Option C is correct. It efficiently filters for events where duration > 2, uses ‘stats count by status’ to aggregate, then sorts the results in descending order by count and limits to the top 5. Option A uses ‘where’ incorrectly (it’s a streaming command, not for search), Option B uses ‘where’ similarly and doesn’t sort, and Option D uses ‘search’ which is less efficient than direct filtering in the base search.

Option B is correct. The ‘eventstats’ command adds the per-customer average as a new field (‘cust_avg’) to each event, then a simple ‘stats avg(cust_avg) by region’ calculates the final result. This is more efficient than performing two separate aggregation passes, which is what the original query and the other options do. It processes the data in a single, more streamlined pipeline.

Option B is correct. The ‘lookup’ command is designed for this exact purpose. It matches the ‘ip’ field from the lookup file with the ‘src_ip’ field in your events (using ‘ip as src_ip’) and outputs the matching ‘threat_group’ value into the results. Option A just loads the lookup file by itself. Option C uses ‘join’, which is resource-intensive and less efficient for simple field enrichment. Option D uses a non-existent ‘OUTPUTNEW’ argument.

Option B is correct. You first need to create a new field (‘total_bps’) that represents the sum of in and out traffic for each event using the ‘eval’ command. Then, you can use ‘timechart’ to sum this new field over time and split the series by interface. Option A attempts to sum an expression within the ‘timechart’ function, which is not valid syntax. Option C creates separate series for in and out traffic, not a combined total. Option D uses ‘stats’ which would not produce a continuous time series chart.

Option C is correct. The most effective way to improve performance is to reduce the number of events being passed into the ‘transaction’ command. Applying strict filters (index, sourcetype, time, keywords) at the beginning of the search limits the dataset. Option A (increasing maxspan) would likely decrease performance by forcing the command to evaluate events over a larger window. Option B (adding sort) adds overhead and is unnecessary for ‘transaction’. Option D changes the fundamental output, as ‘stats values(_raw)’ returns raw text, not a transaction object with duration, eventcount, etc.

Option B is correct. The ‘eval’ command can be used to create a new field (e.g., ‘result_color’) that assigns a color value based on a condition. This field can then be used by the dashboard’s formatting options (like the Simple XML row styling) to apply the color. Option A uses a non-existent ‘highlight’ command. Option C filters out all non-failure events, which is not the requirement. Option D incorrectly removes and re-sets the result field.

Option A is correct. In SPL’s ‘eval’ command, the plus sign (+) is the operator for concatenating strings. Option B uses ‘concat’, which is a function in some languages but not in SPL’s eval. Option C uses a dot (.), which is a concatenation operator in other languages like PHP, but not in SPL. Option D uses ‘mvappend’, which is for creating multi-value fields, not a simple string.

Option D is correct. After a transforming command like ‘stats’ creates new aggregated fields (like ‘avg(response_time)’), you need to use a ‘search’ command to filter based on those new fields. The ‘search’ command can filter on any field present at that point in the pipeline. Option C’s ‘where’ command is a streaming command and cannot be used after a transforming command. Option B is not possible; ‘stats’ does not have a built-in filtering argument. Option A would filter raw event data, not the aggregated results.

Option D is correct. The ‘transaction’ command with ‘maxpause’ is designed to group events (logons) for a ‘user’ where the time between consecutive events is no more than 10 minutes. The ‘keepevicted=true’ argument helps in debugging by showing why events were not included in transactions. Option A with ‘stats’ loses the sequence and order of events. Option B uses ‘maxspan’, which limits the total width of a transaction, not the gap between events. Option C’s ‘streamstats’ could be used to compare fields between consecutive events but is more complex to set up for this specific use case than a well-configured ‘transaction’.

Option C is correct. It efficiently filters for events where duration > 2, uses ‘stats count by status’ to aggregate, then sorts the results in descending order by count and limits to the top 5. Option A uses ‘where’ incorrectly (it’s a streaming command, not for search), Option B uses ‘where’ similarly and doesn’t sort, and Option D uses ‘search’ which is less efficient than direct filtering in the base search.0