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Databricks Certified Data Engineer Associate Practice Test 86

Question 850

The render_job_telemetry_metro pipeline at Media postproduction team has this failure: render_job_telemetry_metro aggregation spends most time scheduling thousands of tiny shuffle tasks. The team observed that Spark UI shows very low per-task data size and excessive scheduling overhead. Business requirement: performance changes must be benchmarked against the same output. The target approach is approved. The current task is to introduce it safely with bounded scope and rollback. What should be done next to deploy the approved solution?

  1. Increase shuffle partitions without measuring task size or skew at Media postproduction team
  2. Tune spark.sql.shuffle.partitions or AQE behavior based on measured shuffle workload
  3. Apply a bounded change in staging and compare resource cost
  4. Rerun identical aggregation and verify result parity and faster completion

Explanation

Correct Answer

C:

Apply a bounded change in staging and compare resource cost provides a bounded way to introduce the approved solution into render_job_telemetry_metro and detect an early mismatch. The existing signal is Spark UI shows very low per-task data size and excessive scheduling overhead; a staged change protects the service requirement (performance changes must be benchmarked against the same output) and makes rollback practical.

Incorrect Answers

A:

Arbitrary partition expansion can increase overhead. The proposed action (Increase shuffle partitions without measuring task size or skew at Media postproduction team) does not meet the requirement that performance changes must be benchmarked against the same output, nor does it constitute the controlled implementation step.

B:

Tune spark.sql.shuffle.partitions or AQE behavior based on measured shuffle workload is a plausible lasting design once chosen, but it does not perform the controlled implementation step at this stage. In the render_job_telemetry_metro scenario, the observed evidence is that Spark UI shows very low per-task data size and excessive scheduling overhead; the requirement remains: performance changes must be benchmarked against the same output.

D:

Rerun identical aggregation and verify result parity and faster completion is a one-time acceptance check. It does not supply the controlled implementation step for render_job_telemetry_metro; a passing test cannot substitute for the specific task now required.

Question 851

At Aquaculture farming group, the backfill recovery workflow handles water-quality readings. The problem is that water_quality_readings_coastal failed halfway through a historical backfill, and a rerun doubles completed partitions. Evidence: run log records successfully committed early dates before failure. Constraint: recovery must replay only incomplete work or be provably idempotent. The first acceptance run passed. The current task is to detect future regressions and changes over time. Which monitoring approach will detect newly emerging defects?

  1. Use partition-aware idempotent writes and recoverable backfill parameters
  2. Append the entire historical dataset on every restart at Aquaculture farming group
  3. Restart from the failure boundary and verify no partition is doubled
  4. Monitor retry replay volumes and partition reconciliation for water_quality_readings_coastal

Explanation

Correct Answer

D:

Monitor retry replay volumes and partition reconciliation for water_quality_readings_coastal creates recurring visibility into the failure mode (water_quality_readings_coastal failed halfway through a historical backfill, and a rerun doubles completed partitions). Unlike a single successful test, the measure can reveal drift, lag, or new failures after later source and deployment changes while recovery must replay only incomplete work or be provably idempotent.

Incorrect Answers

A:

Use partition-aware idempotent writes and recoverable backfill parameters is a plausible lasting design once chosen, but it does not perform continuing operational detection at this stage. In the water_quality_readings_coastal scenario, the observed evidence is that run log records successfully committed early dates before failure; the requirement remains: recovery must replay only incomplete work or be provably idempotent.

B:

Unconditional append duplicates earlier successful partitions. The proposed action (Append the entire historical dataset on every restart at Aquaculture farming group) does not meet the requirement that recovery must replay only incomplete work or be provably idempotent, nor does it constitute continuing operational detection.

C:

Restart from the failure boundary and verify no partition is doubled is a one-time acceptance check. It does not supply continuing operational detection for water_quality_readings_coastal; a passing test cannot substitute for the specific task now required.

Question 852

Consider the turbine_sensor_messages_coastal workload at Renewable energy operator: turbine_sensor_messages_coastal job fails after a Python library upgrade despite unchanged notebook code. Inspection found that driver logs show incompatible package versions imported at startup. The governed outcome must ensure that dependency versions must be reproducible across production runs. The team has not selected a remedy. It first needs evidence that distinguishes the actual dependency or data fault. Which evidence-gathering action should precede an architectural change?

