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Microsoft DP-700 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

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Microsoft DP-700 Practice Test 59

Question 580

A Fabric team uses workspace monitoring to investigate slow jobs. The current environment exhibits the following problem: an administrator assumes all Fabric item telemetry tables exist without enabling required features. Which corrective action addresses the root cause?

  1. Use notebook/Spark stage metrics to identify skewed tasks
  2. Inspect item-level monitoring data for that Dataflow
  3. Verify monitoring prerequisites and supported item telemetry configuration
  4. Correlate KQL query metrics with shortcut acceleration state

Explanation

Correct Answer

C:

Verify monitoring prerequisites and supported item telemetry configuration. Monitoring availability varies by item and workspace setup.

Incorrect Answers

A:

A Spark application spends most time shuffling one partition. Stage-level profiling isolates workload-specific bottlenecks.

B:

Operators look only at overall Fabric health while one Dataflow refresh repeatedly fails. Tenant availability does not reveal per-item execution failures.

D:

Eventhouse query latency rises as external shortcut scans increase. External storage reads may drive observed query cost.

Question 581

An operations engineer monitors dataflow transformations in Microsoft Fabric. An acceptance test shows that a Dataflow Gen2 run stops with a failed Power Query merge step. What is the most defensible next action?

  1. Compare source row counts, applied filters, and sink destination status
  2. Identify the failing named Power Query step and schema expectation
  3. Inspect the dataflow refresh details for the specific transformation error
  4. Validate destination write and downstream model refresh ordering

Explanation

Correct Answer

C:

Inspect the dataflow refresh details for the specific transformation error. Source query and transformation failures appear in refresh diagnostics.

Incorrect Answers

A:

A dataflow executes but its destination contains zero rows. Query success doesn't prove data was published.

B:

A new source column causes only one transformation to fail. Step-level lineage narrows schema-drift root cause.

D:

A dependent model still uses old output after a successful dataflow refresh. Each data layer has its own publication or refresh step.

Question 582

An operations engineer monitors dataflow transformations in Microsoft Fabric. The engineer has established the following constraint: a dataflow executes but its destination contains zero rows. Which technical choice fits this evidence?

  1. Compare source row counts, applied filters, and sink destination status
  2. Validate destination write and downstream model refresh ordering
  3. Inspect the dataflow refresh details for the specific transformation error
  4. Identify the failing named Power Query step and schema expectation

Explanation

Correct Answer

A:

Compare source row counts, applied filters, and sink destination status. Query success doesn't prove data was published.

Incorrect Answers

B:

A dependent model still uses old output after a successful dataflow refresh. Each data layer has its own publication or refresh step.

C:

A Dataflow Gen2 run stops with a failed Power Query merge step. Source query and transformation failures appear in refresh diagnostics.

D:

A new source column causes only one transformation to fail. Step-level lineage narrows schema-drift root cause.

Question 583

An operations engineer monitors dataflow transformations in Microsoft Fabric. During an operations handoff, engineers find that the dataflow began after its expected schedule but no event fired. Which step preserves the intended data behavior?

  1. Check the triggering pipeline schedule and dataflow activity linkage
  2. Inspect the dataflow refresh details for the specific transformation error
  3. Identify the failing named Power Query step and schema expectation
  4. Compare source row counts, applied filters, and sink destination status

Explanation

Correct Answer

A:

Check the triggering pipeline schedule and dataflow activity linkage. Trigger and refresh execution are separate parts of the flow.

Incorrect Answers

B:

A Dataflow Gen2 run stops with a failed Power Query merge step. Source query and transformation failures appear in refresh diagnostics.

C:

A new source column causes only one transformation to fail. Step-level lineage narrows schema-drift root cause.

D:

A dataflow executes but its destination contains zero rows. Query success doesn't prove data was published.

Question 584

An operations engineer monitors dataflow transformations in Microsoft Fabric. The team observes that a dependent model still uses old output after a successful dataflow refresh. Which change most directly resolves this failure?

  1. Check the triggering pipeline schedule and dataflow activity linkage
  2. Validate destination write and downstream model refresh ordering
  3. Identify the failing named Power Query step and schema expectation
  4. Inspect the dataflow refresh details for the specific transformation error

Explanation

Correct Answer

B:

Validate destination write and downstream model refresh ordering. Each data layer has its own publication or refresh step.

Incorrect Answers

A:

The dataflow began after its expected schedule but no event fired. Trigger and refresh execution are separate parts of the flow.

C:

A new source column causes only one transformation to fail. Step-level lineage narrows schema-drift root cause.

D:

A Dataflow Gen2 run stops with a failed Power Query merge step. Source query and transformation failures appear in refresh diagnostics.

Question 585

An operations engineer monitors dataflow transformations in Microsoft Fabric. The implementation review confirms that a new source column causes only one transformation to fail. What is the best engineering response?

