Databricks Certified Data Engineer - Professional

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Explanation:

When using the Databricks Jobs REST API v2.1, complex job dependencies can be defined using the depends_on field inside the task definitions. This field allows for specifying conditional execution based on the outcome of preceding tasks, with options like SUCCESS, FAILED, or SKIPPED to control the flow. This method enables conditional branching, failure handling, and skipped task behavior directly in the job JSON.

Option A is incorrect because the REST API does not execute logic like custom Scala or Python code in the payload; it only defines job configuration.
Option C is incorrect as the Databricks REST API does support conditional task dependencies via the depends_on field.
Option D is incorrect because there is no chain operation in the REST API; dependencies are defined using depends_on.

Explanation:

Option A is incorrect because the REST API does not execute logic like custom Scala or Python code in the payload; it only defines job configuration.
Option C is incorrect as the Databricks REST API does support conditional task dependencies via the depends_on field.
Option D is incorrect because there is no chain operation in the REST API; dependencies are defined using depends_on.

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How can you specify complex job dependencies, such as conditional execution based on the outcome of preceding tasks, using the Databricks REST API?

Real Exam

Embedding custom Scala or Python code in the API payload to evaluate conditions

6.1%

By defining a JSON configuration in the dependencies field for each task

61.6%

Databricks REST API does not support conditional task dependencies; use Databricks Workflows instead.

16.5%

Utilizing the chain operation to link tasks sequentially with conditional checks

15.9%