  1. Pin compatible library versions with supported compute/runtime environment
  2. Compare installed libraries, runtime version and import trace for turbine_sensor_messages_coastal
  3. Install random latest packages on shared production compute at Renewable energy operator
  4. Rebuild a test environment with locked dependencies and execute a smoke run

Explanation

Correct Answer

B:

Compare installed libraries, runtime version and import trace for turbine_sensor_messages_coastal directly investigates the evidence (driver logs show incompatible package versions imported at startup) and distinguishes possible causes of turbine_sensor_messages_coastal job fails after a Python library upgrade despite unchanged notebook code. It establishes what must be corrected without assuming a fix before verifying the stated business requirement: dependency versions must be reproducible across production runs.

Incorrect Answers

A:

Pin compatible library versions with supported compute/runtime environment is a plausible lasting design once chosen, but it does not perform the required pre-change investigation at this stage. In the turbine_sensor_messages_coastal scenario, the observed evidence is that driver logs show incompatible package versions imported at startup; the requirement remains: dependency versions must be reproducible across production runs.

C:

Uncontrolled upgrades can introduce new incompatibilities. The proposed action (Install random latest packages on shared production compute at Renewable energy operator) does not meet the requirement that dependency versions must be reproducible across production runs, nor does it constitute the required pre-change investigation.

D:

Rebuild a test environment with locked dependencies and execute a smoke run may introduce a correction, but the question does not ask for installation or migration. A rollout alone cannot replace the required pre-change investigation, especially given the evidence that driver logs show incompatible package versions imported at startup. This matters here because consider the turbine_sensor_messages_coastal workload at Renewable energy operator: turbine_sensor_messages_coastal….

Question 853

The turbine_sensor_messages_coastal pipeline at Renewable energy operator has this failure: turbine_sensor_messages_coastal job fails after a Python library upgrade despite unchanged notebook code. The team observed that driver logs show incompatible package versions imported at startup. Business requirement: dependency versions must be reproducible across production runs. The new approach is deployed. The current task is to prove correct output and authorized behavior, not just successful installation. What test most directly proves the requirement is satisfied?

  1. Verify the failing import and transformation work in clean compute
  2. Rebuild a test environment with locked dependencies and execute a smoke run
  3. Compare installed libraries, runtime version and import trace for turbine_sensor_messages_coastal
  4. Install random latest packages on shared production compute at Renewable energy operator

Explanation

Correct Answer

A:

Verify the failing import and transformation work in clean compute directly tests the behavior that should change following the correction. With the observed evidence (driver logs show incompatible package versions imported at startup), the acceptance result must demonstrate actual data or permission correctness, while retaining the requirement that dependency versions must be reproducible across production runs.

Incorrect Answers

B:

Rebuild a test environment with locked dependencies and execute a smoke run may introduce a correction, but the question does not ask for installation or migration. A rollout alone cannot replace the post-change acceptance test, especially given the evidence that driver logs show incompatible package versions imported at startup. This matters here because the turbine_sensor_messages_coastal pipeline at Renewable energy operator has this failure:….

C:

Compare installed libraries, runtime version and import trace for turbine_sensor_messages_coastal could clarify the original fault, but the available evidence already establishes its scope; it is not the post-change acceptance test now requested. The turbine_sensor_messages_coastal workflow still requires that dependency versions must be reproducible across production runs.

D:

Uncontrolled upgrades can introduce new incompatibilities. The proposed action (Install random latest packages on shared production compute at Renewable energy operator) does not meet the requirement that dependency versions must be reproducible across production runs, nor does it constitute the post-change acceptance test.

Question 854

At Regional sporting venue, the job DAG observability workflow handles admission scan events. The problem is that admission_scan_events_central final export shows success even though an optional upstream data audit failed. Evidence: the team treats final task status as whole-pipeline correctness. Constraint: operational alerts must reflect required DAG path failures. The new approach is deployed. The current task is to prove correct output and authorized behavior, not just successful installation. Which test should pass before the new flow is accepted?

  1. Simulate a required task failure and verify publish stops and alert fires
  2. Monitor only the last task while ignoring failed required upstream nodes at Regional sporting venue
  3. Configure failure notification and dependency gates before prod promotion
  4. Inspect task graph run states and required-versus-optional dependencies for admission_scan_events_central

Explanation

Correct Answer

A:

Simulate a required task failure and verify publish stops and alert fires directly tests the behavior that should change after the correction. With the observed evidence (the team treats final task status as whole-pipeline correctness), the acceptance result must demonstrate actual data or permission correctness, while retaining the requirement that operational alerts must reflect required DAG path failures.

