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Microsoft DP-700 Practice Test 28

Question 270

A pipeline has been intermittently failing for weeks. Where can an engineer review status across multiple executions?

  1. Use the Fabric monitoring hub run history filtered for the pipeline.
  2. Review pipeline activity start/end times and success dependency configuration.
  3. Track concurrent run counts, queueing or activity durations, and capacity pressure over time.
  4. Follow the upstream/downstream run relationship to the specific child run.

Explanation

Correct Answer

A:

Run history exposes success, failure, and duration patterns that a single editor execution cannot show. Plot failure timestamps by run ID.

Incorrect Answers

B:

Execution ordering is visible in the activity timeline and should match the intended dependency graph.

C:

Peak load diagnosis needs a time series of execution and resource behavior, not one successful sample.

D:

Run hierarchy connects orchestration stages so the relevant child error is not lost in parent status.

Question 271

A long pipeline's wall-clock time increased unexpectedly. Which view helps identify the activity consuming time?

  1. Inspect the run's activity timeline or Gantt view and detailed duration breakdown.
  2. Review pipeline activity start/end times and success dependency configuration.
  3. Track concurrent run counts, queueing or activity durations, and capacity pressure over time.
  4. Use the Fabric monitoring hub run history filtered for the pipeline.

Explanation

Correct Answer

A:

Activity-level timing distinguishes upstream delay from slow transfer or compute operations. Compare the longest activity to a healthy run.

Incorrect Answers

B:

Execution ordering is visible in the activity timeline and should match the intended dependency graph.

C:

Peak load diagnosis needs a time series of execution and resource behavior, not one successful sample.

D:

Run history exposes success, failure, and duration patterns that a single editor execution cannot show.

Question 272

A Copy activity is successful but produces output later than its dependent notebook. What should the observer check?

  1. Follow the upstream/downstream run relationship to the specific child run.
  2. Track concurrent run counts, queueing or activity durations, and capacity pressure over time.
  3. Review pipeline activity start/end times and success dependency configuration.
  4. Use the unique pipeline run ID in the monitoring view.

Explanation

Correct Answer

C:

Execution ordering is visible in the activity timeline and should match the intended dependency graph. Compare activity timestamps against the dependency edges.

Incorrect Answers

A:

Run hierarchy connects orchestration stages so the relevant child error is not lost in parent status.

B:

Peak load diagnosis needs a time series of execution and resource behavior, not one successful sample.

D:

Run IDs distinguish executions of the same pipeline, including reruns using identical parameters.

Question 273

A parent pipeline invokes a child pipeline and failures appear only in the child. Which trace linkage is useful?

  1. Track concurrent run counts, queueing or activity durations, and capacity pressure over time.
  2. Review pipeline activity start/end times and success dependency configuration.
  3. Inspect the run's activity timeline or Gantt view and detailed duration breakdown.
  4. Follow the upstream/downstream run relationship to the specific child run.

Explanation

Correct Answer

D:

Run hierarchy connects orchestration stages so the relevant child error is not lost in parent status. Open the child run ID and inspect its failed activity.

Incorrect Answers

A:

Peak load diagnosis needs a time series of execution and resource behavior, not one successful sample.

B:

Execution ordering is visible in the activity timeline and should match the intended dependency graph.

C:

Activity-level timing distinguishes upstream delay from slow transfer or compute operations.

Question 274

A capacity team wants evidence that pipeline runs are piling up at peak hours. Which measurement should be collected?

  1. Follow the upstream/downstream run relationship to the specific child run.
  2. Use the unique pipeline run ID in the monitoring view.
  3. Track concurrent run counts, queueing or activity durations, and capacity pressure over time.
  4. Inspect the run's activity timeline or Gantt view and detailed duration breakdown.

Explanation

Correct Answer

C:

Peak load diagnosis needs a time series of execution and resource behavior, not one successful sample. Compare peak-period and off-peak run behavior.

Incorrect Answers

A:

Run hierarchy connects orchestration stages so the relevant child error is not lost in parent status.

B:

Run IDs distinguish executions of the same pipeline, including reruns using identical parameters.

D:

Activity-level timing distinguishes upstream delay from slow transfer or compute operations.

Question 275

Operations must locate a particular run without changing its pipeline definition. What identifier should they search?

  1. Inspect the run's activity timeline or Gantt view and detailed duration breakdown.
  2. Track concurrent run counts, queueing or activity durations, and capacity pressure over time.
  3. Review pipeline activity start/end times and success dependency configuration.
  4. Use the unique pipeline run ID in the monitoring view.

Explanation

Correct Answer

D:

Run IDs distinguish executions of the same pipeline, including reruns using identical parameters. Open the targeted run and verify its timestamp.

