This query is the most efficient and direct. It filters at the search head (index=web_logs action=failure), uses a relative time modifier (earliest=-24h), and then uses stats to count failures grouped by src_ip, finally sorting to show the highest counts first. The other options use less efficient commands like regex or overly complex charting for the stated goal.

The transaction command is a stateful, memory-intensive command that must scan and group events in order, often causing performance bottlenecks. For grouping events by a common field (like session_id) to list related actions, using ‘stats values(_raw) as events by session_id’ or ‘streamstats’ to analyze sequences is typically far more efficient and scalable.

Splunk alert definitions allow you to set a trigger condition based on a field value (e.g., avg > 3) and then apply a duration condition like ‘For at least X consecutive results’. This configuration ensures the alert only fires when the threshold is breached consistently across multiple scheduled runs, preventing false positives from transient spikes.

The ‘case’ function is the most readable and efficient way within eval to perform multi-condition categorization. It evaluates conditions in order and returns the corresponding value for the first true condition. The final ‘true()’ acts as a default catch-all. The nested if() statement (option B) works but is less maintainable. Options C and D use incorrect functions.

Automatic lookups are defined via configuration files (transforms.conf and props.conf). This method ensures the lookup is performed at search time for any event matching the specified source, sourcetype, or host, without requiring users to manually add the lookup command. It’s the correct approach for transparent, persistent data enrichment.

The ‘rex’ command is designed for regex field extraction. Using a named capturing group ‘(?pattern)’ is the standard and most efficient way to extract a value into a new field. The regex “\\d{10}” matches exactly ten digits. The eval with replace (option D) can work but is generally less performant and clear for pure extraction than ‘rex’.

This pipeline correctly uses ‘bin’ to group events into weekly buckets, ‘stats’ to sum revenue per week, and then the ‘lag’ function (often with streamstats) to bring the previous period’s value into the current event for comparison. While the syntax for lag/over can vary, this structure represents the correct logic. Option B’s ‘next’ function looks forward, not backward.

This is a common issue with real-time or near-real-time reporting. If a report runs at 9:15 AM for the 9:00-10:00 AM bin, only 15 minutes of data exists for that bin, which may look like a dip or zero. The solution often involves adjusting the report schedule or the search’s time range (e.g., ending at ‘earliest=-1h@h’ to align with the completed hour).

Joining large datasets using ‘join’ or subsearches is notoriously inefficient in Splunk. The most performant alternative is to use ‘tstats’ against a common data model that has already accelerated the relevant fields. tstats operates on indexed metadata, which is orders of magnitude faster than searching raw event data. Options C and D reduce accuracy, and B is not a Splunk solution.

This query is the most efficient and direct. It filters at the search head (index=web_logs action=failure), uses a relative time modifier (earliest=-24h), and then uses stats to count failures grouped by src_ip, finally sorting to show the highest counts first. The other options use less efficient commands like regex or overly complex charting for the stated goal.0