Which combination of actions should a database specialist take to improve the cluster’s performance?

Explanations:

Modifying Kinesis Data Firehose to use a larger buffer size and to load data using the COPY command does not address performance directly. The real-time data processing workflow (using Firehose) can be improved by using staging tables, but this option isn’t a complete solution for improving cluster performance.

Streaming real-time data into temporary tables first allows for staging data before it is inserted into permanent tables. This reduces the load on the cluster and allows batch processing to take place during off-peak times, improving overall performance.

Splitting input files into multiple files corresponding to the number of slices in Redshift optimizes the parallel processing capability of the COPY command. This leads to faster data loading as Redshift distributes the load across multiple slices.

Theparallelparameter is not a valid option in the COPY command. The COPY command automatically handles parallel loading based on the number of slices in the cluster, so this option does not improve performance.

Optimizing SQL queries to use sort keys helps Redshift efficiently organize data on disk. This reduces the amount of data scanned during query execution, improving the performance of analytics queries.

Using temporary tables in analytics queries can actually improve performance by reducing the complexity of operations on permanent tables. Temporary tables are optimized for short-lived data and can improve query execution times.