Databricks Certified Data Engineer Associate Practice Test 32
Question 310
A data engineer at Manufacturing quality network is responsible for factory defect records. The service principal data grants issue is that factory_defect_records_urban scheduled ingestion succeeds under one employee but fails under automation identity. The supporting evidence is that UC audit shows only the employee has the required table rights. The engineer must ensure that the job must operate under a managed nonhuman least-privilege principal. 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?
- Switch the job identity and verify its input and output access
- Run the pipeline end-to-end using the service principal and deny unrelated catalogs
- Compare job run-as identity and catalog, schema and table grants for factory_defect_records_urban
- Share a workspace administrator's personal access token with all job owners at Manufacturing quality network
Explanation
Correct Answer
B:Run the pipeline end-to-end using the service principal and deny unrelated catalogs directly tests the behavior that should change after the correction. With the observed evidence (UC audit shows only the employee has the required table rights), the acceptance result must demonstrate actual data or permission correctness, while retaining the requirement that the job must operate under a managed nonhuman least-privilege principal.
Incorrect Answers
A:Switch the job identity and verify its input and output access 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 UC audit shows only the employee has the required table rights.
C:Compare job run-as identity and catalog, schema and table grants for factory_defect_records_urban could clarify the original fault, but the available evidence already establishes its scope; it is not the post-change acceptance test now requested. The factory_defect_records_urban workflow still requires that the job must operate under a managed nonhuman least-privilege principal.
D:Shared personal tokens are fragile and overprivileged. The proposed action (Share a workspace administrator's personal access token with all job owners at Manufacturing quality network) does not meet the requirement that the job must operate under a managed nonhuman least-privilege principal, nor does it constitute the post-change acceptance test.
Question 311
The trade_settlement_files_delta pipeline at Wholesale produce exchange has this failure: trade_settlement_files_delta production job UI was manually edited after bundle deployment. The team observed that resolved job settings differ from the versioned bundle manifest. Business requirement: releases must remain traceable to approved Git changes. 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?
- Update the source by copying random current production settings without review at Wholesale produce exchange
- Check resource properties and a smoke run against the approved manifest
- Reapply the approved bundle after controlled review of production-only changes
- Treat the versioned bundle as the source of truth and prohibit unreviewed manual drift
Explanation
Correct Answer
C:Reapply the approved bundle after controlled review of production-only changes provides a bounded way to introduce the approved solution into trade_settlement_files_delta and detect an early mismatch. The existing signal is resolved job settings differ from the versioned bundle manifest; a staged change protects the service requirement (releases must remain traceable to approved Git changes) and makes rollback practical.
Incorrect Answers
A:Unreviewed drift can be promoted into every environment. The proposed action (Update the source by copying random current production settings without review at Wholesale produce exchange) does not meet the requirement that releases must remain traceable to approved Git changes, nor does it constitute the controlled implementation step.
B:Check resource properties and a smoke run against the approved manifest is a one-time acceptance check. It does not supply the controlled implementation step for trade_settlement_files_delta; a passing test cannot substitute for the specific task now required.
D:Treat the versioned bundle as the source of truth and prohibit unreviewed manual drift is a plausible lasting design once chosen, but it does not perform the controlled implementation step at this stage. In the trade_settlement_files_delta scenario, the observed evidence is that resolved job settings differ from the versioned bundle manifest; the requirement remains: releases must remain traceable to approved Git changes.
Question 312
A data engineer at Food delivery franchise is responsible for dispatch and delivery status. The predictive optimization behavior issue is that dispatch_and_delivery_status_south managed table has manual OPTIMIZE jobs colliding with platform-managed maintenance. The supporting evidence is that table maintenance history shows overlapping compaction attempts. The engineer must ensure that the maintenance policy must avoid redundant cost and scheduling conflict. The first acceptance run passed. The current task is to detect future regressions and changes over time. What repeatable measure detects this failure after go-live?
