If Task A fails in a Databricks job with dependencies, what happens to Tasks B and C?

When Task A fails in a Databricks job that has dependencies, the behavior of Tasks B and C is governed by the transactional nature of Delta Lake, which ensures data integrity. In this scenario, since Task A is a prerequisite for both Tasks B and C, they will be skipped. The failure of Task A means that its execution did not complete successfully, and thus it cannot provide the necessary output or state that Tasks B and C require to operate correctly.

Additionally, any changes that Task A might have partially succeeded in making prior to its failure would not be committed to the Lakehouse, ensuring that the data remains in a consistent state. Therefore, the integrity of the overall job is maintained by preventing subsequent tasks from executing when their prerequisites have not been satisfied. This behavior is critical in data engineering, where maintaining the correctness and reliability of data operations is essential.

In this context, the correct answer reflects the expectation that dependent tasks do not proceed when their prerequisite task fails, thereby preserving the integrity of the operations being conducted within the jobs.