Data Engineer Associate · 16% of the exam

Working with Lakeflow Jobs: free practice questions

5 sample questions from our 47-question bank for this domain — answers and explanations included. These are the same scenario-based style as the real Databricks exam.

1. A data engineer wants to run a Databricks Workflow on a recurring schedule but needs to ensure that if a run is still in progress when the next scheduled trigger fires, the new run does NOT start until the previous one completes. Which Workflow setting should the engineer configure?

  • A. Set the job's maximum concurrent runs to 1✓ Correct
  • B. Enable the 'Skip if already running' checkbox in the schedule configuration
  • C. Configure the job cluster with autoscaling to handle overlapping runs
  • D. Set a retry policy with zero retries and a long timeout
Explanation

Setting maximum concurrent runs to 1 ensures only one instance of the job can execute at a time; if a scheduled trigger fires while a run is active, the new run is queued rather than started immediately. There is no native 'Skip if already running' checkbox in Databricks Workflows — this is a distractor conflating behavior from other orchestrators. Autoscaling compute does not control concurrency at the job scheduling level. A retry policy with zero retries and a long timeout only affects failure handling, not concurrent execution.

2. A data engineering team manages a Delta Live Tables pipeline in Production mode that runs on a schedule. They want to implement a comprehensive observability strategy. A new engineer proposes the following four actions: (I) Use DLT data quality expectations with 'FAIL UPDATE' to halt the pipeline when critical data quality rules are violated, and configure a job failure notification to alert the on-call engineer. (II) Set the DLT pipeline to Development mode in production to keep the cluster warm and reduce latency. (III) Create a Databricks SQL Alert on a query that checks the row counts of DLT output tables after each run to detect silent data loss. (IV) Rely solely on Spark driver logs to detect pipeline failures, without configuring any Workflow notifications. Which combination of actions represents BEST PRACTICES for production pipeline observability?

  • A. I and III only✓ Correct
  • B. I, III, and IV only
  • C. II and III only
  • D. I, II, and III only
Explanation

Actions I and III represent best practices; II and IV do not. Action I is correct: using 'FAIL UPDATE' expectations stops the pipeline when critical data quality is violated, and pairing it with a job failure notification ensures the on-call team is immediately alerted—this is the recommended DLT observability pattern. Action III is correct: a SQL Alert monitoring row counts after each run catches 'silent failures' where the pipeline succeeds technically but produces empty or drastically reduced output, which expectations alone may not catch. Action II is wrong: using Development mode in production is a significant anti-pattern. Development mode does not automatically retry on failures and keeps the cluster alive between runs in a way not suited for production reliability; Production mode should always be used in production. Action IV is wrong: relying solely on driver logs requires someone to proactively check logs, which is not a scalable observability strategy; automated notifications are essential for production pipelines. Therefore, only Option A (I and III only) is correct.

3. A senior data engineer is reviewing a team's Delta Live Tables implementation. The pipeline is configured in Continuous mode and processes streaming data from cloud storage. The team reports that the pipeline cluster restarts frequently, which they believe is causing data gaps. Which THREE of the following are valid reasons why a DLT pipeline running in Continuous mode might restart its cluster? (Select THREE)

  • A. A new version of the pipeline code is published and the pipeline detects the change✓ Correct
  • B. The underlying cloud provider reclaims a spot instance used by the pipeline cluster✓ Correct
  • C. A streaming query encounters an unhandled exception that exceeds the configured retry threshold✓ Correct
  • D. The pipeline automatically scales down to zero nodes when no new data arrives for 10 minutes
  • E. A new Databricks Runtime version is applied to the pipeline cluster through an automated platform update
  • F. The pipeline is switched from Continuous mode to Triggered mode while it is actively running
Explanation

Options A, B, and C are correct: Publishing a new version of the pipeline code causes DLT to restart the pipeline cluster to apply the changes (A). Spot instance preemption by the cloud provider is a common cause of unexpected cluster restarts in any Databricks job, including DLT pipelines (B). If a streaming query encounters a fatal unhandled exception and retries are exhausted, DLT will restart the cluster as part of its recovery mechanism (C). Option D is incorrect because DLT Continuous mode pipelines do not scale to zero when idle — the cluster remains running and polls for new data; autoscaling to zero is not a feature of DLT continuous pipelines. Option E is incorrect because Databricks Runtime updates on running clusters do not happen automatically mid-run; runtime version changes require the pipeline to be stopped and restarted manually by the user. Option F is incorrect because switching pipeline modes is a configuration change that requires the pipeline to be stopped first; you cannot toggle between modes on an actively running pipeline.

4. A data engineer needs to configure alerting for a production Databricks Workflow so that the on-call team receives a notification whenever a job run fails. Which of the following is the correct place to configure this within the Databricks Workflow UI?

  • A. In the cluster configuration panel, under the 'Notifications' tab for the attached job cluster
  • B. In the job's settings under 'Email notifications', specifying addresses to alert on failure✓ Correct
  • C. In the Databricks SQL Alerts section, creating a new alert with a failure condition query
  • D. In the Unity Catalog audit log settings, enabling job failure events
Explanation

Option B is correct: Databricks Workflows provides a built-in 'Email notifications' section in the job settings where you can configure email addresses to be notified on job start, success, or failure — this is the standard mechanism for alerting on workflow failures. Option A is incorrect because cluster configuration panels do not contain notification settings; notifications are a job-level construct. Option C is incorrect because Databricks SQL Alerts are for query-result-based alerting in the SQL analytics context, not for Workflow job status notifications. Option D is incorrect because Unity Catalog audit logs capture governance events and are not a mechanism for operational alerting on job failures.

5. A job must run automatically every day at 6 AM. What should you configure?

  • A. A time-based (cron) schedule on the job✓ Correct
  • B. A one-time manual trigger
  • C. A Delta table constraint
  • D. A cluster autoscaling policy
Explanation

A scheduled (cron) trigger runs the job automatically on the defined cadence. A manual trigger (B) requires a person, and constraints/autoscaling (C, D) don't schedule runs.

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Working with Lakeflow Jobs — Free Data Engineer Associate Practice Questions | DataCertPrep — Certification Prep