Databricks-DE-Assoc Working with Lakeflow Jobs Practice Question

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

• ✗

  A SQL warehouse task can only run if the job uses an all-purpose cluster for all other tasks.

  Why it's wrong here

  SQL warehouse tasks run on SQL warehouses, not on all-purpose clusters. There is no requirement that other tasks use an all-purpose cluster. In fact, job clusters are recommended for scheduled workloads to reduce cost. The compute for each task is independent, so a SQL warehouse task can coexist with job cluster tasks and DLT pipeline tasks in the same job.

• ✗

  Task-level compute configuration is only available for notebook tasks, not for DLT pipeline or SQL tasks.

  Why it's wrong here

  Lakeflow Jobs support task-level compute for multiple task types, including notebook tasks, DLT pipeline tasks, and SQL tasks. Restricting compute configuration to notebooks would prevent the orchestration of diverse workloads. The platform allows each task to specify the appropriate compute, such as a DLT pipeline for transformations or a SQL warehouse for queries, enabling end-to-end pipelines in a single job.

• ✓

  A Delta Live Tables pipeline task and a SQL warehouse task can be combined in the same Lakeflow Job with dependencies between them.

  Why this is correct

  Lakeflow Jobs allow dependencies between tasks regardless of their compute type. A DLT pipeline task can run, and upon success, a dependent SQL warehouse task can execute. This is useful when transformation results need to be queried or reported on a SQL warehouse. The job orchestrator manages the dependency graph and triggers each task on its designated compute.

• ✗

  All tasks in a Lakeflow Job must share the same compute; task-level compute is not supported.

  Why it's wrong here

  Lakeflow Jobs explicitly support task-level compute, so this statement is false. Requiring all tasks to share compute would prevent mixing workloads such as a notebook task and a DLT pipeline. The job definition allows each task to specify its own cluster or compute resource, which is essential for pipelines that combine ingestion, transformation, and reporting on different engines.

• ✓

  Each task in a Lakeflow Job can be configured with its own compute, including job clusters, DLT pipelines, and SQL warehouses.

  Why this is correct

  Lakeflow Jobs support per-task compute configuration, allowing a single job to orchestrate heterogeneous workloads. A task can target a job cluster, a Delta Live Tables pipeline, or a SQL warehouse depending on the task type. This enables the ingest task to use a job cluster with custom Spark settings, the transform task to run as a DLT pipeline, and the report task to use a SQL warehouse, all within one job definition.