The best practice is to use "Complete" as output mode instead of "append" when working with aggregated tables. Since gold layer is work final aggregated tables, the only option with output mode as complete is option E.

Explanation
From the docs:
In Databricks Repos, you can use Git functionality to:
Clone, push to, and pull from a remote Git repository.
Create and manage branches for development work, including merging, rebasing, and resolving conflicts.
Create notebooks—including IPYNB notebooks—and edit them and other files.
Visually compare differences upon commit and resolve merge conflicts.
Source: https://docs.databricks.com/en/repos/index.html

Explanation
With merge , you can avoid inserting the duplicate records. The dataset containing the new logs needs to be deduplicated within itself. By the SQL semantics of merge, it matches and deduplicates the new data with the existing data in the table, but if there is duplicate data within the new dataset, it is inserted.https://docs.databricks.com/en/delta/merge.html#:~:text=With%20merge%20%2C%20you%20can%20a

To return the number of null values in the member_id column, the best option is to use the count_if function, which counts the number of rows that satisfy a given condition. In this case, the condition is that the member_id column is null. The other options are either incorrect or not supported by Spark SQL. Option A will return the number of non-null values in the member_id column. Option B will not work because there is no count_null function in Spark SQL. Option D will not work because there is no null function in Spark SQL. Option E will not work because there is no count_null function in Spark SQL. Reference:
Built-in Functions - Spark SQL, Built-in Functions
count_if - Spark SQL, Built-in Functions