- Compare file counts, query results and maintenance cost across a full cycle
- Use predictive optimization where supported and retire redundant manual jobs
- Monitor predictive optimization events, costs and deferred maintenance for dispatch_and_delivery_status_south
- Manually VACUUM all data files before retention safeguards are checked at Food delivery franchise
Explanation
Correct Answer
C:Monitor predictive optimization events, costs and deferred maintenance for dispatch_and_delivery_status_south creates recurring visibility into the failure mode (dispatch_and_delivery_status_south managed table has manual OPTIMIZE jobs colliding with platform-managed maintenance). Unlike a single successful test, the measure can reveal drift, lag, or new failures after later source and deployment changes while the maintenance policy must avoid redundant cost and scheduling conflict.
Incorrect Answers
A:Compare file counts, query results and maintenance cost across a full cycle is a one-time acceptance check. It does not supply continuing operational detection for dispatch_and_delivery_status_south; a passing test cannot substitute for the specific task now required.
B:Use predictive optimization where supported and retire redundant manual jobs is a plausible lasting design once chosen, but it does not perform continuing operational detection at this stage. In the dispatch_and_delivery_status_south scenario, the observed evidence is that table maintenance history shows overlapping compaction attempts; the requirement remains: the maintenance policy must avoid redundant cost and scheduling conflict.
D:Unguarded vacuum can remove files needed for recovery. The proposed action (Manually VACUUM all data files before retention safeguards are checked at Food delivery franchise) does not meet the requirement that the maintenance policy must avoid redundant cost and scheduling conflict, nor does it constitute continuing operational detection.
Question 313
At Public water district, the pipeline flow metrics workflow depends on pipeline_flow_metrics_delta. Engineers report that pipeline_flow_metrics_delta bronze records include null business IDs that break downstream joins; the evidence is that profiling identifies a small fraction of missing identifiers plus valid optional fields. They require that silver must reject missing required keys but preserve optional null attributes. The target approach is approved. The current task is to introduce it safely with bounded scope and rollback. Which controlled implementation step is appropriate now?
- Create a tested silver transform and quarantine invalid rows before promoting
- Call fillna on every field with zero regardless of semantic type at Public water district
- Filter or quarantine null required IDs while applying explicit nullable rules to optional columns
- Verify no null required IDs remain and legitimate optional-null records survive
Explanation
Correct Answer
A:Create a tested silver transform and quarantine invalid rows before promoting provides a bounded way to introduce the approved solution into pipeline_flow_metrics_delta and detect an early mismatch. The existing signal is profiling identifies a small fraction of missing identifiers plus valid optional fields; a staged change protects the service requirement (silver must reject missing required keys but preserve optional null attributes) and makes rollback practical.
Incorrect Answers
B:Blanket replacement changes data meaning and can corrupt types. The proposed action (Call fillna on every field with zero regardless of semantic type at Public water district) does not meet the requirement that silver must reject missing required keys but preserve optional null attributes, nor does it constitute the controlled implementation step.
C:Filter or quarantine null required IDs while applying explicit nullable rules to optional columns is a plausible lasting design once chosen, but it does not perform the controlled implementation step at this stage. In the pipeline_flow_metrics_delta scenario, the observed evidence is that profiling identifies a small fraction of missing identifiers plus valid optional fields; the requirement remains: silver must reject missing required keys but preserve optional null attributes.
D:Verify no null required IDs remain and legitimate optional-null records survive is a one-time acceptance check. It does not supply the controlled implementation step for pipeline_flow_metrics_delta; a passing test cannot substitute for the specific task now required.
Question 314
At Community energy cooperative, the smart meter readings workflow depends on smart_meter_readings_urban. Engineers report that developers edit smart_meter_readings_urban production notebooks directly and bypass code review; the evidence is that workspace Git history shows changes without a pull request. They require that releases need traceable approval and a recoverable commit. The failure and requirements have been diagnosed; the team must select the lasting pipeline or governance approach. Which platform or PySpark choice best fits the stated semantics?
