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Databricks Certified Data Engineer Associate Practice Test 90

Question 890

At Agricultural credit union, the farm-loan transaction history workflow depends on farm_loan_transaction_history_south. Engineers report that farm_loan_transaction_history_south notebook hardcodes a development catalog and writes there during production runs; the evidence is that job run settings differ from values embedded in the notebook code. They require that the same notebook must respect runtime environment configuration. The new approach is deployed. The current task is to prove correct output and authorized behavior, not just successful installation. Which post-change acceptance check is strongest?

  1. Remove hardcoded target paths and test parameterized notebook invocation
  2. Edit the source notebook by hand every time the job changes environment at Agricultural credit union
  3. Run dev and prod parameters and confirm each writes only to its own catalog
  4. Identify job parameter keys and where farm_loan_transaction_history_south notebook resolves the catalog

Explanation

Correct Answer

C:

Run dev and prod parameters and confirm each writes only to its own catalog directly tests the behavior that should change following the correction. With the observed evidence (job run settings differ from values embedded in the notebook code), the acceptance result must demonstrate actual data or permission correctness, while retaining the requirement that the same notebook must respect runtime environment configuration.

Incorrect Answers

A:

Remove hardcoded target paths and test parameterized notebook invocation 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 job run settings differ from values embedded in the notebook code. Here, the relevant situation is: the farm-loan transaction history workflow depends on farm_loan_transaction_history_south. Engineers report that….

B:

Manual code edits are not reproducible parameterization. The proposed action (Edit the source notebook by hand every time the job changes environment at Agricultural credit union) does not meet the requirement that the same notebook must respect runtime environment configuration, nor does it constitute the post-change acceptance test.

D:

Identify job parameter keys and where farm_loan_transaction_history_south notebook resolves the catalog could clarify the original fault, but the available evidence already establishes its scope; it is not the post-change acceptance test now requested. The farm_loan_transaction_history_south workflow still requires that the same notebook must respect runtime environment configuration.

Question 891

The sampling_field_notes_metro pipeline at Environmental inspection office has this failure: sampling_field_notes_metro serves an exploratory dashboard counting hundreds of millions of unique session IDs. The team observed that exact count-distinct creates high aggregation cost during interactive prototyping. Business requirement: users explicitly accept bounded approximation in exchange for speed. 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. Trial approximate aggregation on representative samples against exact ground truth
  2. Use approx_count_distinct for approximate high-cardinality distinct estimates
  3. Substitute sum(session_id) as an estimate of unique sessions at Environmental inspection office
  4. Measure cardinality, acceptable error and resource use for sampling_field_notes_metro

Explanation

Correct Answer

D:

Measure cardinality, acceptable error and resource use for sampling_field_notes_metro directly investigates the evidence (exact count-distinct creates high aggregation cost during interactive prototyping) and distinguishes possible causes of sampling_field_notes_metro serves an exploratory dashboard counting hundreds of millions of unique session IDs. It establishes what must be corrected without assuming a fix before verifying the stated business requirement: users explicitly accept bounded approximation in exchange for speed.

Incorrect Answers

A:

Trial approximate aggregation on representative samples against exact ground truth 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 exact count-distinct creates high aggregation cost during interactive prototyping. This matters here because the sampling_field_notes_metro pipeline at Environmental inspection office has this failure:….

B:

Use approx_count_distinct for approximate high-cardinality distinct estimates is a plausible lasting design once chosen, but it does not perform the required pre-change investigation at this stage. In the sampling_field_notes_metro scenario, the observed evidence is that exact count-distinct creates high aggregation cost during interactive prototyping; the requirement remains: users explicitly accept bounded approximation in exchange for speed.

C:

Adding identifiers is not a distinct-count estimator. The proposed action (Substitute sum(session_id) as an estimate of unique sessions at Environmental inspection office) does not meet the requirement that users explicitly accept bounded approximation in exchange for speed, nor does it constitute the required pre-change investigation.

Question 892

A data engineer at Online bicycle retailer is responsible for product sales transactions. The library dependency conflict issue is that product_sales_transactions_island 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 target approach is approved. The current task is to introduce it safely with bounded scope and rollback. What is the safest migration or deployment sequence?

