Question 69

A nightly Lakeflow Job ingests data from a partner REST API into a bronze Delta table. The API is occasionally unavailable for a few seconds, causing the `ingest` notebook task to fail with a transient connection error. Today the task fails roughly once a week, and on those mornings the on-call engineer manually re-runs the job. A Structured Streaming workload in the same task also relies on automatic schema-evolution behavior, which Databricks documents as assuming the job runs with retries so the environment is reset and the stream can proceed. You want the task to recover automatically from these transient errors without manual intervention, while keeping all other failure-handling behavior unchanged. What is the most appropriate change?

• AWrap every cell of the notebook in a broad `try/except` that swallows all exceptions and returns success.
• BAdd a task-level retry policy to the `ingest` task so it restarts up to a configured number of times on failure.
• CConvert the job trigger to continuous mode so the workload runs nonstop and never has to be re-run.
• DIncrease the cluster size so the API call completes faster and never times out.