- Develop in a feature branch with a reviewed pull request before merging
- Monitor direct main-branch pushes and unreviewed notebook changes for smart_meter_readings_urban
- Inspect working branch, remote commit and protected-base rules for smart_meter_readings_urban
- Copy arbitrary notebook cells into production without a commit at Community energy cooperative
Explanation
Correct Answer
A:Develop in a feature branch with a reviewed pull request before merging is the intended durable approach for smart_meter_readings_urban: it addresses the verified problem (developers edit smart_meter_readings_urban production notebooks directly and bypass code review) while preserving the required behavior (releases need traceable approval and a recoverable commit). A diagnostic or monitoring step alone would not create this target-state behavior.
Incorrect Answers
B:Monitor direct main-branch pushes and unreviewed notebook changes for smart_meter_readings_urban would help detect future recurrence, but metrics alone do not deliver the target approach. The smart_meter_readings_urban requirement is that releases need traceable approval and a recoverable commit.
C:Inspect working branch, remote commit and protected-base rules for smart_meter_readings_urban could clarify the original fault, but the available evidence already establishes its scope; it is not the target approach now requested. The smart_meter_readings_urban workflow still requires that releases need traceable approval and a recoverable commit.
D:Untracked copies cannot reliably reproduce the approved source. The proposed action (Copy arbitrary notebook cells into production without a commit at Community energy cooperative) does not meet the requirement that releases need traceable approval and a recoverable commit, nor does it constitute the target approach.
Question 315
The station_observation_files_rural pipeline at Public weather agency has this failure: station_observation_files_rural join hangs on one task while almost every task completes quickly. The team observed that Spark UI reveals one partition contains most rows for a hot customer key. Business requirement: optimization must preserve all correct joined rows. The failure and requirements have been diagnosed; the team must select the lasting pipeline or governance approach. What lasting solution should be selected for the verified conditions?
- Address skew with AQE skew handling or justified key-salting strategy
- Drop the most frequent business key to balance partitions at Public weather agency
- Monitor max-to-median task times and hot-key growth for station_observation_files_rural
- Profile key frequencies and straggler task metrics for station_observation_files_rural
Explanation
Correct Answer
A:Address skew with AQE skew handling or justified key-salting strategy is the intended durable approach for station_observation_files_rural: it addresses the verified problem (station_observation_files_rural join hangs on one task while almost every task completes quickly) while preserving the required behavior (optimization must preserve all correct joined rows). A diagnostic or monitoring step alone would not create this target-state behavior.
Incorrect Answers
B:Dropping a hot key loses legitimate data. The proposed action (Drop the most frequent business key to balance partitions at Public weather agency) does not meet the requirement that optimization must preserve all correct joined rows, nor does it constitute the target approach.
C:Monitor max-to-median task times and hot-key growth for station_observation_files_rural would help detect future recurrence, but metrics alone do not deliver the target approach. The station_observation_files_rural requirement is that optimization must preserve all correct joined rows.
D:Profile key frequencies and straggler task metrics for station_observation_files_rural could clarify the original fault, but the available evidence already establishes its scope; it is not the target approach now requested. The station_observation_files_rural workflow still requires that optimization must preserve all correct joined rows.
Question 316
At Airport facilities operator, the pull request regression tests workflow handles maintenance work orders. The problem is that maintenance_work_orders_delta DataFrame join change increases record count after merging. Evidence: PR diff contains a new join clause but CI checks only linting. Constraint: joining behavior and key uniqueness must be tested before promotion. 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?
- Add functional transformation tests with known join and dedup assertions
- Approve PRs solely on code formatting without data assertions at Airport facilities operator
- Run a duplicate-key fixture and verify output matches expected cardinality
- Monitor CI test regressions and production row-amplification signals for maintenance_work_orders_delta
Explanation
Correct Answer
D:Monitor CI test regressions and production row-amplification signals for maintenance_work_orders_delta creates recurring visibility into the failure mode (maintenance_work_orders_delta DataFrame join change increases record count after merging). Unlike a single successful test, the measure can reveal drift, lag, or new failures after later source and deployment changes while joining behavior and key uniqueness must be tested before promotion.
