In which of the following scenarios should a data engineer use the MERGE INTO command instead of the INSERT INTO command?

1. A. When the location of the data needs to be changed
2. B. When the target table is an external table
3. C. When the source table can be deleted
4. D. When the target table cannot contain duplicate records
5. E. When the source is not a Delta table

Correct Answer: D
With merge , you can avoid inserting the duplicate records. The dataset containing the new logs needs to be deduplicated within itself. By the SQL semantics of merge, it matches and deduplicates the new data with the existing data in the table, but if
there is duplicate data within the new dataset, it is inserted.https://docs.databricks.com/en/delta/merge.html#:~:text=With%20merge%20%2C
%20you%20can%20avoid%20inserting%20the%20duplicate%20records.&text=The%20dat aset%20containing%20the%20new,new%20dataset%2C%20it%20is%20inserted.

A dataset has been defined using Delta Live Tables and includes an expectations clause:
CONSTRAINT valid_timestamp EXPECT (timestamp > '2020-01-01') ON VIOLATION DROP ROW
What is the expected behavior when a batch of data containing data that violates these constraints is processed?

1. A. Records that violate the expectation are dropped from the target dataset and loaded into a quarantine table.
2. B. Records that violate the expectation are added to the target dataset and flagged as invalid in a field added to the target dataset.
3. C. Records that violate the expectation are dropped from the target dataset and recorded as invalid in the event log.
4. D. Records that violate the expectation are added to the target dataset and recorded as invalid in the event log.
5. E. Records that violate the expectation cause the job to fail.

Correct Answer: C
With the defined constraint and expectation clause, when a batch of data is processed, any records that violate the expectation (in this case, where the timestamp is not greater than '2020-01-01') will be dropped from the target dataset. These dropped records will also be recorded as invalid in the event log, allowing for auditing and tracking of the data quality issues without causing the entire job to fail. https://docs.databricks.com/en/delta-live-tables/expectations.html