Databricks-Certified-Data-Engineer-Associate Exam Dumps : Databricks Certified Data Engineer Associate Exam

The MERGE INTO command is used to perform upserts, which are a combination of insertions and updates, based on a source table into a target Delta table1. The MERGE INTO command can handle scenarios where the target table cannot contain duplicate records, such as when there is a primary key or a unique constraint on the target table. The MERGE INTO command can match the source and target rows based on a merge condition and perform different actions depending on whether the rows are matched or not. For example, the MERGE INTO command can update the existing target rows with the new source values, insert the new source rows that do not exist in the target table, or delete the target rows that do not exist in the source table1.

The INSERT INTO command is used to append new rows to an existing table or create a new table from a query result2. The INSERT INTO command does not perform any updates or deletions on the existing target table rows. The INSERT INTO command can handle scenarios where the location of the data needs to be changed, such as when the data needs to be moved from one table to another, or when the data needs to be partitioned by a certain column2. The INSERT INTO command can also handle scenarios where the target table is an external table, such as when the data is stored in an external storage system like Amazon S3 or Azure Blob Storage3. The INSERT INTO command can also handle scenarios where the source table can be deleted, such as when the source table is a temporary table or a view4. The INSERT INTO command can also handle scenarios where the source is not a Delta table, such as when the source is a Parquet, CSV, JSON, or Avro file5.

To send the Databricks Job owner an email in the case that the Job fails, the best approach is to set up an Alert in the Job page. This way, the Job owner can configure the email address and the notification type for the Job failure event. The other options are either not feasible, not reliable, or not relevant for this task. Manually programming an alert system in each cell of the Notebook is tedious and error-prone. Setting up an Alert in the Notebook is not possible, as Alerts are only available for Jobs and Clusters. There is a way to notify the Job owner in the case of Job failure, so option D is incorrect. MLflow Model Registry Webhooks are used for model lifecycle events, not Job events, so option E is not applicable. References:

Add email and system notifications for job events

Alerts

MLflow Model Registry Webhooks

 A serverless SQL endpoint is a compute resource that is automatically managed by Databricks and scales up or down based on the workload. A serverless SQL endpoint can be used to run queries and dashboards without requiring manual configuration or management. A serverless SQL endpoint is only active when it is needed and shuts down automatically when idle, minimizing the total running time and cost. A serverless SQL endpoint can be created and assigned to a dashboard using the Databricks SQL UI or the SQL Analytics API. References:

Create a serverless SQL endpoint

Assign a SQL endpoint to a dashboard

SQL Analytics API