Incorrect Answers
A:Add functional transformation tests with known join and dedup assertions is a plausible lasting design once chosen, but it does not perform continuing operational detection at this stage. In the maintenance_work_orders_delta scenario, the observed evidence is that PR diff contains a new join clause but CI checks only linting; the requirement remains: joining behavior and key uniqueness must be tested before promotion.
B:Well-formatted code can still produce incorrect data. The proposed action (Approve PRs solely on code formatting without data assertions at Airport facilities operator) does not meet the requirement that joining behavior and key uniqueness must be tested before promotion, nor does it constitute continuing operational detection.
C:Run a duplicate-key fixture and verify output matches expected cardinality is a one-time acceptance check. It does not supply continuing operational detection for maintenance_work_orders_delta; a passing test cannot substitute for the specific task now required.
Question 317
The ticket_transactions_coastal pipeline at Independent cinema group has this failure: ticket_transactions_coastal quality validation flags an empty input but the pipeline always publishes gold. The team observed that the job DAG lacks an if/else decision based on validation outcome. Business requirement: empty invalid batches must skip publication while still reporting status. 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?
- Test valid and invalid input and verify only the intended branch executes
- Deploy a condition and branch tasks using an output from the validator
- Always run publish regardless of validator outcome at Independent cinema group
- Use a Lakeflow condition task to route pass and fail paths explicitly
Explanation
Correct Answer
B:Deploy a condition and branch tasks using an output from the validator provides a bounded way to introduce the approved solution into ticket_transactions_coastal and detect an early mismatch. The existing signal is the job DAG lacks an if/else decision based on validation outcome; a staged change protects the service requirement (empty invalid batches must skip publication while still reporting status) and makes rollback practical.
Incorrect Answers
A:Test valid and invalid input and verify only the intended branch executes is a one-time acceptance check. It does not supply the controlled implementation step for ticket_transactions_coastal; a passing test cannot substitute for the specific task now required.
C:Unconditional publication violates the quality gate. The proposed action (Always run publish regardless of validator outcome at Independent cinema group) does not meet the requirement that empty invalid batches must skip publication while still reporting status, nor does it constitute the controlled implementation step.
D:Use a Lakeflow condition task to route pass and fail paths explicitly is a plausible lasting design once chosen, but it does not perform the controlled implementation step at this stage. In the ticket_transactions_coastal scenario, the observed evidence is that the job DAG lacks an if/else decision based on validation outcome; the requirement remains: empty invalid batches must skip publication while still reporting status.
Question 318
A data engineer at Online apparel cooperative is responsible for customer order changes. The PySpark window latest record issue is that customer_order_changes_north tracks successive device configurations and needs the most recent per device. The supporting evidence is that the raw table contains equal device_id rows with varying event timestamps. The engineer must ensure that exactly one latest row per device must be selected using deterministic tie-breaks. The first acceptance run passed. The current task is to detect future regressions and changes over time. What repeatable measure detects this failure after go-live?
- Apply row_number over partitionBy(device_id) orderBy(timestamp desc, version desc)
- Insert tied timestamps and verify deterministic version selection
- Call dropDuplicates(device_id) without an explicit ordering rule at Online apparel cooperative
- Monitor tied-key anomalies and late configuration records for customer_order_changes_north
Explanation
Correct Answer
D:Monitor tied-key anomalies and late configuration records for customer_order_changes_north creates recurring visibility into the failure mode (customer_order_changes_north tracks successive device configurations and needs the most recent per device). Unlike a single successful test, the measure can reveal drift, lag, or new failures after later source and deployment changes while exactly one latest row per device must be selected using deterministic tie-breaks.