  1. Verify the failing import and transformation work in clean compute
  2. Pin compatible library versions with supported compute/runtime environment
  3. Rebuild a test environment with locked dependencies and execute a smoke run
  4. Install random latest packages on shared production compute at Online bicycle retailer

Explanation

Correct Answer

C:

Rebuild a test environment with locked dependencies and execute a smoke run provides a bounded way to introduce the approved solution into product_sales_transactions_island and detect an early mismatch. The existing signal is driver logs show incompatible package versions imported at startup; a staged change protects the service requirement (dependency versions must be reproducible across production runs) and makes rollback practical.

Incorrect Answers

A:

Verify the failing import and transformation work in clean compute is a one-time acceptance check. It does not supply the controlled implementation step for product_sales_transactions_island; a passing test cannot substitute for the specific task now required.

B:

Pin compatible library versions with supported compute/runtime environment is a plausible lasting design once chosen, but it does not perform the controlled implementation step at this stage. In the product_sales_transactions_island 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.

D:

Uncontrolled upgrades can introduce new incompatibilities. The proposed action (Install random latest packages on shared production compute at Online bicycle retailer) does not meet the requirement that dependency versions must be reproducible across production runs, nor does it constitute the controlled implementation step.

Question 893

Consider the clinical_appointment_reports_east workload at Regional hospital network: clinical_appointment_reports_east job runs hourly although only one file arrives most days. Inspection found that execution history shows repeated empty runs with compute startup cost. The governed outcome must ensure that process each new delivered file promptly without idle polling. The target approach is approved. The current task is to introduce it safely with bounded scope and rollback. Which staged rollout best preserves the existing workload?

  1. Run a full pipeline every minute even if no files arrive at Regional hospital network
  2. Upload a test file and confirm exactly the expected run fires
  3. Use a supported file-arrival trigger for the eligible storage location
  4. Enable trigger in staging and test a new object arrival

Explanation

Correct Answer

D:

Enable trigger in staging and test a new object arrival provides a bounded way to introduce the approved solution into clinical_appointment_reports_east and detect an early mismatch. The existing signal is execution history shows repeated empty runs with compute startup cost; a staged change protects the service requirement (process each new delivered file promptly without idle polling) and makes rollback practical.

Incorrect Answers

A:

Repetitive empty executions waste compute and cost. The proposed action (Run a full pipeline every minute even if no files arrive at Regional hospital network) does not meet the requirement that process each new delivered file promptly without idle polling, nor does it constitute the controlled implementation step.

B:

Upload a test file and confirm exactly the expected run fires is a one-time acceptance check. It does not supply the controlled implementation step for clinical_appointment_reports_east; a passing test cannot substitute for the specific task now required.

C:

Use a supported file-arrival trigger for the eligible storage location is a plausible lasting design once chosen, but it does not perform the controlled implementation step at this stage. In the clinical_appointment_reports_east scenario, the observed evidence is that execution history shows repeated empty runs with compute startup cost; the requirement remains: process each new delivered file promptly without idle polling.

Question 894

A data engineer at Retail pharmacy network is responsible for inventory adjustments. The nested JSON extraction issue is that nested arrays in inventory_adjustments_highland events are written as uninterpreted strings. The supporting evidence is that sample JSON contains an array of line items under each order. The engineer must ensure that analytics requires one record per line item while retaining the parent ID. The failure and requirements have been diagnosed; the team must select the lasting pipeline or governance approach. Which durable Databricks control best addresses the requirement?

  1. Inspect schema, array cardinality and nested field types for inventory_adjustments_highland
  2. Split raw JSON text on commas to infer nested objects at Retail pharmacy network
  3. Monitor newly nested fields, null child keys and malformed JSON for inventory_adjustments_highland
  4. Parse nested JSON to typed structures and explode line arrays while preserving parent keys

Explanation

Correct Answer

D:

Parse nested JSON to typed structures and explode line arrays while preserving parent keys is the intended durable approach for inventory_adjustments_highland: it addresses the verified problem (nested arrays in inventory_adjustments_highland events are written as uninterpreted strings) while preserving the required behavior (analytics requires one record per line item while retaining the parent ID). A diagnostic or monitoring step alone would not create this target-state behavior.

