How can searches be optimized in Splunk?

Optimizing searches in Splunk is crucial for improving performance and ensuring that results are returned efficiently. Limiting the timeframe is an effective method for search optimization. When a search is focused on a specific timeframe, Splunk can reduce the volume of data it needs to process. This decreases the amount of indexing and scanning required, leading to faster search results and reduced resource consumption.

In contrast, other approaches mentioned in the options may not lead to optimal search performance. For example, using only wildcard searches can lead to longer search times since wildcards require Splunk to evaluate more potential matches, which can significantly slow down the process. Avoiding indexed fields may also lead to inefficiency since indexed fields are optimized for quick access, and not leveraging them means relying on slower searches through unindexed data. Expanding the timeframe would typically increase the amount of data processed, which can negatively affect search performance rather than optimize it.

Thus, focusing the search by narrowing down the timeframe is a strategic way to enhance search efficiency and effectiveness in Splunk.