Incorrect Answers
A:Apply row_number over partitionBy(device_id) orderBy(timestamp desc, version desc) is a plausible lasting design once chosen, but it does not perform continuing operational detection at this stage. In the customer_order_changes_north scenario, the observed evidence is that the raw table contains equal device_id rows with varying event timestamps; the requirement remains: exactly one latest row per device must be selected using deterministic tie-breaks.
B:Insert tied timestamps and verify deterministic version selection is a one-time acceptance check. It does not supply continuing operational detection for customer_order_changes_north; a passing test cannot substitute for the specific task now required.
C:An unordered dedup does not guarantee selection of the newest record. The proposed action (Call dropDuplicates(device_id) without an explicit ordering rule at Online apparel cooperative) does not meet the requirement that exactly one latest row per device must be selected using deterministic tie-breaks, nor does it constitute continuing operational detection.
Question 319
At University research consortium, the experiment records workflow depends on experiment_records_urban. Engineers report that experiment_records_urban revenue reporting counts each customer event as if it were a unique customer; the evidence is that a sample shows three transactions from the same account on one date. They require that dashboard needs daily total revenue and distinct paying accounts. The failure and requirements have been diagnosed; the team must select the lasting pipeline or governance approach. Which platform or PySpark choice best fits the stated semantics?
- Reconcile invoice IDs, account IDs and grouping dates in experiment_records_urban
- Monitor total-versus-distinct counts and unexpected distribution shifts
- GroupBy day with sum(amount) and countDistinct(account_id) for unique accounts
- Use sum(account_id) or count of rows as unique-customer count at University research consortium
Explanation
Correct Answer
C:GroupBy day with sum(amount) and countDistinct(account_id) for unique accounts is the intended durable approach for experiment_records_urban: it addresses the verified problem (experiment_records_urban revenue reporting counts each customer event as if it were a unique customer) while preserving the required behavior (dashboard needs daily total revenue and distinct paying accounts). A diagnostic or monitoring step alone would not create this target-state behavior.
Incorrect Answers
A:Reconcile invoice IDs, account IDs and grouping dates in experiment_records_urban could clarify the original fault, but the available evidence already establishes its scope; it is not the target approach now requested. The experiment_records_urban workflow still requires that dashboard needs daily total revenue and distinct paying accounts.
B:Monitor total-versus-distinct counts and unexpected distribution shifts would help detect future recurrence, but metrics alone do not deliver the target approach. The experiment_records_urban requirement is that dashboard needs daily total revenue and distinct paying accounts.
D: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 University research consortium) does not meet the requirement that dashboard needs daily total revenue and distinct paying accounts, nor does it constitute the target approach.
Question 320
A data engineer at Renewable energy operator is responsible for turbine sensor messages. The library dependency conflict issue is that turbine_sensor_messages_coastal job fails after a Python library upgrade despite unchanged notebook code. The supporting evidence is that driver logs show incompatible package versions imported at startup. The engineer must ensure that dependency versions must be reproducible across production runs. The first acceptance run passed. The current task is to detect future regressions and changes over time. What repeatable measure detects this failure after go-live?
- Pin compatible library versions with supported compute/runtime environment
- Monitor library drift and import failures for turbine_sensor_messages_coastal
- Install random latest packages on shared production compute at Renewable energy operator
- Verify the failing import and transformation work in clean compute
Explanation
Correct Answer
B:Monitor library drift and import failures for turbine_sensor_messages_coastal creates recurring visibility into the failure mode (turbine_sensor_messages_coastal job fails after a Python library upgrade despite unchanged notebook code). Unlike a single successful test, the measure can reveal drift, lag, or new failures after later source and deployment changes while 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 continuing operational detection 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 continuing operational detection.
D:Verify the failing import and transformation work in clean compute is a one-time acceptance check. It does not supply continuing operational detection for turbine_sensor_messages_coastal; a passing test cannot substitute for the specific task now required.