Incorrect Answers

A:

Inspect schema, array cardinality and nested field types for inventory_adjustments_highland could clarify the original fault, but the available evidence already establishes its scope; it is not the target approach now requested. The inventory_adjustments_highland workflow still requires that analytics requires one record per line item while retaining the parent ID.

B:

String splitting is not valid nested json parsing. The proposed action (Split raw JSON text on commas to infer nested objects at Retail pharmacy network) does not meet the requirement that analytics requires one record per line item while retaining the parent ID, nor does it constitute the target approach.

C:

Monitor newly nested fields, null child keys and malformed JSON for inventory_adjustments_highland would help detect future recurrence, but metrics alone do not deliver the target approach. The inventory_adjustments_highland requirement is that analytics requires one record per line item while retaining the parent ID.

Question 895

At Public water district, the bundle deployment resource promotion workflow handles pipeline flow metrics. The problem is that pipeline_flow_metrics_north pipeline configuration exists only in one engineer's workspace UI. Evidence: releasing to test requires manual reconstruction of tasks. Constraint: the same versioned workload definitions must deploy to multiple targets. 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. Recreate every task manually with different values in each environment at Public water district
  2. Monitor resource drift and unmanaged manually edited production jobs for pipeline_flow_metrics_north
  3. Compare deployed resource properties and run results to the approved bundle
  4. Package supported jobs and pipelines as Declarative Automation Bundle resources

Explanation

Correct Answer

B:

Monitor resource drift and unmanaged manually edited production jobs for pipeline_flow_metrics_north creates recurring visibility into the failure mode (pipeline_flow_metrics_north pipeline configuration exists only in one engineer's workspace UI). Unlike a single successful test, the measure can reveal drift, lag, or new failures after later source and deployment changes while the same versioned workload definitions must deploy to multiple targets.

Incorrect Answers

A:

Manual recreation invites hidden differences and unreproducible releases. The proposed action (Recreate every task manually with different values in each environment at Public water district) does not meet the requirement that the same versioned workload definitions must deploy to multiple targets, nor does it constitute continuing operational detection.

C:

Compare deployed resource properties and run results to the approved bundle is a one-time acceptance check. It does not supply continuing operational detection for pipeline_flow_metrics_north; a passing test cannot substitute for the specific task now required.

D:

Package supported jobs and pipelines as Declarative Automation Bundle resources is a plausible lasting design once chosen, but it does not perform continuing operational detection at this stage. In the pipeline_flow_metrics_north scenario, the observed evidence is that releasing to test requires manual reconstruction of tasks; the requirement remains: the same versioned workload definitions must deploy to multiple targets.

Question 896

Consider the admission_scan_events_metro workload at Regional sporting venue: a admission_scan_events_metro enterprise SaaS table requires recurring ingestion but the team maintains a brittle custom API crawler. Inspection found that API tokens expire and source field names change without ingestion alerts. The governed outcome must ensure that the source must arrive in governed Delta tables with manageable credential rotation. The first acceptance run passed. The current task is to detect future regressions and changes over time. Which continuing signal best reveals recurrence or drift?

  1. Monitor connector lag, source authentication errors and schema drift for admission_scan_events_metro
  2. Update a source row and verify correct incremental destination behavior
  3. Prefer a supported Lakeflow Connect managed connector with managed ingestion state
  4. Copy API secrets into a widely shared notebook and paste full responses manually at Regional sporting venue

Explanation

Correct Answer

A:

Monitor connector lag, source authentication errors and schema drift for admission_scan_events_metro creates recurring visibility into the failure mode (a admission_scan_events_metro enterprise SaaS table requires recurring ingestion but the team maintains a brittle custom API crawler). Unlike a single successful test, the measure can reveal drift, lag, or new failures after later source and deployment changes while the source must arrive in governed Delta tables with manageable credential rotation.