Incorrect Answers

B:

Last-task success does not prove full dag correctness. The proposed action (Monitor only the last task while ignoring failed required upstream nodes at Regional sporting venue) does not meet the requirement that operational alerts must reflect required DAG path failures, nor does it constitute the post-change acceptance test.

C:

Configure failure notification and dependency gates before prod promotion may introduce a correction, but the question does not ask for installation or migration. A rollout alone cannot replace the post-change acceptance test, especially given the evidence that the team treats final task status as whole-pipeline correctness.

D:

Inspect task graph run states and required-versus-optional dependencies for admission_scan_events_central could clarify the original fault, but the available evidence already establishes its scope; it is not the post-change acceptance test now requested. The admission_scan_events_central workflow still requires that operational alerts must reflect required DAG path failures.

Question 855

The pollution_measurements_central pipeline at Air quality nonprofit has this failure: a new optional field appears in pollution_measurements_central JSON files and the ingestion job starts rejecting rows. The team observed that failure details identify a field absent from the previously inferred schema. Business requirement: new attributes must be captured without silently corrupting existing columns. The team has not selected a remedy. It first needs evidence that distinguishes the actual dependency or data fault. What should the engineer inspect to establish the failure mechanism?

  1. Pilot the new field, persist the updated schema and restart as required
  2. Silently drop every unexpected field and label ingestion complete at Air quality nonprofit
  3. Inspect schema inference, evolution mode and rescued data behavior on pollution_measurements_central
  4. Configure supported Auto Loader schema evolution with an explicit schema location and review process

Explanation

Correct Answer

C:

Inspect schema inference, evolution mode and rescued data behavior on pollution_measurements_central directly investigates the evidence (failure details identify a field absent from the previously inferred schema) and distinguishes possible causes of a new optional field appears in pollution_measurements_central JSON files and the ingestion job starts rejecting rows. It establishes what must be corrected without assuming a fix before verifying the stated business requirement: new attributes must be captured without silently corrupting existing columns.

Incorrect Answers

A:

Pilot the new field, persist the updated schema and restart as required may introduce a correction, but the question does not ask for installation or migration. A rollout alone cannot replace the required pre-change investigation, especially given the evidence that failure details identify a field absent from the previously inferred schema. This matters here because the pollution_measurements_central pipeline at Air quality nonprofit has this failure: a new optional field appears….

B:

Dropping added fields hides source changes and loses information. The proposed action (Silently drop every unexpected field and label ingestion complete at Air quality nonprofit) does not meet the requirement that new attributes must be captured without silently corrupting existing columns, nor does it constitute the required pre-change investigation.

D:

Configure supported Auto Loader schema evolution with an explicit schema location and review process is a plausible lasting design once chosen, but it does not perform the required pre-change investigation at this stage. In the pollution_measurements_central scenario, the observed evidence is that failure details identify a field absent from the previously inferred schema; the requirement remains: new attributes must be captured without silently corrupting existing columns.

Question 856

A data engineer at Local government portal is responsible for citizen service applications. The inner versus left join issue is that citizen_service_applications_metro orders disappear from silver after a dimension join. The supporting evidence is that audit shows some orders have a missing matching customer row. The engineer must ensure that all fact orders must remain visible even if a dimension lookup fails. The new approach is deployed. The current task is to prove correct output and authorized behavior, not just successful installation. What evidence establishes that correct output is maintained?

  1. Count unmatched keys and examine join predicates for citizen_service_applications_metro
  2. Replace the fact join with inner join and discard nonmatches at Local government portal
  3. Compare fact input and output row counts and inspect unmatched dimension values
  4. Introduce the corrected join with data-quality quarantine for missing attributes

Explanation

Correct Answer

C:

Compare fact input and output row counts and inspect unmatched dimension values directly tests the behavior that should change following the correction. With the observed evidence (audit shows some orders have a missing matching customer row), the acceptance result must demonstrate actual data or permission correctness, while retaining the requirement that all fact orders must remain visible even if a dimension lookup fails.

Incorrect Answers

A:

Count unmatched keys and examine join predicates for citizen_service_applications_metro could clarify the original fault, but the available evidence already establishes its scope; it is not the post-change acceptance test now requested. The citizen_service_applications_metro workflow still requires that all fact orders must remain visible even if a dimension lookup fails.

