McAfee-Secured Website

Exam Bundle

Exam Code: DP-700

Exam Name Implementing Data Engineering Solutions Using Microsoft Fabric

Certification Provider: Microsoft

Corresponding Certification: Microsoft Certified: Fabric Data Engineer Associate

Microsoft DP-700 Bundle $25.00
DP-700 Sample 1 DP-700 Sample 2 DP-700 Sample 3 DP-700 Sample 4 DP-700 Sample 5 DP-700 Sample 6 DP-700 Sample 7 DP-700 Sample 8 DP-700 Sample 9 DP-700 Sample 10

100% Microsoft DP-700 Guaranteed Success With Testking Training Materials

Get Real DP-700 Exam Questions & Expert Verified Answers as Experienced on the Real Exam!

  • Questions & Answers

    DP-700 Questions & Answers

    160 Questions & Answers

    The ultimate exam preparation tool, Questions & Answers mirrors the actual DP-700 exam while allowing you to hone and measure every aspect of your test-taking ability.

  • DP-700 Video Course

    DP-700 Video Course

    160 Video Lectures

    DP-700 Video Course is developed by Microsoft Professionals to help you pass the DP-700 exam.

    Description

    This course will improve your knowledge and skills required to pass Implementing Data Engineering Solutions Using Microsoft Fabric exam.

Implementing Data Engineering Solutions Using Microsoft Fabric (DP-700) Practice Tests

Satisfaction Guaranteed

Money Back Guarantee

Testking provides hassle-free money back guarantee with our products. That is because we have 100% trust in the abilities of our professional and experience product team, and our record is a proof of that.

99.6% PASS RATE
Total Cost: $164.98
Bundle Price: $139.98

Purchase Individually

  • Questions & Answers

    Questions & Answers

    160 Questions

    $124.99
  • DP-700 Video Course

    Video Course

    160 Video Lectures

    $39.99

Microsoft DP-700 Practice Test 7

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.

Question 61

In a Microsoft Fabric solution, warehouse queries should return only customers with more than three completed orders. Which configuration or implementation should the engineer choose?

  1. Apply Power Query Merge on the selected customer key.
  2. Use GROUP BY customer with HAVING COUNT condition.
  3. Use a PySpark left anti join on customer ID.
  4. Use KQL summarize count() by service and bin(timestamp, 5m).

Explanation

Correct Answer

B:

HAVING filters groups after aggregation. The result addresses the requirement stated in this case.

Incorrect Answers

A:

Merge combines records according to its join condition.

C:

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

D:

Summarize and bin produce grouped time buckets.

Question 62

A pipeline must perform a cleanup notebook only after the Copy activity succeeds, and otherwise run compensation logic. What should the engineer implement first to satisfy the stated need?

  1. Configure failure notifications on the scheduled pipeline run.
  2. Set bounded retry settings and a retry interval for the Copy activity.
  3. Build a Fabric data pipeline with Copy and Notebook activities and failure handling.
  4. Configure success and failure activity dependencies for the downstream branches.

Explanation

Correct Answer

D:

Activity dependency conditions explicitly control which path runs after an outcome.

Incorrect Answers

A:

Scheduled pipelines support configured failure notifications distinct from manual invocation.

B:

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

C:

Pipelines orchestrate multi-activity ingestion and dependent jobs.

Question 63

Two otherwise compatible interactive notebooks refuse to share a session after a team adds a different package to one published environment. Which engineering decision is most suitable for this specific situation?

  1. Set the experimental Spark property in that workload's published environment.
  2. Align the published environment libraries used by both notebooks.
  3. Set the chosen published environment as the workspace default environment.
  4. Publish a Fabric environment with the common library and attach it to the notebooks.

Explanation

Correct Answer

B:

High-concurrency sharing requires matching library packages, in addition to compatible compute and identities.

Incorrect Answers

A:

Environment-specific Spark properties can scope settings to an attached workload.

C:

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

D:

Environment items centrally define reusable libraries and Spark runtime settings.

Question 64

In a Microsoft Fabric solution, a team wants to see what a workspace publication would change in source control before committing modifications. Which configuration or implementation should the engineer choose?

