SPLK-1004 Sample Questions & Answers

The statement is false. A fundamental Splunk search optimization principle is to filter data as early as possible. Filtering commands should be placed before transforming commands to reduce the number of events that the resource-intensive transforming command needs to process. This significantly improves search performance.

The eventstats command is the correct choice because it calculates a statistical result (like percentile) across a dataset and appends that result to every event without altering the original event structure. By using by service, it calculates the percentile within each service group and adds the cpu_percentile field to each corresponding event. stats would remove the original events, and streamstats would calculate a running percentile, which is not what was requested.

While rex could work, Splunk provides a more specialized and efficient command, kvform, for exactly this type of extraction. kvform is designed to extract key-value pairs from structured text formats. This approach is more robust and performant than a general-purpose regex because it's optimized for this specific task and doesn't require crafting a complex regex pattern. It correctly defines the delimiters and will extract all pairs present in the event.

For cascading inputs to work, the search for the dependent input (host) must use the token set by the parent input (region). The most common error is that the search populating the host dropdown does not filter based on the selected region token (e.g., $tok_region$). Without this filter, the search doesn't know how to narrow down the host list. The handler on the region input is necessary to trigger the update, but the host search itself must be correctly configured to use the token.

This is the most performant alternative. Using multiple subsearches or a join is very inefficient. This stats-based approach filters for all relevant events in a single pass (status=404 OR status=503), then uses stats dc(status) by host to count the number of unique statuses for each host. A final where status_count=2 filters this small statistical result set to only the hosts that have experienced both types of errors. This avoids the overhead of subsearches and is a core optimization pattern.

This is the correct approach. It first creates a clean numeric field numeric_amount by stripping all non-digit and non-decimal point characters. Then, it uses the case function to apply the correct conversion factor based on the currency symbol found in the original raw_amount field. This multi-step process is robust and handles the data cleaning and conditional logic correctly. The tonumber function ensures the final field is numeric.

This is the most appropriate configuration. The search ... | stats count, values(service) as affected_services aggregates the total count and collects the names of the services. The alert condition search count > 10 correctly triggers the alert based on the aggregated count. The webhook payload {"total_errors": $result.count$, "top_services": $result.affected_services$} correctly uses the $result. $ token syntax to populate the JSON payload with the values from the single result row generated by the stats command. The quotes around the tokens are removed to ensure the values are inserted correctly as a number and an array.

This is the correct syntax. The tags field is a JSON array. spath uses curly braces {} to denote array indexing, which is 0-based. The 'project' tag is the second element in the array, so its index is 1. The query then accesses the value key within that array element. tags{1}.value correctly navigates to the value "apollo".

This is the most direct and correct implementation. It takes the macro argument $ip_addr$ and assigns it to a temporary field validated_ip. The where command then filters the event stream. If the IP matches any of the private CIDR ranges, the event (and the validated_ip field) is passed through. If it doesn't match, the event is discarded. This effectively returns the IP only when it's valid, as required. This approach integrates seamlessly into a search pipeline.

This statement is false. Report acceleration works by creating a summary of searches that contain transforming commands (like stats, chart, timechart, top). The streamstats command is a streaming command, not a transforming one. It calculates statistics on an event-by-event basis without transforming the entire result set. Because of this, searches containing streamstats do not qualify for report acceleration.