B:

An inner join suppresses facts when the right-side lookup is absent. The proposed action (Replace the fact join with inner join and discard nonmatches at Local government portal) does not meet the requirement that all fact orders must remain visible even if a dimension lookup fails, nor does it constitute the post-change acceptance test.

D:

Introduce the corrected join with data-quality quarantine for missing attributes may introduce a correction, but the question does not ask for installation or migration. A rollout alone cannot replace the post-change acceptance test, especially given the evidence that audit shows some orders have a missing matching customer row. This matters here because a data engineer at Local government portal is responsible for citizen service applications. The inner versus left….

Question 857

At Cloud gaming cooperative, the groupBy distinct metrics workflow handles match-session events. The problem is that match_session_events_west revenue reporting counts each customer event as if it were a unique customer. Evidence: a sample shows three transactions from the same account on one date. Constraint: dashboard needs daily total revenue and distinct paying accounts. The team has not selected a remedy. It first needs evidence that distinguishes the actual dependency or data fault. Which investigation is the strongest first step?

  1. Reconcile invoice IDs, account IDs and grouping dates in match_session_events_west
  2. GroupBy day with sum(amount) and countDistinct(account_id) for unique accounts
  3. Use sum(account_id) or count of rows as unique-customer count at Cloud gaming cooperative
  4. Publish the aggregate behind a reconciled silver-to-gold transform

Explanation

Correct Answer

A:

Reconcile invoice IDs, account IDs and grouping dates in match_session_events_west directly investigates the evidence (a sample shows three transactions from the same account on one date) and distinguishes possible causes of match_session_events_west revenue reporting counts each customer event as if it were a unique customer. It establishes what must be corrected without assuming a fix before verifying the stated business requirement: dashboard needs daily total revenue and distinct paying accounts.

Incorrect Answers

B:

GroupBy day with sum(amount) and countDistinct(account_id) for unique accounts is a plausible lasting design once chosen, but it does not perform the required pre-change investigation at this stage. In the match_session_events_west scenario, the observed evidence is that a sample shows three transactions from the same account on one date; the requirement remains: dashboard needs daily total revenue and distinct paying accounts.

C:

Row counts or numeric sums cannot measure distinct account identifiers. The proposed action (Use sum(account_id) or count of rows as unique-customer count at Cloud gaming cooperative) does not meet the requirement that dashboard needs daily total revenue and distinct paying accounts, nor does it constitute the required pre-change investigation.

D:

Publish the aggregate behind a reconciled silver-to-gold transform may introduce a correction, but the question does not ask for installation or migration. A rollout alone cannot replace the required pre-change investigation, especially given the evidence that a sample shows three transactions from the same account on one date. Here, the relevant situation is: the groupBy distinct metrics workflow handles match-session events. The problem is that match_session_events_west….

Question 858

Consider the dispatch_and_delivery_status_coastal workload at Food delivery franchise: dispatch_and_delivery_status_coastal overnight export fires one hour early after daylight-saving changes. Inspection found that run history shows schedule configured in a different timezone than business cutoff. The governed outcome must ensure that the export must complete relative to the local reporting calendar. The new approach is deployed. The current task is to prove correct output and authorized behavior, not just successful installation. How should the team verify the original fault has been addressed?

  1. Change the job schedule in a test environment and review next fire times
  2. Inspect schedule timezone, cron expression and daylight-saving policy for dispatch_and_delivery_status_coastal
  3. Verify triggers across DST transition dates against business calendar
  4. Assume all local timezones have the same UTC offset year-round at Food delivery franchise

Explanation

Correct Answer

C:

Verify triggers across DST transition dates against business calendar directly tests the behavior that should change following the correction. With the observed evidence (run history shows schedule configured in a different timezone than business cutoff), the acceptance result must demonstrate actual data or permission correctness, while retaining the requirement that the export must complete relative to the local reporting calendar.

Incorrect Answers

A:

Change the job schedule in a test environment and review next fire times may introduce a correction, but the question does not ask for installation or migration. A rollout alone cannot replace the post-change acceptance test, especially given the evidence that run history shows schedule configured in a different timezone than business cutoff. This matters here because consider the dispatch_and_delivery_status_coastal workload at Food delivery franchise:….