  1. Commit the environment configuration and notebook changes together when both are supported.
  2. Review the Git changes and differences for the affected supported Fabric items.
  3. Use a SQL database project containing the warehouse schema definitions.
  4. Use the deployment pipeline stage comparison to review pending item changes.

Explanation

Correct Answer

B:

Git comparison exposes modified definitions prior to persistence.

Incorrect Answers

A:

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

C:

Database projects capture deployable SQL object definitions in source control.

D:

Stage comparison identifies supported items that changed or are missing between stages.

Question 65

A KQL query suddenly fails with an unknown-column error after the source payload renamed its status field. Which action best addresses the requirement?

  1. Update the event schema parsing and transformation expressions for the new structure.
  2. Update the column reference or apply an explicit rename mapping to the new schema.
  3. Pin a compatible package/runtime combination in the published environment.
  4. Update the KQL column reference or project a compatible alias.

Explanation

Correct Answer

D:

KQL queries must reference fields that exist in the current table schema.

Incorrect Answers

A:

Transformations need field paths consistent with actual source messages.

B:

A missing field name cannot be resolved by the Spark analyzer.

C:

Runtime upgrades can change dependent library compatibility.

Question 66

A pipeline copies a large trusted Delta dataset multiple times for consumers that could use a reference. Which engineering decision is most suitable for this specific situation?

  1. Configure supported partitioned source reads and controlled parallelism.
  2. Create an appropriate materialized view on the native table.
  3. Use an appropriate shared location or shortcut instead of redundant full copies.
  4. Use a predicate on the physical partition column and preserve pruning.

Explanation

Correct Answer

C:

Virtualized access avoids avoidable data movement.

Incorrect Answers

A:

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

B:

Precomputed aggregates reduce repeated full computations.

D:

An eligible filter allows partition pruning.

Question 67

In a Microsoft Fabric solution, a retailer receives order facts with event dates that are earlier than their arrival dates; daily reporting must use when the order occurred. Which configuration or implementation should the engineer choose?

  1. Backfill only the missing business-date partition with idempotent processing.
  2. Recompute or adjust affected aggregates using the changed underlying business keys.
  3. Partition and aggregate by business event date, while tracking arrival time separately.
  4. Use event-time processing with a configured lateness or watermark policy.

Explanation

Correct Answer

C:

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

Incorrect Answers

A:

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

B:

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

D:

Event-time watermarks allow controlled late-arriving contributions before state is finalized.

Question 68

A storage event trigger is enabled but only files outside the configured subject path are being created. Which engineering decision is most suitable for this specific situation?

  1. Correct the Eventhouse ingestion mapping to the intended timestamp column and type.
  2. Correct the Copy mapping and type conversions for the affected columns.
  3. Correct the event subject filter to match the actual file path and event type.
  4. Inspect evaluated dynamic expressions and correct the runtime source path.

Explanation

Correct Answer

C:

Event-based triggers fire only for events satisfying configured matching conditions.

Incorrect Answers

A:

Ingestion mapping aligns payload paths with typed target fields.

B:

Data type mismatch occurs at mapping or insert even when connectivity succeeds.

D:

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

Question 69

Old unreferenced Delta files remain after the approved history retention window. Which action best addresses the requirement?

  1. Run VACUUM with a safe retention policy.
  2. Tune supported ingestion batching policy for the feed.
  3. Run Delta OPTIMIZE compaction.
  4. Coalesce appropriately before writing.

Explanation

Correct Answer

A:

VACUUM removes eligible unreferenced files. The result addresses the requirement stated in this case.

Incorrect Answers

B:

Suitable batching balances ingest overhead with freshness.

C:

Compaction combines small files into more efficient files.

D:

Reducing output partitions prevents excessive small files.

Question 70

A staging table contains several order versions and only the newest per order_id should survive. Which action best addresses the requirement?

  1. Use ROW_NUMBER partitioned by order_id ordered newest first.
  2. Use PySpark unionByName with controlled missing-field handling.
  3. Create a pipeline Copy activity into the destination.
  4. Use Delta MERGE keyed by order ID.

Explanation

Correct Answer

A:

Ranking selects the latest version per business key.

Incorrect Answers

B:

Name-based union aligns columns without relying on position.

C:

Copy handles scalable source-to-target data movement.

D:

MERGE handles matched updates and unmatched inserts.