A data engineer runs Amazon Athena queries against data in Amazon S3 and uses the AWS Glue Data Catalog for metadata. Query planning is slow because the S3 dataset has a very large number of partitions. Which actions will reduce Athena planning time and resolve the bottleneck? (Choose two.)

Creating an AWS Glue partition index and enabling partition filtering significantly reduces Athena planning time for datasets with many partitions. The index allows Athena to quickly prune irrelevant partitions, avoiding a full scan of the Glue Data Catalog. Athena partition projection is also effective; it infers partition values from the S3 path structure, bypassing the need to query the Glue Data Catalog for partition metadata, which is a major bottleneck with numerous partitions. Bucketing data can improve query performance but doesn't directly address slow query planning due to a high number of partitions. Converting data to Parquet improves query execution speed by enabling columnar reads and compression, but it doesn't solve the partition planning bottleneck. S3DistCP for combining small objects helps with S3 performance and small file issues, but not directly with the partition metadata lookup problem.