Use Auto Loader with Schema Enforcement and Schema Evolution in Batch Modes (for Example, Directory Listing or File Notification) to Land Data into UnityCatalog–governed Tables. DATA-ENGINEER-ASSOCIATE Practice Questions (Page 2)

Questions 6–10

1. 6foundation · easy

   How does Auto Loader discover new files in directory listing mode?

   • ABy reading a manifest file that lists all files
   • BBy receiving notifications from a cloud storage event service
   • CBy querying a metadata catalog for file listings
   • DBy scanning the directory structure for files that have not been processed yet
   Reveal Solution Discussion Select an answer first
2. 7foundation · easy

   How do you specify the target Unity Catalog table when writing Auto Loader output?

   • ABy using the storage path of the table
   • BBy using the table ID from the Unity Catalog UI
   • CBy using only the table name, and the default catalog and schema are used
   • DBy using the fully qualified table name in the format `catalog.schema.table`
   Reveal Solution Discussion Select an answer first
4. 8foundation · easy

   What is the primary purpose of Auto Loader in Databricks?

   • ATo incrementally and efficiently ingest new data files from cloud storage as they arrive
   • BTo provide a graphical interface for monitoring cluster performance
   • CTo transform data using Spark SQL after it has been loaded into a Delta table
   • DTo manage access control and governance for Unity Catalog tables
   Reveal Solution Discussion Select an answer first
5. 9foundation · easy

   What is an advantage of file notification mode over directory listing mode?

   • AIt does not require any cloud storage configuration
   • BIt works with on-premises storage
   • CIt automatically handles schema evolution without any configuration
   • DIt provides faster discovery of new files because it reacts to events rather than scanning
   Reveal Solution Discussion Select an answer first
6. 10foundation · easy

   Which statement about Auto Loader batch mode is true?

   • AIt automatically scales out to multiple clusters for each batch
   • BIt processes all available new files in a single batch and then stops until the next trigger
   • CIt requires a continuous streaming query to be active
   • DIt processes data in micro-batches with a configurable interval
   Reveal Solution Discussion Select an answer first