he approach that can be used to identify the owner of new_table is to review the Owner field in the table's page in Data Explorer. Data Explorer is a web-based interface that allows users to browse, create, and manage data objects such as tables, views, and functions in Databricks1. The table's page in Data Explorer provides various information about the table, such as its schema, partitions, statistics, history, and permissions2. The Owner field shows the name and email address of the user who created or owns the table3. The data engineer can use this information to contact the table owner and request for permission to access the table.
The other options are not correct or reliable for identifying the owner of new_table. Reviewing the Permissions tab in the table's page in Data Explorer can show the users and groups who have access to the table, but not necessarily the owner4. Reviewing the Owner field in the table's page in the cloud storage solution can be misleading, as the owner of the data files may not be the same as the owner of the table5.
There is a way to identify the owner of the table, as explained above, so option E is false.
:
1: Data Explorer | Databricks on AWS
2: Table details | Databricks on AWS
3: Set owner when creating a view in databricks sql - Databricks - 9978
4: Table access control | Databricks on AWS
5: External tables | Databricks on AWS

A data lakehouse is a new paradigm that can be used to simplify and unify siloed data architectures that are specialized for specific use cases. A data lakehouse combines the best of both data lakes and data warehouses, providing a single platform that supports diverse data types, open standards, low-cost storage, high-performance queries, ACID transactions, schema enforcement, and governance. A data lakehouse enables data engineers to build reliable and scalable data pipelines that can serve various downstream applications and users, such as data science, machine learning, analytics, and reporting. A data lakehouse leverages the power of Delta Lake, a storage layer that brings reliability and performance to data lakes. References: What is a data lakehouse?, Delta Lake, Lakehouse: A New Generation of Open Platforms that Unify Data Warehousing and Advanced Analytics

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.
:
1: MERGE INTO | Databricks on AWS
2: [INSERT INTO | Databricks on AWS]
3: [External tables | Databricks on AWS]
4: [Temporary views | Databricks on AWS]
5: [Data sources | Databricks on AWS]