Splunk Core Certified User SPLK-1002 (SPLK-1001) — Questions 301–375

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Option A is wrong because eventstats adds the total_bytes field to every event but does not filter to the top 5 source IPs, and the subsequent stats count by src_ip, dest_ip ignores the total_bytes field entirely, failing to identify the top source IPs. Option B is wrong because it groups by src_ip and dest_ip before sorting, so the sort and head 5 operate on the combined src_ip-dest_ip pairs rather than on the total bytes per source IP, which does not yield the top 5 source IPs by total bytes. Option D is wrong because the top command with limit=5 src_ip by bytes is syntactically invalid (top does not support a by clause for bytes), and the subsequent search index=net sourcetype=netflow is a separate search that does not use the results from the top command, leading to incorrect or no filtering.

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Option A is wrong because exporting to CSV only saves the current results as a static file, not the search logic itself, so the analyst would have to re-create the search each time. Option B is wrong because saving as an alert is intended for triggering actions based on scheduled search results, not for simply re-running the search manually; it adds unnecessary overhead and complexity for a use case that only requires saving the search for future use. Option C is wrong because saving as a dashboard panel embeds the search into a specific dashboard context, which is less flexible for standalone reuse and requires additional dashboard configuration steps.

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Option A is wrong because the `default` attribute on a post-process search does not exist; post-process searches cannot have fallback results defined via an attribute, and they rely entirely on the base search's output. Option C is wrong because resetting the base search to return at least one result is not a valid solution—you cannot force a search to return results if the data doesn't exist, and doing so would corrupt the dashboard's accuracy. Option D is wrong because setting the panel refresh to a higher interval does not address the root cause of the error; it only changes how often the search runs, but if the base search still returns no results, the error will persist on each refresh.

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Option A is wrong because `top` does not sort alphabetically; it sorts by frequency count descending. Option B is wrong because `top` shows the most common values, not those appearing fewer than 5 times; that would require a `where count < 5` after a `stats count` command. Option D is wrong because `top` does not sort by time or show recent events; it aggregates counts over the entire search timeframe and orders by frequency.

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Option A is wrong because including a username in the report name ties the report to an individual, reducing reusability and violating Splunk's recommendation for role-agnostic naming; reports should be shareable across users. Option B is wrong because embedding the current date in the report name creates a new report each day, defeating the purpose of a saved report that can be scheduled to run dynamically with time range modifiers. Option C is wrong because a generic name like 'Daily Report' is ambiguous and does not convey the search's specific purpose, making it difficult to locate or understand in a large knowledge object library.

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Option A is wrong because using only root datasets eliminates the ability to define specialized fields or constraints for different sourcetypes, leading to a flat, inflexible schema that cannot handle heterogeneous log sources without field conflicts. Option C is wrong because creating one data model per sourcetype defeats the purpose of a unified data model, causing duplication of effort, increased maintenance overhead, and inability to correlate data across sourcetypes in a single pivot or report. Option D is wrong because avoiding calculated fields does not address field conflicts; calculated fields are derived from existing fields and do not cause schema collisions, and the real issue is inconsistent field naming across sourcetypes, which normalization resolves.

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Option A is wrong because `| stats count by src_ip | sort - count` does produce a count per source IP and sorts it, but it does not limit the output to the top 5; it would return all source IPs sorted, which is not what the question asks. Option C is wrong because `| sort - count | head 5` is missing the initial `stats` or `top` command to generate the count; `sort - count` would fail because there is no `count` field to sort on unless preceded by a stats command. Option D is wrong because `| table src_ip | head 5` simply displays the first 5 source IPs from the raw events, not the top 5 by traffic volume; it does not perform any aggregation or counting.

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Option A is wrong because removing unnecessary fields reduces the data model's complexity and storage footprint but does not directly address the memory allocation error; the error is about insufficient memory for the build process, not about field count. Option C is wrong because reducing the summary range to less than 7 days may reduce the amount of data to process but does not resolve the underlying memory shortage; the error is about memory, not time range. Option D is wrong because using tstats instead of data model acceleration is a workaround that bypasses acceleration entirely, not a best practice to prevent the memory error; the question asks for a practice to prevent the error, not to avoid acceleration.