Incorrect Answers

A:

Activity-level timing distinguishes upstream delay from slow transfer or compute operations.

B:

Peak load diagnosis needs a time series of execution and resource behavior, not one successful sample.

C:

Execution ordering is visible in the activity timeline and should match the intended dependency graph.

Question 276

A PySpark notebook runs slowly and the owner needs to inspect a specific Spark stage. Which monitoring surface should be opened?

  1. Use the Spark application/run ID and the matching driver and executor logs.
  2. Use the semantic model refresh-history page to search for DAX refresh failures instead of opening Spark application stage metrics.
  3. Review the notebook's recent runs and application performance history.
  4. Open the Spark application details and stage metrics from the Fabric monitor experience.

Explanation

Correct Answer

D:

Spark monitoring exposes tasks, stages, shuffles, and execution time rather than only notebook success. Compare the dominant stage with a baseline run.

Incorrect Answers

A:

Application IDs and logs tie an incident to its concrete execution and failure, avoiding confusion with concurrent jobs.

B:

Semantic model refresh history reports semantic-model refresh operations and their outcomes; it does not expose a particular PySpark notebook's executor stages, task distribution, or shuffle spill. Even if a dashboard also appears slow, this unrelated report cannot identify which Spark stage is responsible. The engineer should open the Spark application's monitoring view and inspect its stage-level metrics to diagnose the observed notebook slowdown.

C:

Recent-run telemetry provides duration and status comparisons without modifying the notebook.

Question 277

A notebook fails after a library upgrade, and the job monitor only says Failed. Where should technical detail be examined?

  1. Open the Spark activity's inline monitoring details from the pipeline run.
  2. Track source and output row counts or defined quality metrics for the notebook transformation.
  3. Inspect notebook or Spark application logs and the failed cell or stage traceback.
  4. Review the notebook's recent runs and application performance history.

Explanation

Correct Answer

C:

A job status is only a summary; logs reveal the exception and affected operation. Reproduce the failing cell in an isolated session.

Incorrect Answers

A:

Inline Spark monitoring connects orchestration activity with the underlying Spark application and stages.

B:

Execution success does not prove that business records were present or transformed correctly.

D:

Recent-run telemetry provides duration and status comparisons without modifying the notebook.

Question 278

A Spark job definition was triggered by a pipeline. Which link can reveal Spark performance context?

  1. Inspect per-task duration and shuffle/skew details in Spark stage monitoring.
  2. Inspect notebook or Spark application logs and the failed cell or stage traceback.
  3. Track source and output row counts or defined quality metrics for the notebook transformation.
  4. Open the Spark activity's inline monitoring details from the pipeline run.

Explanation

Correct Answer

D:

Inline Spark monitoring connects orchestration activity with the underlying Spark application and stages. Match the Spark application ID to the pipeline execution.

Incorrect Answers

A:

Large task imbalances reveal partition skew or shuffle bottlenecks that overall runtime hides.

B:

A job status is only a summary; logs reveal the exception and affected operation.

C:

Execution success does not prove that business records were present or transformed correctly.

Question 279

A data engineer wants to compare the last five notebook executions to find a regression. What should they examine?

  1. Track source and output row counts or defined quality metrics for the notebook transformation.
  2. Inspect per-task duration and shuffle/skew details in Spark stage monitoring.
  3. Use the Spark application/run ID and the matching driver and executor logs.
  4. Review the notebook's recent runs and application performance history.

Explanation

Correct Answer

D:

Recent-run telemetry provides duration and status comparisons without modifying the notebook. Compare the same workload and data volumes across runs.

Incorrect Answers

A:

Execution success does not prove that business records were present or transformed correctly.

B:

Large task imbalances reveal partition skew or shuffle bottlenecks that overall runtime hides.

C:

Application IDs and logs tie an incident to its concrete execution and failure, avoiding confusion with concurrent jobs.

Question 280

A distributed join runs for hours while CPU is uneven across tasks. Which metric should guide investigation?

  1. Open the Spark activity's inline monitoring details from the pipeline run.
  2. Track source and output row counts or defined quality metrics for the notebook transformation.
  3. Inspect per-task duration and shuffle/skew details in Spark stage monitoring.
  4. Use the Spark application/run ID and the matching driver and executor logs.

Explanation

Correct Answer

C:

Large task imbalances reveal partition skew or shuffle bottlenecks that overall runtime hides. Compare task duration percentiles and partition sizes.

Incorrect Answers

A:

Inline Spark monitoring connects orchestration activity with the underlying Spark application and stages.

B:

Execution success does not prove that business records were present or transformed correctly.

D:

Application IDs and logs tie an incident to its concrete execution and failure, avoiding confusion with concurrent jobs.

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