The correct command to create a new table that contains all records from two tables without duplicate records is to use the UNION operator. The UNION operator combines the results of two queries and removes any duplicate rows. The INNER JOIN, OUTER JOIN, and MERGE operators do not remove duplicate rows, and the INTERSECT operator only returns the rows that are common to both tables. Therefore, option B is the only correct answer. Reference: Databricks SQL Reference - UNION, Databricks SQL Reference - JOIN, Databricks SQL Reference - MERGE, [Databricks SQL Reference - INTERSECT]

The processingTime option specifies a time-based trigger interval for fixed interval micro-batches. This means that the query will execute a micro-batch to process data every 5 seconds, regardless of how much data is available. This option is suitable for near-real time processing workloads that require low latency and consistent processing frequency. The other options are either invalid syntax (A, C), default behavior (B), or experimental feature (E). Reference: Databricks Documentation - Configure Structured Streaming trigger intervals, Databricks Documentation - Trigger.

In Databricks, you can use different languages within the same notebook by using magic commands. Magic commands are special commands that start with a percentage sign (%) and allow you to change the behavior of the cell. To use SQL within a cell of a Python notebook, you can add %sql to the first line of the cell. This will tell Databricks to interpret the rest of the cell as SQL code and execute it against the default database. You can also specify a different database by using the USE statement. The result of the SQL query will be displayed as a table or a chart, depending on the output mode. You can also assign the result to a Python variable by using the -o option. For example, %sql -o df SELECT * FROM my_table will run the SQL query and store the result as a pandas DataFrame in the Python variable df. Option A is incorrect, as it is possible to use SQL in a Python notebook using magic commands. Option B is incorrect, as attaching the cell to a SQL endpoint is not necessary and will not change the language of the cell. Option C is incorrect, as simply writing SQL syntax in the cell will result in a syntax error, as the cell will still be interpreted as Python code. Option E is incorrect, as changing the default language of the notebook to SQL will affect all the cells, not just one. Reference: Use SQL in Notebooks - Knowledge Base - Noteable, [SQL magic commands - Databricks], [Databricks SQL Guide - Databricks]

Explanation
In a Delta Live Table pipeline running in Continuous Pipeline Mode, when you click Start to update the pipeline, the following outcome is expected: All datasets defined using STREAMING LIVE TABLE and LIVE TABLE against Delta Lake table sources will be updated at set intervals. The compute resources will be deployed for the update process and will be active during the execution of the pipeline. The compute resources will be terminated when the pipeline is stopped or shut down. This mode allows for continuous and periodic updates to the datasets as new data arrives or changes in the underlying Delta Lake tables occur. The compute resources are provisioned and utilized during the update intervals to process the data and perform the necessary operations.