Free Splunk Power User Exam Flashcards

Use chart with an aggregation and a split-by field, such as chart count OVER status BY host. The OVER field forms rows; BY creates separate series or columns.

timechart bins events by _time and returns time-series statistics. Use an explicit span, such as timechart span=1h count BY host, when the interval matters.

eval assigns an expression to a field, for example eval total=price*quantity. A later command can filter or aggregate total; eval itself does not remove events.

Use where with an eval-style expression, such as where bytes_out>bytes_in. A search predicate treats the right side of field=value as a value, not another field name.

search accepts field-value terms and * wildcards, while where evaluates expressions and uses functions such as like(field,"prefix%"). Choose based on the comparison needed.

Pipeline order matters: a later extraction can introduce or change field values after fillnull has run. Extract first, then fill the remaining nulls in that result set.

If no event has a non-null value for that field, Splunk may not include it in the result schema, so fillnull cannot discover it automatically. Naming the field in fillnull can create and fill it explicitly.

A transaction groups related events into one result while retaining the constituent events. Use a shared field and, when appropriate, time or boundary constraints.

maxspan limits elapsed time from the earliest to the latest event in one transaction. It is a whole-transaction window, not the gap between neighbors.

maxpause limits the time gap between consecutive events in one transaction. A group may span longer overall if each neighboring gap stays within the limit.

Group by a stable identifier, for example transaction session_id, and add time constraints if IDs can be reused. A time window alone does not identify a user or session.

startswith defines the event condition that begins a transaction; endswith defines its closing condition. Both can be combined with grouping fields and time limits.

transaction adds eventcount and duration. eventcount counts its events; duration is the elapsed time between its earliest and latest event.

Use stats count BY id when a compact aggregate is enough. transaction retains raw constituent events and relationship boundaries, so it generally costs more memory.

Commands such as stats replace the original events with aggregate rows. Run transaction while individual time-stamped events are still available, then summarize its output if needed.

Use it for consistently structured events whose values are separated by a common character, such as a comma or pipe. Name and validate the resulting positions.

Use regex when the event text has variable surrounding text or multiple patterns and simple positions are unreliable. Validate against examples that could produce false matches.

It creates a reusable search-time field extraction associated with a source or source type. It does not retroactively rewrite indexed raw events.

One matching example may hide false positives or missed variants. Check representative and edge events before saving a reusable extraction.

A source field must exist in search results. Extraction identifies the value; an alias copies its field name and a calculated field derives a new value from it.

It exposes an existing field under another name, letting searches use a common field name. It does not parse an absent value from raw text or compute a formula.

Create a calculated field when the new value requires an eval expression, such as a unit conversion. Use an alias when the existing value only needs another field name.

Splunk applies field aliases after extraction and before calculated fields, so a calculated field can use an alias already present in its search-time context.

Calculated fields are processed before automatic lookups in the search-time operation sequence. Derive the value later in the SPL pipeline if it depends on a lookup field.

A field alias can expose src_ip as src without changing the vendor's indexed event. Confirm the source field is extracted for the intended source type first.

A tag labels a field-value pair, including an eventtype value. It helps searches refer to a meaningful category without changing the underlying field value.

An event type is a named search definition that classifies matching events. A tag is a label assigned to a field-value pair or event type.

Save a search that identifies the event class as an event type, then use its name for consistent classification and tagging across searches.

Event types are evaluated before tags in Splunk's search-time sequence. Define the event type using searchable event criteria, then tag it if useful.

An event type can define a search matching both conditions. A tag can then label that event type for a broader category.

A search macro: a named, reusable fragment of SPL that Splunk expands into a search before execution. It centralizes repeated search logic; it is not a stored result set.

Put the macro name or parameterized call between backtick characters in the SPL search. Splunk substitutes its definition at that location.

Arguments substitute caller-supplied values into named variables in the macro definition, so one reusable search fragment can serve related cases.

The argument name is surrounded by dollar signs in the definition. Callers pass the corresponding argument values in the macro invocation.

Check its defined argument names, the values and quoting supplied by the call, and the final expanded search. A valid macro name alone does not guarantee valid expansion.

A GET workflow action: it opens a URL with selected event context passed as URL parameters. Choose it when the destination expects a link-style request.

It sends selected event values to a configured URI in an HTTP POST request. Choose it when the destination expects submitted form-style data.

A Search workflow action: it opens a secondary Splunk search built from values in the selected event, so an analyst can pivot into related events.

The configured field or field pattern must match data in that event. Otherwise there is no relevant event value for the action to pass.

Use a Search workflow action that inserts the selected event's IP field into a secondary search. A GET or POST action instead targets a web URI.

A data model defines curated datasets, constraints, and fields. Pivot uses those definitions to build reports without requiring the analyst to write SPL directly.

A child inherits its parent's constraints and fields and can narrow the event set or add fields. The root establishes the broader starting set.

It restricts the dataset to events with that field, helping Pivot users work with records that have the information a report needs.

Use the Data Model Editor to add root or child datasets and define their constraints and fields. The management page handles model-level settings such as permissions.

No. Acceleration builds summaries to speed eligible searches and pivots; the model's constraints and field definitions still determine what the dataset represents.

The Common Information Model (CIM): it supplies shared field names, tags, and data-model categories so searches and apps can analyze similar events from different products consistently.

No. Its models provide the target schema; your data must be mapped with suitable extractions, aliases, lookups, calculated fields, event types, or tags.

Field names normalize attributes, while tags help select the relevant event class. A matching field name alone does not establish that an event belongs in a particular model.

Check the model's event constraints and required tags, then confirm the source events expose the expected normalized fields. A successful raw search is not sufficient.

A dashboard or search built for a CIM category can analyze the new source alongside other sources once its events meet the model's tags and field definitions.