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Option A is wrong because the search will return results; the `case` function is valid and will assign priorities based on the conditions. Option C is wrong because the `case` function evaluates conditions sequentially, so errors with value 'critical' get priority 1 and 'warning' get priority 2, not just priority 3. Option D is wrong because `=` is a valid comparison operator in Splunk's `eval` context, equivalent to `==`, so no syntax error occurs.

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Option B is wrong because report acceleration pre-computes and stores aggregated results for a single report or search, but it does not enable multiple panels to share a single base search result; each accelerated report still runs its own search against the index. Option C is wrong because a data model defines a hierarchical, persistent schema over indexed data and is used for pivot-based reporting, not for sharing a live base search result across panels; it requires separate searches or pivot queries to generate data. Option D is wrong because summary indexing saves the results of a search to a summary index for later use, but it requires explicit scheduling and writing to a separate index, and panels would need to search that summary index independently, not directly share the same base search result in real time.

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Option B is wrong because the question explicitly states the auditor role has read access to the 'transactions' index, so index-level permissions are not the issue. Option C is wrong because if the dashboard were private, the analyst could not share it at all; the auditor can see the dashboard (no data), so the dashboard itself is shared. Option D is wrong because there is no 'rbac_perm' privilege in Splunk; dashboards can be set to run as a specific role via 'run as' permissions, but the core issue here is the saved search ownership, not a missing privilege name that doesn't exist.

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Option A is wrong because disabling acceleration forces the dashboard to run raw searches against the full dataset, which would drastically increase query latency and likely break the 30-minute refresh performance requirement. Option C is wrong because the backfill time controls how far back the summary is initially built, not the ongoing disk usage; reducing it only affects the initial build, not the steady-state storage. Option D is wrong because increasing the acceleration time range would cause the summary to cover more data, thereby increasing disk usage and worsening the problem, not solving it.

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Option A is wrong because events that are not indexed would not appear in search results at all, so they cannot have empty status values after a lookup. Option B is wrong because output_fields controls which fields from the lookup file are added to events; incorrect usage would cause missing fields entirely, not empty values for a field that exists. Option C is wrong because duplicate keys in a lookup file cause the lookup to return only the first matching value, not empty values; empty values occur only when no match is found.

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Option B is wrong because `where` can be used with string comparisons; the command supports both string and numeric comparisons, so the issue is not about the command's capability but the data type. Option C is wrong because `inputlookup` is a valid command that loads a lookup file into the search, and it is correctly used here to retrieve the CSV data. Option D is wrong because `table` is a valid command that returns results in a tabular format, and replacing it with `fields` would not resolve the version comparison issue; the problem lies in the comparison logic, not the output command.

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Option A is wrong because inputlookup is a search command that loads the entire lookup file into memory as a dataset, which does not improve performance for field-based lookups and can actually be slower for large files. Option C is wrong because CSV files do not support indexing; indexes are a database concept and Splunk does not create indexes on static lookup files. Option D is wrong because converting CSV to TSV does not change the underlying linear scan behavior; the delimiter has no impact on lookup performance.

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Option B is wrong because Splunk column charts can display multiple columns when the x-axis field has multiple distinct values; the chart type is not inherently limited to one column. Option C is wrong because if the protocol field were not extracted, the chart would show no columns or an error, not a single column with data. Option D is wrong because the time range being too narrow would reduce the number of events but still allow multiple protocol values to appear if they exist in that window; a single column indicates a single distinct value, not insufficient time span.

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Option B is wrong because the `fields` command with a minus sign removes the listed fields, not keeps them; the command removes `_raw` and `_time`, leaving all other fields intact. Option C is wrong because Splunk allows removal of internal fields like `_raw` and `_time` using the `fields` command; no error is generated. Option D is wrong because the `_time` field is not merely emptied but is completely removed from the event data, so it is not available at all.

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Option A is wrong because Ctrl+Enter is not a recognized shortcut for triggering autocomplete; it is commonly used to submit or run a search, not to invoke field suggestions. Option C is wrong because the 'Add to Search' button is used to manually insert a field into the search bar from the field sidebar, but it is not related to enabling or triggering the autocomplete feature. Option D is wrong because there is no user preference or setting under Settings that controls the autocomplete feature; it is always active and cannot be toggled off.

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Option A is wrong because the question states the panel shows no data, not that the index is empty; the index 'web' likely contains events, but the specific field required is missing. Option B is wrong because the bar chart is capable of displaying results if the search returned valid data; the issue is upstream in the search, not in the visualization type. Option C is wrong because the stats command does not require a 'by' clause to calculate aggregate functions like avg; it can compute a single value across all events, and 'host' is a valid default field in Splunk, but the real problem is that 'response_time' does not exist.

