You are working on a process to load external CSV files into a delta table by leveraging the COPY INTO command, but after running the command for the second time no data was loaded into the table name, why is that?
1.COPY INTO table_name
2.FROM 'dbfs:/mnt/raw/*.csv'
3.FILEFORMAT = CSV

• A.COPY INTO only works one time data load
• B.Run REFRESH TABLE sales before running COPY INTO
• C.COPY INTO did not detect new files after the last load
• D.Use incremental = TRUE option to load new files
• E.COPY INTO does not support incremental load, use AUTO LOADER

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You are looking to process the data based on two variables, one to check if the department is supply chain or check if process flag is set to True

• A.if department == "supply chain" | if process == TRUE:
• B.if department == "supply chain" or process:
• C.if department = "supply chain" | process:
• D.if department == "supply chain" or process = TRUE:
• E.if department == "supply chain" | process == TRUE:

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A Databricks job has been configured with 3 tasks, each of which is a Databricks notebook. Task A does not depend on other tasks. Tasks B and C run in parallel, with each having a serial dependency on Task A.
If task A fails during a scheduled run, which statement describes the results of this run?

• A.Because all tasks are managed as a dependency graph, no changes will be committed to the Lakehouse until all tasks have successfully been completed.
• B.Tasks B and C will attempt to run as configured; any changes made in task A will be rolled back due to task failure.
• C.Unless all tasks complete successfully, no changes will be committed to the Lakehouse; because task A failed, all commits will be rolled back automatically.
• D.Tasks B and C will be skipped; some logic expressed in task A may have been committed before task failure.
• E.Tasks B and C will be skipped; task A will not commit any changes because of stage failure.

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A nightly job ingests data into a Delta Lake table using the following code:

The next step in the pipeline requires a function that returns an object that can be used to manipulate new records that have not yet been processed to the next table in the pipeline.
Which code snippet completes this function definition?
def new_records():

• A.return spark.readStream.table("bronze")
• B.return spark.readStream.load("bronze")
  
• C.return spark.read.option("readChangeFeed", "true").table ("bronze")
• D.

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Which statement describes Delta Lake optimized writes?

• A.A shuffle occurs prior to writing to try to group data together resulting in fewer files instead of each executor writing multiple files based on directory partitions.
• B.Optimized writes logical partitions instead of directory partitions partition boundaries are only represented in metadata fewer small files are written.
• C.An asynchronous job runs after the write completes to detect if files could be further compacted; yes, an OPTIMIZE job is executed toward a default of 1 GB.
• D.Before a job cluster terminates, OPTIMIZE is executed on all tables modified during the most recent job.

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