In a Databricks Asset Bundle project, in the file resources/app.yml, the data engineer would like to deploy a Databricks Apps databricks_app_deployed and Volume volume_deployed and grant the Service Principal behind Databricks Apps permissions to READ and WRITE to the Volume.
How should the data engineer achieve the deployment?

• A.
  
• B.
  
• C.
  
• D.
  

A data engineer wants to automate job monitoring and recovery in Databricks using the Jobs API.
They need to list all jobs, identify a failed job, and rerun it. Which sequence of API actions should the data engineer perform?

• A. Use the jobs/cancel endpoint to remove failed jobs, then recreate them with jobs/create and run the new ones.
• B. Use the jobs/list endpoint to list jobs, then use the jobs/create endpoint to create a new job, and run the new job using jobs/run-now.
• C. Use the jobs/list endpoint to list jobs, check job run statuses with jobs/runs/list, and rerun a failed job using jobs/run-now.
• D. Use the jobs/get endpoint to retrieve job details, then use jobs/update to rerun failed jobs.

A new data engineer notices that a critical field was omitted from an application that writes its Kafka source to Delta Lake. This happened even though the critical field was in the Kafka source.
That field was further missing from data written to dependent, long-term storage. The retention threshold on the Kafka service is seven days. The pipeline has been in production for three months.
Which describes how Delta Lake can help to avoid data loss of this nature in the future?

• A. Delta Lake automatically checks that all fields present in the source data are included in the ingestion layer.
• B. Ingestine all raw data and metadata from Kafka to a bronze Delta table creates a permanent, replayable history of the data state.
• C. The Delta log and Structured Streaming checkpoints record the full history of the Kafka producer.
• D. Delta Lake schema evolution can retroactively calculate the correct value for newly added fields, as long as the data was in the original source.
• E. Data can never be permanently dropped or deleted from Delta Lake, so data loss is not possible under any circumstance.

A data engineer is building a streaming data pipeline to ingest JSON files from cloud storage into a Delta Lake table. The pipeline must process files incrementally, handle schema evolution automatically, ensure exactly-once processing, and minimize manual infrastructure management.
How should the data engineer fulfill these requirements?

• A. Use traditional Spark Structured Streaming with Auto Loader, manually configuring checkpoints location and enabling schema inference with "mergeSchema"= "true"
• B. Use Auto Loader in batch mode with a daily job to overwrite the Delta table.
• C. Use Lakeflow Spark Declarative Pipelines with a static DataFrame read, merge schema with spark.conf.set ("spark.databricks.delta.schema.autoMerge.enabled", "true")
• D. Use Lakeflow Spart Declarative Pipelines with Auto Loader and enabling schema inference with
  "cloudFiles.schemaEvolutionMode"= "addNewColumns"