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Option A is wrong because increasing the acceleration frequency rebuilds summaries more often, which increases CPU and I/O load and can temporarily use more disk space during rebuilds, but does not reduce the total amount of stored summary data. Option C is wrong because disabling acceleration eliminates all precomputed summaries, which would severely degrade query performance on large datasets, especially for searches over the 90-day range. Option D is wrong because deleting old indexed data removes raw data that may be needed for compliance or historical analysis, and it does not directly address the disk space consumed by the acceleration summaries themselves.

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Option A is wrong because `top method by status` returns the most common values of 'method' for each value of 'status', but it does not show the full distribution of all status values across all method values; it only shows the top method per status, which is a different analytical goal. Option C is wrong because `pivot` is a command used in the Splunk Pivot interface for building data models, not a search command that can be run directly in the search bar; it requires a data model and is not a valid transforming command for this ad-hoc search. Option D is wrong because `chart count over status by method` has incorrect syntax; the correct syntax for the `chart` command is `chart count by status, method` or `chart count over method by status`, but `over status by method` is invalid and would cause a parsing error.

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Option B (`fields *`) is wrong because it only retains or displays all fields in the search results, but does not show their data types or summary statistics. Option C (`eval`) is wrong because it creates new fields or modifies existing ones using expressions, not for listing field names and types. Option D (`rex`) is wrong because it extracts fields using regular expressions from raw event data, not for displaying existing field metadata.

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Option A is wrong because data model names are case-sensitive but can be in any case; uppercase is not required. Option C is wrong because the field 'action' is a common, allowed field in data models; there is no restriction against it. Option D is wrong because the constraint is not defined at the root level incorrectly; the root level is the correct place for constraints, but the syntax is missing the dataset name prefix.

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Option B is wrong because granting write permission on the data model would allow the user to modify its definition, which directly contradicts the requirement to prevent modification. Option C is wrong because there is no 'search' permission for data models; Splunk permissions for knowledge objects are based on read and write, and search access is implicitly granted through read permission. Option D is wrong because assigning the data model to a role's default app controls where the data model appears in the app context, not the user's ability to search or modify it; it does not enforce any permission restrictions.

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Option A is wrong because the lookup command syntax 'lookup assets ip AS dest_ip' is correct: it maps the lookup field 'ip' to the event field 'dest_ip', which matches the definition. Option B is wrong because there is no 'name' field in the lookup definition; the matching field is 'ip', and the lookup command does not require a 'name' argument. Option D is wrong because 'inputlookup' is used to load the entire CSV as a dataset, not to perform field-based matching in a search; the 'lookup' command is the correct command for enriching events with lookup data.

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Option A is wrong because `top user` returns the most frequent users sorted by count, but it does not guarantee a count for every user and is designed for ranking, not for a simple per-user count. Option B is wrong because `timechart count by user` creates a time-based chart, which is unnecessary and adds complexity when the requirement is only for the last hour (the time filter is already applied in the search). Option C is wrong because `chart count by user` produces a statistical chart with default splitting, but it is less straightforward than `stats` and may introduce unwanted formatting or limit the output; `stats` is the canonical command for simple group-by counts.

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Option A is wrong because a dashboard panel is a visualization element that displays real-time or historical data in a dashboard; it does not have scheduling or conditional email capabilities. Option B is wrong because an Alert is designed for real-time or scheduled monitoring with actions, but it is not a report; the question specifically asks for a 'report action,' and alerts are separate entities that can trigger actions based on search results, not report-specific actions. Option C is wrong because a real-time search continuously streams data without a fixed schedule and cannot be configured to run every 5 minutes or send conditional emails based on a count threshold.

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Option A is wrong because the 'run_data_model' capability does not exist; the relevant capability for using data models is 'dm_model' or 'list_data_models', and lacking a capability would typically produce an error message, not empty results. Option C is wrong because if the time range were outside the acceleration summary, `|tstats` with `summariesonly=t` would still return results from the accelerated range (if any) or an empty set, but the question states no results at all, and the user is not using `summariesonly=t`. Option D is wrong because a syntax error in the data model's constraint field would cause the data model to fail to validate or save, not silently return zero results when queried.

