https://docs.aws.amazon.com/glue/latest/dg/glue-troubleshooting-errors.html#error-job- bookmarks-reprocess-data

https://docs.aws.amazon.com/redshift/latest/mgmt/data-api.html

Explanation
Split larger files into a greater number of smaller files to distribute the load among the compute re-sources in an active warehouse. This would minimize the processing overhead rather than maximize it.
Rest is recommended Data loading considerations.

Explanation
The recommendations that apply to this scenario are:
Ensure that data files are 100-250 MB (or larger) in size compressed: This recommendation will improve Snowpipe performance by reducing the number of files that need to be loaded and increasing the parallelism of loading. Smallerfiles can cause performance degradation or errors due to excessive metadata operations or network latency.
Verify each value of each unique element stores a single native data type (string or number): This recommendation will improve Snowpipe performance by avoiding data type conversions or errors when loading JSON data into variant columns. Snowflake supports two native data types for JSON elements:
string and number. If an element has mixed data types across different files or records, such as string and boolean, Snowflake will either convert them to string or raise an error, depending on the FILE_FORMAT option.
Create data files that are less than 100 MB and stage them in cloud storage at a sequence greater than once each minute: This recommendation will minimize Snowpipe costs by reducing the number of notifications that need to be sent to Snowpipe for auto-ingestion. Snowpipe charges for notifications based on the number of files per notification and the frequency of notifications. By creating smaller files and staging them at a lower frequency, fewer notifications will be needed.

Explanation
Defining a clustering key on a materialized view is supported and can increase performance in many situations. However, it also adds costs.
If you cluster both the materialized view(s) and the base table on which the materialized view(s) are defined, you can cluster the materialized view(s) on different columns from the columns used to cluster the base table.
You can create a materialized view on shared data.
Also You can use Snowflake's data sharing feature to share a materialized view.
Rest all are correct.