A company has an existing Redshift table which contains all the order information for a product for historical analysis. Currently the timestamp on the table is being used as the sort key. More batches of data were uploaded on the table, but the performance of the queries on the new batches of data are not up to the mark as the prior queries. What needs to be done to ensure that the queries on the new data is optimized?

Answer options:

A.Run the Query optimizer
B.Use the Analyze Compression command
C.Change the sort key for the table
D.Use the VACUUM command

Answer – D
The AWS Documentation mentions the following
For tables with a sort key, the VACUUM command ensures that new data in tables is fully sorted on disk. When data is initially loaded into a table that has a sort key, the data is sorted according to the SORTKEY specification in the CREATE TABLE statement. However, when you update the table, using COPY, INSERT, or UPDATE statements, new rows are stored in a separate unsorted region on disk, then sorted on demand for queries as required. If large numbers of rows remain unsorted on disk, query performance might be degraded for operations that rely on sorted data, such as range-restricted scans or merge joins. The VACUUM command merges new rows with existing sorted rows, so range-restricted scans are more efficient and the execution engine doesn`t need to sort rows on demand during query execution.
Since this is clearly mentioned in the AWS Documentation , all other options are incorrect.
For more information on reclaiming storage please refer to the below URL
https://docs.aws.amazon.com/redshift/latest/dg/t_Reclaiming_storage_space202.html