B:

Inspect schedule timezone, cron expression and daylight-saving policy for dispatch_and_delivery_status_coastal could clarify the original fault, but the available evidence already establishes its scope; it is not the post-change acceptance test now requested. The dispatch_and_delivery_status_coastal workflow still requires that the export must complete relative to the local reporting calendar.

D:

Timezones with dst can shift relative to utc. The proposed action (Assume all local timezones have the same UTC offset year-round at Food delivery franchise) does not meet the requirement that the export must complete relative to the local reporting calendar, nor does it constitute the post-change acceptance test.

Question 859

At Cloud gaming cooperative, the bundle environment promotion isolation workflow handles match-session events. The problem is that match_session_events_urban test run overwrites production tables because both deployment targets share one catalog. Evidence: resolved bundle targets point to the same UC prefix. Constraint: development tests must not mutate production data. The failure and requirements have been diagnosed; the team must select the lasting pipeline or governance approach. What governed target implementation should the engineer design?

  1. Segregate dev/test/prod catalog settings using bundle target overrides
  2. Use identical production destinations and hope developers avoid dangerous input at Cloud gaming cooperative
  3. Compare target resolution, workspace URL and catalog bindings for match_session_events_urban
  4. Monitor cross-environment writes and unexpected permissions for match_session_events_urban

Explanation

Correct Answer

A:

Segregate dev/test/prod catalog settings using bundle target overrides is the intended durable approach for match_session_events_urban: it addresses the verified problem (match_session_events_urban test run overwrites production tables because both deployment targets share one catalog) while preserving the required behavior (development tests must not mutate production data). A diagnostic or monitoring step alone would not create this target-state behavior.

Incorrect Answers

B:

Shared output targets defeat environment isolation. The proposed action (Use identical production destinations and hope developers avoid dangerous input at Cloud gaming cooperative) does not meet the requirement that development tests must not mutate production data, nor does it constitute the target approach.

C:

Compare target resolution, workspace URL and catalog bindings for match_session_events_urban could clarify the original fault, but the available evidence already establishes its scope; it is not the target approach now requested. The match_session_events_urban workflow still requires that development tests must not mutate production data.

D:

Monitor cross-environment writes and unexpected permissions for match_session_events_urban would help detect future recurrence, but metrics alone do not deliver the target approach. The match_session_events_urban requirement is that development tests must not mutate production data.

Question 860

At Coastal freight alliance, the Auto Loader schema evolution workflow handles shipment manifests. The problem is that a new optional field appears in shipment_manifests_west JSON files and the ingestion job starts rejecting rows. Evidence: failure details identify a field absent from the previously inferred schema. Constraint: new attributes must be captured without silently corrupting existing columns. The failure and requirements have been diagnosed; the team must select the lasting pipeline or governance approach. What governed target implementation should the engineer design?

  1. Configure supported Auto Loader schema evolution with an explicit schema location and review process
  2. Inspect schema inference, evolution mode and rescued data behavior on shipment_manifests_west
  3. Monitor schema changes, rescued columns and parsing failures for shipment_manifests_west
  4. Silently drop every unexpected field and label ingestion complete at Coastal freight alliance

Explanation

Correct Answer

A:

Configure supported Auto Loader schema evolution with an explicit schema location and review process is the intended durable approach for shipment_manifests_west: it addresses the verified problem (a new optional field appears in shipment_manifests_west JSON files and the ingestion job starts rejecting rows) while preserving the required behavior (new attributes must be captured without silently corrupting existing columns). A diagnostic or monitoring step alone would not create this target-state behavior.

Incorrect Answers

B:

Inspect schema inference, evolution mode and rescued data behavior on shipment_manifests_west could clarify the original fault, but the available evidence already establishes its scope; it is not the target approach now requested. The shipment_manifests_west workflow still requires that new attributes must be captured without silently corrupting existing columns.

C:

Monitor schema changes, rescued columns and parsing failures for shipment_manifests_west would help detect future recurrence, but metrics alone do not deliver the target approach. The shipment_manifests_west requirement is that new attributes must be captured without silently corrupting existing columns.

D:

Dropping added fields hides source changes and loses information. The proposed action (Silently drop every unexpected field and label ingestion complete at Coastal freight alliance) does not meet the requirement that new attributes must be captured without silently corrupting existing columns, nor does it constitute the target approach.

Certified Data Engineer Associate Practice Tests