Working with Lakeflow Jobs

Which TWO of the following statements are true regarding the behavior and capabilities of Databricks Jobs parameters and values?

A data engineer is configuring a Lakeflow Job that processes sensitive customer data. The job must notify the on-call team when a run fails and must also capture the run's output for auditing. Which TWO actions should the engineer take in the Lakeflow Jobs configuration? (Choose two.)

A data engineer has a Lakeflow Job with two tasks: Task1 and Task2. Task2 must run only if Task1 succeeds. The engineer also wants Task2 to be skipped if Task1 fails, but the overall job status should be marked as failed. Which configuration should the engineer use for the dependency between Task1 and Task2?

A data engineer needs to pass the execution date to a job task dynamically. Which feature should they use?

A data engineer needs to configure a Databricks Job containing multiple tasks where downstream tasks should only execute if all upstream parent tasks complete successfully. Which task dependency setting should be configured?

A data engineer is creating a Lakeflow Job that must run a Python script stored in DBFS. The engineer wants to ensure the script is executed with the correct dependencies and environment. Which task type should be used?

A data engineer is building a Lakeflow Job that must process a parameterized date range. The engineer wants to pass start_date and end_date values into a notebook task at runtime and have those values available as widget-like parameters inside the notebook. Which approach should the engineer use?

A data engineering team runs a nightly Lakeflow Job that ingests files from cloud storage, transforms them with a notebook, and then runs a SQL task. The team wants the SQL task to execute only after the notebook transform succeeds, but they do not want the SQL task to wait for a fixed delay. Which Lakeflow Jobs feature should they configure on the SQL task?

Which THREE of the following are benefits of using Delta Live Tables (DLT) for managing your data pipelines?

A data engineer has a Lakeflow Job with three tasks: bronze_ingest, silver_transform, and gold_aggregate. The silver_transform task must run only if bronze_ingest succeeds, and gold_aggregate must run only if silver_transform succeeds. The engineer also wants gold_aggregate to run even if silver_transform fails, so that partial results can be published. Which configuration should the engineer apply to gold_aggregate?

Which of the following is the primary benefit of using a 'Job Cluster' rather than an 'All-Purpose Cluster' for running scheduled data pipelines?

Refer to the exhibit. If 'task1' fails due to a timeout, what happens to 'task2'?

A data engineer is creating a Lakeflow Job that must run a notebook every weekday at 06:00 in the company's local time zone, which is America/New_York. The engineer configures a schedule trigger but the job runs at the wrong time. Which setting should the engineer verify first?

A data engineer has a Lakeflow Job that runs daily. They want to receive an email only when the job fails, not on every run. Which notification configuration should they set?

A data engineer wants to pass a file path from Task A to Task B in a Lakeflow Job. Task A is a notebook that computes the path, and Task B is a notebook that reads from that path. Which mechanism should the engineer use to share the value between tasks?

A data engineer has a Lakeflow Job with a linear dependency chain: Task A, then Task B, then Task C. Task B sometimes fails due to transient errors. The engineer wants Task C to run only if Task B succeeds, but also wants Task B to be retried automatically before considering the job failed. Which configuration should they use?

Refer to the exhibit. When is this job scheduled to run?

A data engineer is setting up a Lakeflow Job that runs a notebook task. The engineer needs the task to always execute even if the upstream task in the workflow fails. Which configuration should be applied to the dependent task's condition?

A data engineer is designing a Databricks Job workflow. Which TWO of the following are valid ways to trigger a Databricks Job?

A data engineer wants to ensure that a Databricks Job task only runs if the preceding task completes successfully, but needs to add a specific timeout threshold for this individual task. Where should this configuration be applied?

A data engineer configures a Lakeflow Job to run a notebook task on a job cluster. The notebook reads a parameter named run_date using the widget API. During a manual run, the engineer wants to supply a specific date without editing the notebook. The job also runs on a nightly schedule where the date should default to the current day. Which approach correctly supplies the parameter for both the manual and scheduled runs?

A data engineer has configured a Databricks Job with multiple dependent tasks forming a linear pipeline. Task A extracts data, Task B transforms it, and Task C loads it into a gold table. The pipeline runs daily. The team notices that if Task B fails due to an intermittent schema validation issue, the entire job run fails, but they want Task C to execute conditionally only if Task B succeeds, while alerting the on-call engineer immediately upon any failure. How should the task dependencies and conditional execution be configured?

A data engineer has a Lakeflow Job with a notebook task that occasionally fails due to transient network errors when reading from an external REST API. The engineer wants the task to automatically retry up to three times, but only for this specific task, without affecting other tasks in the job. What should the engineer do?

A data engineer is building a Lakeflow Job that must run a sequence of tasks across different compute types. The ingest task must run on a job cluster with a specific Spark configuration, the transform task must run as a Delta Live Tables pipeline, and the report task must run on a separate SQL warehouse. Which TWO statements about task-level compute configuration in Lakeflow Jobs are correct? (Choose two.)

A data engineer is building a Lakeflow Job with a task that runs a SQL notebook. The task must run only on weekdays and must be completed before 9 AM. The engineer wants to configure the schedule to meet these requirements. What should the engineer do?

What is the primary function of the 'Retries' setting in a Databricks Job task?

A data engineer needs to configure a Databricks Job containing three distinct tasks: ingest, transform, and report. The transform task must only execute if the ingest task completes successfully, but the report task should execute regardless of whether the transform task succeeds or fails. How should the task dependencies be configured?

A data engineer manages a Lakeflow Job that runs a long-running notebook task on a job cluster. The task occasionally fails due to transient cloud storage errors, and the engineer wants the task to retry automatically without failing the entire job on the first attempt. The engineer also wants to be alerted only if all retries are exhausted. Which configuration should the engineer apply?

When a Data Engineer uses a 'Repair and Rerun' functionality on a failed Databricks Job, what happens?

A data engineer maintains a Lakeflow Job with a scheduled trigger set to run every day at 08:00. The job's source table is refreshed by an upstream process that sometimes finishes later than expected, causing the job to process stale data. The engineer wants the job to start only after the upstream refresh completes, regardless of the clock time, while still preserving the existing 08:00 schedule as a fallback. Which trigger configuration should the engineer implement?