This is the correct answer because the code uses the dropDuplicates method to remove any duplicate records within each batch of data before writing to the orders table. However, this method does not check for duplicates across different batches or in the target table, so it is possible that newly written records may have duplicates already present in the target table. To avoid this, a better approach would be to use Delta Lake and perform an upsert operation using mergeInto. Verified References: [Databricks Certified Data Engineer Professional], under "Delta Lake" section; Databricks Documentation, under "DROP DUPLICATES" section.

Comprehensive and Detailed In-Depth Explanation:
Databricks provides differentpermission levelsto control access to clusters. The correctminimal permission required for viewing driver logs isCAN VIEW.
Databricks Cluster Permission Levels:
* CAN ATTACH TO:
* Allows users toattach notebooks to a clusterbutdoes notallow them toview logs.
* Not sufficientfor viewing driver logs.
* CAN MANAGE:
* Grantsfull controlover the cluster, including starting, stopping, and editing configurations.
* Too broad for this requirement.
* CAN VIEW (Correct Answer):
* Allows users toview cluster details, logs, and statusbutnot modifyany configurations.
* Minimal required permission for viewing logs.
* CAN RESTART:
* Grants permission torestart the cluster, but doesnot include log access.
* Not sufficientfor viewing logs.
Conclusion:
Theminimalpermission needed to allow the development team toview driver logsisCAN VIEW.
References:
* Databricks Cluster Permissions Documentation

Explanation
The events are that a data engineer, User A, has promoted a new pipeline to production by using the REST API to programmatically create several jobs, and a DevOps engineer, User B, has configured an external orchestration tool to trigger job runs through the REST API. Both users authorized the REST API calls using their personal access tokens. The workspace audit logs are logs that record user activities in a Databricks workspace, such as creating, updating, or deleting objects like clusters, jobs, notebooks, or tables. The workspace audit logs also capture the identity of the user who performed each activity, as well as the time and details of the activity. Because these events are managed separately, User A will have their identity associated with the job creation events and User B will have their identity associated with the job run events in the workspace audit logs. Verified References: [Databricks Certified Data Engineer Professional], under
"Databricks Workspace" section; Databricks Documentation, under "Workspace audit logs" section.