Incorrect Answers

B:

An overlap interval guards against delayed visibility without duplicating accepted data.

C:

Quarantine preserves invalid input for review without corrupting deduplication.

D:

Event-time classification prevents late ingestion from shifting historical business totals.

Question 189

Monthly DataFrames have the same field names in different column order. What should the engineer implement first to satisfy the stated need?

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

Explanation

Correct Answer

C:

Name-based union aligns columns without relying on position.

Incorrect Answers

A:

Explicit parsing controls data types and malformed values.

B:

Independent loads can overlap when source and sink throughput permit.

D:

Explicit evolution maps new attributes without misplacing old values.

Question 190

A source adds nullable attributes during a staged rollout, and the target must retain old records while adopting the new fields. Which engineering decision is most suitable for this specific situation?

  1. Implement slowly changing dimension Type 2 versioned records with effective dates.
  2. Rebuild the current-state target from each complete snapshot with controlled overwrite.
  3. Use a timestamp watermark and MERGE changed records by stable customer key.
  4. Use a controlled schema-evolution load with explicit null defaults for older records.

Explanation

Correct Answer

D:

Schema evolution allows new fields without misaligning established rows.

Incorrect Answers

A:

Type 2 preserves previous attribute values by storing dated versions.

B:

A replacement snapshot represents the new complete state rather than append-only changes.

C:

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