Incorrect Answers

B:

Update a source row and verify correct incremental destination behavior is a one-time acceptance check. It does not supply continuing operational detection for admission_scan_events_metro; a passing test cannot substitute for the specific task now required.

C:

Prefer a supported Lakeflow Connect managed connector with managed ingestion state is a plausible lasting design once chosen, but it does not perform continuing operational detection at this stage. In the admission_scan_events_metro scenario, the observed evidence is that API tokens expire and source field names change without ingestion alerts; the requirement remains: the source must arrive in governed Delta tables with manageable credential rotation.

D:

Shared secrets and manual copies do not provide reliable governed ingestion. The proposed action (Copy API secrets into a widely shared notebook and paste full responses manually at Regional sporting venue) does not meet the requirement that the source must arrive in governed Delta tables with manageable credential rotation, nor does it constitute continuing operational detection.

Question 897

The warehouse_movement_logs_metro pipeline at Multinational parts distributor has this failure: warehouse_movement_logs_metro bundle deployment fails after someone changes its job task reference. The team observed that CLI output identifies invalid resource or variable configuration. Business requirement: the error must be caught before production resources change. The first acceptance run passed. The current task is to detect future regressions and changes over time. What should be tracked as an operational early warning?

  1. Always run bundle deploy despite failing validation at Multinational parts distributor
  2. Submit a deliberately invalid bundle and confirm promotion is blocked
  3. Monitor validation failures and unsupported resource configuration in warehouse_movement_logs_metro
  4. Include `databricks bundle validate -t prod` before deployment

Explanation

Correct Answer

C:

Monitor validation failures and unsupported resource configuration in warehouse_movement_logs_metro creates recurring visibility into the failure mode (warehouse_movement_logs_metro bundle deployment fails after someone changes its job task reference). Unlike a single successful test, the measure can reveal drift, lag, or new failures after later source and deployment changes while the error must be caught before production resources change.

Incorrect Answers

A:

Deploying invalid configuration bypasses the preflight gate. The proposed action (Always run bundle deploy despite failing validation at Multinational parts distributor) does not meet the requirement that the error must be caught before production resources change, nor does it constitute continuing operational detection.

B:

Submit a deliberately invalid bundle and confirm promotion is blocked is a one-time acceptance check. It does not supply continuing operational detection for warehouse_movement_logs_metro; a passing test cannot substitute for the specific task now required.

D:

Include `databricks bundle validate -t prod` before deployment is a plausible lasting design once chosen, but it does not perform continuing operational detection at this stage. In the warehouse_movement_logs_metro scenario, the observed evidence is that CLI output identifies invalid resource or variable configuration; the requirement remains: the error must be caught before production resources change.

Question 898

A data engineer at E-commerce payments provider is responsible for payment authorization records. The Auto Loader checkpoint issue is that a streaming file reader for payment_authorization_records_coastal reprocesses historical data after every job restart. The supporting evidence is that the streaming query creates a new checkpoint directory on each run. The engineer must ensure that a restart must resume discovered files instead of replaying all existing input. The target approach is approved. The current task is to introduce it safely with bounded scope and rollback. What is the safest migration or deployment sequence?

  1. Restart after a micro-batch and confirm no duplicate file ingestion
  2. Configure cloudFiles with a stable checkpoint path for the payment_authorization_records_coastal stream
  3. Deploy the stream with a durable checkpoint and restart the same logical query
  4. Delete checkpoint state as part of each successful load at E-commerce payments provider

Explanation

Correct Answer

C:

Deploy the stream with a durable checkpoint and restart the same logical query provides a bounded way to introduce the approved solution into payment_authorization_records_coastal and detect an early mismatch. The existing signal is the streaming query creates a new checkpoint directory on each run; a staged change protects the service requirement (a restart must resume discovered files instead of replaying all existing input) and makes rollback practical.

Incorrect Answers

A:

Restart after a micro-batch and confirm no duplicate file ingestion is a one-time acceptance check. It does not supply the controlled implementation step for payment_authorization_records_coastal; a passing test cannot substitute for the specific task now required.

