A company is building an analytics platform with Amazon S3 as the data lake and Amazon Redshift as the data warehouse. The firm intends to use Amazon Redshift Spectrum to query S3 data. Which actions will PRODUCE THE FASTEST query performance? (Select two.)

Using a columnar storage file format like Parquet or ORC significantly improves Redshift Spectrum query performance because it allows Redshift Spectrum to read only the necessary columns for a query, reducing the amount of data scanned from S3. Partitioning the data based on common query predicates (e.g., date, region) allows Redshift Spectrum to prune irrelevant partitions, scanning only a subset of the data in S3. These two strategies directly minimize the data scanned, which is the primary factor in Redshift Spectrum performance and cost. Compressing files to 1-5 GB is generally good practice for S3 data lakes, but the specific size range isn't the primary driver for fastest Redshift Spectrum performance compared to columnar formats and partitioning. Splitting data into files less than 10 KB creates too many small files, leading to increased overhead and slower performance due to excessive metadata operations. Using non-splittable file formats prevents parallel processing, severely hindering performance.