Explanation
The answer is both options A and B
Options C and D are incorrect because there is no command in Spark called execute.sql or delta.sql

Explanation
Answer is Databricks notebooks support automatic change tracking and versioning.
When you are editing the notebook on the right side check version history to view all the changes, every change you are making is captured and saved.

Explanation
The answer is, Provide schema hints.
1.spark.readStream \
2.format("cloudFiles") \
3.option("cloudFiles.format", "csv") \
4.option("header", "true") \
5.option("cloudFiles.schemaLocation", schema_location) \
6.option("cloudFiles.schemaHints", "id int, description string")
7.load(raw_data_location)
8.writeStream \
9.option("checkpointLocation", checkpoint_location) \
10.start(target_delta_table_location)option("cloudFiles.schemaHints", "id int, description string")
# Here we are providing a hint that id column is int and the description is a string When cloudfiles.schemalocation is used to store the output of the schema inference during the load process, with schema hints you can enforce data types for known columns ahead of time.

Explanation
The answer is
1.table_name = "sales"
2.query = f"select * from {schema_name}.{table_name}"
It is always best to use f strings to replace python variables, rather than using string concatenation.