Databricks Databricks-Certified-Data-Engineer-Associate valid study dumps : Databricks Certified Data Engineer Associate Exam

A data engineer is attempting to write Python and SQL in the same command cell and is running into an error. The engineer thought that it was possible to use a Python variable in a select statement. Why does the command fail?

• A. Databricks supports multiple languages but only one per notebook.
• B. Databricks supports one language per cell.
• C. Databricks supports language interoperability in the same cell but only between Scala and SQL.
• D. Databricks supports language interoperability but only if a special character is used.

A data engineer has a bundle with a job resource keyed as etl_job in databricks.yml. After running 'databricks bundle deploy', the job appears in the workspace but has not executed. Which command triggers execution of the deployed job?

• A. databricks bundle execute etl_job
• B. databricks bundle run etl_job
• C. databricks jobs submit --json <config>
• D. databricks bundle deploy --run

Which method should a Data Engineer apply to ensure Workflows are being triggered on schedule?

• A. Scheduled Workflows require an always-running cluster, which is more expensive but reduces processing latency.
• B. Scheduled Workflows can reduce resource consumption and expense since the cluster runs only long enough to execute the pipeline.
• C. Scheduled Workflows process data as it arrives at configured sources.
• D. Scheduled Workflows run continuously until manually stopped.

A data engineering team has two tables. The first table march_transactions is a collection of all retail transactions in the month of March. The second table april_transactions is a collection of all retail transactions in the month of April. There are no duplicate records between the tables.
Which of the following commands should be run to create a new table all_transactions that contains all records from march_transactions and april_transactions without duplicate records?

• A. CREATE TABLE all_transactions AS
  SELECT * FROM march_transactions
  OUTER JOIN SELECT * FROM april_transactions;
• B. CREATE TABLE all_transactions AS
  SELECT * FROM march_transactions
  INNER JOIN SELECT * FROM april_transactions;
• C. CREATE TABLE all_transactions AS
  SELECT * FROM march_transactions
  UNION SELECT * FROM april_transactions;
• D. CREATE TABLE all_transactions AS
  SELECT * FROM march_transactions
  INTERSECT SELECT * from april_transactions;
• E. CREATE TABLE all_transactions AS
  SELECT * FROM march_transactions
  MERGE SELECT * FROM april_transactions;

A data engineer is decommissioning a sandbox schema in Unity Catalog. Some tables are ephemeral staging outputs that can be safety removed entirely, but a few tables point at shared cloud storage used by downstream jobs outside Databricks. The engineer must avoid deleting any shared files when cleaning up catalog objects.
How does Unity Catalog behave when dropping Managed vs External tables?

• A. DROP managed staging tables to remove data and metadata, and DROP external tables to remove only metadata
• B. DROP all tables; Databricks will only remove metadata for both managed and external tables.
• C. DROP managed tables that are ephemeral and DROP external tables; files for both remain for 7 days.
• D. DROP external tables first to delete their files, then DROP managed tables to keep their files for recovery.