This is the correct answer because it describes what will happen with new records that have the same event_id as an existing record when the query is executed. The query uses the INSERT INTO command to append new records from the view new_events to the table events. However, the INSERT INTO command does not check for duplicate values in the primary key column (event_id) and does not perform any update or delete operations on existing records. Therefore, if there are new records that have the same event_id as an existing record, they will be ignored and not inserted into the table events. Verified Reference: [Databricks Certified Data Engineer Professional], under "Delta Lake" section; Databricks Documentation, under "Append data using INSERT INTO" section.
"If none of the WHEN MATCHED conditions evaluate to true for a source and target row pair that matches the merge_condition, then the target row is left unchanged." https://docs.databricks.com/en/sql/language-manual/delta-merge-into.html#:~:text=If%20none%20of%20the%20WHEN%20MATCHED%20conditions%20evaluate%20to%20true%20for%20a%20source%20and%20target%20row%20pair%20that%20matches%20the%20merge_condition%2C%20then%20the%20target%20row%20is%20left%20unchanged.

Explanation
Answer is Managed Delta table
Anytime a table is created without the Location keyword it is considered a managed table, by de-fault all managed tables DELTA tables Syntax CREATE TABLE table_name ( column column_data_type...)

In the context of cluster performance and resource utilization, a CPU utilization rate of around 75% is generally considered a good indicator of efficient resource usage. This level of CPU utilization suggests that the cluster is being effectively used without being overburdened or underutilized.
* A consistent 75% CPU utilization indicates that the cluster's processing power is being effectively employed while leaving some headroom to handle spikes in workload or additional tasks without maxing out the CPU, which could lead to performance degradation.
* A five Minute Load Average that remains consistent/flat (Option A) might indicate underutilization or a bottleneck elsewhere.
* Monitoring network I/O (Options B and C) is important, but these metrics alone don't provide a complete picture of resource utilization efficiency.
* Total Disk Space (Option D) remaining constant is not necessarily an indicator of proper resource utilization, as it's more related to storage rather than computational efficiency.
References:
* Ganglia Monitoring System: Ganglia Documentation
* Databricks Documentation on Monitoring: Databricks Cluster Monitoring

Explanation
The answer is Records that violate the expectation are dropped from the target dataset and recorded as invalid in the event log.
Delta live tables support three types of expectations to fix bad data in DLT pipelines Review below example code to examine these expectations, Diagram Description automatically generated with medium confidence