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Option A is wrong because `max_matches` does not limit the number of fields output; it limits the number of rows (results) returned per event, while field selection is controlled by the lookup definition's field mapping or the `fields` parameter. Option B is wrong because `max_matches` has nothing to do with creating lookup files; it is a runtime behavior setting for an existing lookup table, not a file creation limit. Option D is wrong because `max_matches` limits results per event, not the total number of events that can be matched; the lookup will still attempt to match all events in the search results.

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Option A is wrong because TSV (tab-separated values) is a valid delimited text format that Splunk can parse as a classic CSV-based lookup when the delimiter is set to tab. Option C is wrong because CSV (comma-separated values) is the primary format for classic CSV-based lookups. Option D is wrong because JSON is not a delimited text format; it is a structured data format that Splunk supports via KV store lookups or as a lookup table file only when using the JSON lookup type, not the classic CSV-based lookup.

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Option B is wrong because the default stanza does not set a `maxGlobalTimeFieldSec` that applies to all indexes; this setting is per-index and is not inherited from the default stanza in a way that would cause an incorrect override. Option C is wrong because the replication factor (set in server.conf or outputs.conf) controls data redundancy across peers, not the time range of events returned by searches. Option D is wrong because the question states that searches against other indexes do not return older events, implying the search time range is not the issue; if the search itself were configured to include older data, it would affect all indexes, not just 'sample_index'.

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Option A is wrong because `chart` without a time-based split does not automatically produce daily buckets; it would require an explicit `by` clause for time, and a column chart is not ideal for time-series data without proper time axis. Option B is wrong because `top user` returns the most frequent users, not a distinct count per day, and `timechart` with a pie chart is invalid since pie charts cannot display time-series data. Option D is wrong because `count` counts all events, not distinct users, and `chart` without a time-based grouping does not produce daily results; a bar chart is also inappropriate for time-series trends.

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Option A is wrong because the `eval` command syntax is correct: `eval memory_MB = memory_bytes / 1024 / 1024` is valid Splunk syntax. Option C is wrong because the field does not need to be 'converted first' in a generic sense—the specific issue is that the commas make it a string, not that the field type is inherently incompatible. Option D is wrong because the division operator does work on string fields that contain numeric values without commas (Splunk auto-converts them), but it fails when the string contains non-numeric characters like commas.

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Option A is wrong because the Patterns tab groups events by common structural patterns (like log formats or timestamps), not raw event content. Option B is wrong because the Statistics tab shows aggregated, computed results (e.g., counts, averages) from transforming commands like stats or timechart, not raw events. Option D is wrong because the Visualization tab renders charts, graphs, or other visual representations of statistical data, not the original raw event text.

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Option A is wrong because Data Summary provides a high-level overview of data sources, sourcetypes, and hosts, but does not allow searching for specific field values like an IP address. Option B is wrong because a Dashboard panel displays pre-configured visualizations based on saved searches, not an interactive tool for ad-hoc investigation of a specific IP. Option C is wrong because Alert actions are triggered by scheduled or real-time searches to send notifications, not designed for manually searching across all data.

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Option B is wrong because restarting Splunk is done with `splunk restart`, not `splunk add forward-server`. Option C is wrong because enabling SSL for forwarding requires additional flags like `-ssl` or configuring `ssl://` in the target URI; this command alone does not enable SSL. Option D is wrong because adding a forwarder to receive data is done on the indexer side with `splunk add receive-port`, not on the forwarder with `splunk add forward-server`.

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Option A is wrong because adding more indexers improves indexing and search distribution, but it does not directly reduce the time to run reports on an accelerated data model; acceleration works by pre-computing summaries on the search head, not by scaling indexers. Option B is wrong because limiting the data model to only the most recent 7 days of data would exclude historical data from reports, which may not meet business requirements; acceleration can be applied to any time range, and the summary size is managed by the acceleration's time range and granularity settings, not by artificially restricting the data model. Option C is wrong because the |accelerate command does not exist in Splunk; acceleration is configured on data models or reports via the 'Acceleration' settings in the UI or through the 'datamodel accelerate' command, and creating a separate summary for each search would be inefficient and is not a supported best practice.