  1. Identify the failing named Power Query step and schema expectation
  2. Check the triggering pipeline schedule and dataflow activity linkage
  3. Inspect the dataflow refresh details for the specific transformation error
  4. Compare source row counts, applied filters, and sink destination status

Explanation

Correct Answer

A:

Identify the failing named Power Query step and schema expectation. Step-level lineage narrows schema-drift root cause.

Incorrect Answers

B:

The dataflow began after its expected schedule but no event fired. Trigger and refresh execution are separate parts of the flow.

C:

A Dataflow Gen2 run stops with a failed Power Query merge step. Source query and transformation failures appear in refresh diagnostics.

D:

A dataflow executes but its destination contains zero rows. Query success doesn't prove data was published.

Question 586

An engineer monitors Warehouse performance after a new ETL release. A scheduled execution exposes this issue: T-SQL queries run much slower after new tables were loaded. Which action should the team take next?

  1. Compare query insights, plan shape, and current statistics
  2. Correlate the run timestamp with capacity utilization and competing workloads
  3. Compare actual affected row count and merge logic with source extraction
  4. Check source data publication and the downstream refresh or caching boundary

Explanation

Correct Answer

A:

Compare query insights, plan shape, and current statistics. Plan and data distribution changes affect Warehouse performance.

Incorrect Answers

B:

A slow query appears only during capacity saturation. Shared capacity load can amplify query latency.

C:

A pipeline copies millions of rows but the target table is short. Merge filters and mismatched keys can drop intended changes.

D:

A warehouse model refresh isn't seeing newly committed rows. Query availability and consumer refresh can have different timing.

Question 587

An engineer monitors Warehouse performance after a new ETL release. Data validation establishes that a long-running query is blocking a publish window. Which approach best satisfies this requirement?

  1. Inspect active requests and resource contention diagnostics
  2. Compare query insights, plan shape, and current statistics
  3. Compare actual affected row count and merge logic with source extraction
  4. Check source data publication and the downstream refresh or caching boundary

Explanation

Correct Answer

A:

Inspect active requests and resource contention diagnostics. Concurrent queries can interfere with scheduled transformations.

Incorrect Answers

B:

T-SQL queries run much slower after new tables were loaded. Plan and data distribution changes affect Warehouse performance.

C:

A pipeline copies millions of rows but the target table is short. Merge filters and mismatched keys can drop intended changes.

D:

A warehouse model refresh isn't seeing newly committed rows. Query availability and consumer refresh can have different timing.

Question 588

An engineer monitors Warehouse performance after a new ETL release. The production incident report states that a warehouse model refresh isn't seeing newly committed rows. Which corrective action addresses the root cause?

  1. Correlate the run timestamp with capacity utilization and competing workloads
  2. Compare actual affected row count and merge logic with source extraction
  3. Check source data publication and the downstream refresh or caching boundary
  4. Compare query insights, plan shape, and current statistics

Explanation

Correct Answer

C:

Check source data publication and the downstream refresh or caching boundary. Query availability and consumer refresh can have different timing.

Incorrect Answers

A:

A slow query appears only during capacity saturation. Shared capacity load can amplify query latency.

B:

A pipeline copies millions of rows but the target table is short. Merge filters and mismatched keys can drop intended changes.

D:

T-SQL queries run much slower after new tables were loaded. Plan and data distribution changes affect Warehouse performance.

Question 589

An engineer monitors Warehouse performance after a new ETL release. A postdeployment check reveals that a pipeline copies millions of rows but the target table is short. What should the administrator implement or verify?

  1. Compare actual affected row count and merge logic with source extraction
  2. Check source data publication and the downstream refresh or caching boundary
  3. Correlate the run timestamp with capacity utilization and competing workloads
  4. Inspect active requests and resource contention diagnostics

Explanation

Correct Answer

A:

Compare actual affected row count and merge logic with source extraction. Merge filters and mismatched keys can drop intended changes.

Incorrect Answers

B:

A warehouse model refresh isn't seeing newly committed rows. Query availability and consumer refresh can have different timing.

C:

A slow query appears only during capacity saturation. Shared capacity load can amplify query latency.

D:

A long-running query is blocking a publish window. Concurrent queries can interfere with scheduled transformations.

Question 590

An engineer monitors Warehouse performance after a new ETL release. The current environment exhibits the following problem: a slow query appears only during capacity saturation. Which option directly handles this observed condition?

  1. Check source data publication and the downstream refresh or caching boundary
  2. Correlate the run timestamp with capacity utilization and competing workloads
  3. Compare query insights, plan shape, and current statistics
  4. Compare actual affected row count and merge logic with source extraction

Explanation

Correct Answer

B:

Correlate the run timestamp with capacity utilization and competing workloads. Shared capacity load can amplify query latency.

Incorrect Answers

A:

A warehouse model refresh isn't seeing newly committed rows. Query availability and consumer refresh can have different timing.

C:

T-SQL queries run much slower after new tables were loaded. Plan and data distribution changes affect Warehouse performance.

D:

A pipeline copies millions of rows but the target table is short. Merge filters and mismatched keys can drop intended changes.