B:

Configure cloudFiles with a stable checkpoint path for the payment_authorization_records_coastal stream is a plausible lasting design once chosen, but it does not perform the controlled implementation step at this stage. In the payment_authorization_records_coastal scenario, the observed evidence is that the streaming query creates a new checkpoint directory on each run; the requirement remains: a restart must resume discovered files instead of replaying all existing input.

D:

Deleting checkpoint progress can cause full replay. The proposed action (Delete checkpoint state as part of each successful load at E-commerce payments provider) does not meet the requirement that a restart must resume discovered files instead of replaying all existing input, nor does it constitute the controlled implementation step.

Question 899

A data engineer at Media postproduction team is responsible for render-job telemetry. The Git branch review issue is that developers edit render_job_telemetry_west production notebooks directly and bypass code review. The supporting evidence is that workspace Git history shows changes without a pull request. The engineer must ensure that releases need traceable approval and a recoverable commit. The target approach is approved. The current task is to introduce it safely with bounded scope and rollback. What is the safest migration or deployment sequence?

  1. Commit and push the change, review it, then deploy the merged revision
  2. Develop in a feature branch with a reviewed pull request before merging
  3. Verify deployed code hash matches the approved merged commit
  4. Copy arbitrary notebook cells into production without a commit at Media postproduction team

Explanation

Correct Answer

A:

Commit and push the change, review it, then deploy the merged revision provides a bounded way to introduce the approved solution into render_job_telemetry_west and detect an early mismatch. The existing signal is workspace Git history shows changes without a pull request; a staged change protects the service requirement (releases need traceable approval and a recoverable commit) and makes rollback practical.

Incorrect Answers

B:

Develop in a feature branch with a reviewed pull request before merging is a plausible lasting design once chosen, but it does not perform the controlled implementation step at this stage. In the render_job_telemetry_west scenario, the observed evidence is that workspace Git history shows changes without a pull request; the requirement remains: releases need traceable approval and a recoverable commit.

C:

Verify deployed code hash matches the approved merged commit is a one-time acceptance check. It does not supply the controlled implementation step for render_job_telemetry_west; a passing test cannot substitute for the specific task now required.

D:

Untracked copies cannot reliably reproduce the approved source. The proposed action (Copy arbitrary notebook cells into production without a commit at Media postproduction team) does not meet the requirement that releases need traceable approval and a recoverable commit, nor does it constitute the controlled implementation step.

Question 900

The product_sales_transactions_east pipeline at Online bicycle retailer has this failure: product_sales_transactions_east downstream transformation needs to run after its governed source table changes. The team observed that the source updates irregularly while cron polling starts empty jobs. Business requirement: data freshness should follow actual table changes rather than wall-clock guesses. 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. Enable the data-driven trigger and run a controlled upstream update
  2. Configure a table-update trigger tied to the supported source table
  3. Modify source and confirm downstream task runs only after detectable update
  4. Schedule a blind daily task when source updates require immediate processing at Online bicycle retailer

Explanation

Correct Answer

A:

Enable the data-driven trigger and run a controlled upstream update provides a bounded way to introduce the approved solution into product_sales_transactions_east and detect an early mismatch. The existing signal is the source updates irregularly while cron polling starts empty jobs; a staged change protects the service requirement (data freshness should follow actual table changes rather than wall-clock guesses) and makes rollback practical.

Incorrect Answers

B:

Configure a table-update trigger tied to the supported source table is a plausible lasting design once chosen, but it does not perform the controlled implementation step at this stage. In the product_sales_transactions_east scenario, the observed evidence is that the source updates irregularly while cron polling starts empty jobs; the requirement remains: data freshness should follow actual table changes rather than wall-clock guesses.

C:

Modify source and confirm downstream task runs only after detectable update is a one-time acceptance check. It does not supply the controlled implementation step for product_sales_transactions_east; a passing test cannot substitute for the specific task now required.

D:

Coarse polling cannot consistently meet data-driven freshness. The proposed action (Schedule a blind daily task when source updates require immediate processing at Online bicycle retailer) does not meet the requirement that data freshness should follow actual table changes rather than wall-clock guesses, nor does it constitute the controlled implementation step.

Certified Data Engineer Associate Practice Tests