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Option A is wrong because insufficient permissions would cause a data access error or blank results, not a failure to update when the time range changes; permissions are unrelated to time token inheritance. Option C is wrong because the dashboard time picker being set to 'All time' would affect all panels uniformly, not cause a single panel to ignore the picker; the issue is panel-level override, not picker-level setting. Option D is wrong because a panel based on a scheduled report can still respect the time picker if its search uses time tokens; the scheduled report itself does not inherently prevent dynamic time updates.

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Option A is wrong because `sum(user)` attempts to sum string values, which is invalid and will not produce a count of unique users; it would cause an error or return 0. Option C is wrong because `stats count by user` counts the number of events per user, not the number of unique users; it returns a table of users with their event counts, not a single total. Option D is wrong because `top user` lists the most frequent users based on event count, not the count of unique users; it focuses on frequency rather than uniqueness.

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Option A is wrong because the lookup is case-sensitive, so a case mismatch prevents a match, and the correct sales_rep is not returned. Option B is wrong because a case mismatch does not cause an error; the lookup simply fails to match and returns the default value or an empty string. Option D is wrong because when `default_match` is configured, the lookup returns that default value (e.g., 'N/A') instead of an empty string.

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Option A is wrong because `| datamodel info` is not a valid Splunk command; the correct command for viewing details of a specific data model is `| datamodel` with the model name, but it does not list all models or show acceleration status. Option C is wrong because `| datamodel search` is used to search against a data model's fields (e.g., `| datamodel <model_name> search`) and does not list models or show acceleration status. Option D is wrong because `| datamodel show` is not a valid Splunk command; the closest valid command is `| datamodel` with no subcommand, which outputs the data model's XML definition, not a list of all models with acceleration status.

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Option A is wrong because `dc(user)` counts unique users, not hosts; the `by host` clause groups results by host, but the aggregation is on the user field. Option C is wrong because `dc(user)` does not count all users across the entire result set; it counts distinct users per group (per host), not a global total. Option D is wrong because `dc(user)` performs a distinct count, not a sum; summing user IDs would be meaningless and is not what the `dc()` function does.

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Option A is wrong because reducing the number of forwarders would decrease data intake, but it is not a viable solution for a medium-sized enterprise that needs all data; it also does not address the root cause of memory exhaustion during peak hours. Option B is wrong because switching universal forwarders to heavy forwarders would increase resource consumption on the forwarders and potentially add more load to the indexer due to parsing overhead, worsening the memory issue. Option D is wrong because increasing the max memory for the indexer process would exacerbate the out-of-memory condition, likely causing more frequent crashes on a 16 GB system.

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Option A is wrong because role-based time range restrictions typically limit the maximum selectable range (e.g., cannot select 'All time'), but they do not silently shift a selected range like 'Last 24 hours' to only return the last hour; the user would likely see an error or the time picker would be grayed out. Option B is wrong because if an indexer were down, the search would either fail with an error or return partial results, not consistently return only the last hour's events while still returning recent data. Option C is wrong because timezone misconfiguration would shift the displayed timestamps or cause a consistent offset, not truncate the time range to exactly one hour; it would affect all events equally, not limit the range to the last hour.

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Option A is wrong because search-time field extraction cannot derive owner and department from dest_ip without an external data source; it would require complex regex or pattern matching that is not feasible for arbitrary IP-to-asset mapping. Option B is wrong because increasing the number of indexers does not parallelize lookup operations; indexers handle indexing and search distribution, but the bottleneck here is the per-event dbquery call to the SQL database, which is not parallelized by adding indexers. Option D is wrong because using a KV Store lookup populated in real time from the database would still require a live connection and per-event queries, defeating the performance improvement; KV Store is designed for static or periodically refreshed data, not real-time database synchronization.

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Option B is wrong because 'login_failures' scanning too many events would typically cause high disk I/O and memory usage, not necessarily high CPU for few results, and the exhibit shows high CPU relative to results. Option C is wrong because multiple simultaneous searches would show a general system overload across all searches, not a single search with disproportionate CPU usage. Option D is wrong because duplicate SIDs would cause job conflicts or errors, not a performance issue where one search uses high CPU for few results.

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Option A is wrong because field aliases work on any extracted field, not just lookup fields; they rename fields regardless of how the field was created. Option B is wrong because 'src_ip' is already present in the events (as shown by the search), so it is already extracted; no explicit extraction is needed for an alias to work. Option C is wrong because the 'rename' command only renames fields for the duration of a single search, whereas a field alias created in the UI persists across all searches and is the correct method for permanent renaming.