SPLK-1004 Splunk Core Certified Advanced Power User Exam Questions and Answers

When the base search of a dashboard panel with post-processing searches is refreshed, the panels with these post-processing searches are refreshed automatically (Option C). Post-processing searches inherit the scope and results of the base search, and when the base search is updated or rerun, the post-processed results are recalculated to reflect the latest data.

Using the tstats command with summariesonly=false instructs Splunk to return results from both summarized (accelerated) data and non-summarized (raw) data. This can be useful when you need a comprehensive view of the data that includes both the high-performance summaries provided by data model acceleration and the detailed granularity of raw data.

The Search head (Option B) in Splunk architecture is responsible for initiating and coordinating search activities across a distributed environment. When a search is run, the search head parses the search query, distributes the search tasks to the appropriate indexers (which hold the actual data), and then consolidates the results retrieved by the indexers. The search head is the component that interacts with the user, presenting the final search results

Form inputs in Splunk dashboards can dynamically impact the panels using inline searches by allowing a token in the search to be replaced by a form input value (Option D). This capability enables dashboard panels to update their content based on user interaction with the form elements. When a user makes a selection or enters data into a form input, the corresponding token in the search string of a dashboard panel is replaced with this value, effectively customizing the search based on user input. This feature makes dashboards more interactive and adaptable to different user needs or questions.

A report qualifies for acceleration in Splunk if it involves fewer than 100,000 events in the search results and uses transforming commands in the search string (Option A). Transforming commands aggregate data, making it more suitable for acceleration by reducing the dataset's complexity and size, which in turn improves the speed and efficiency of report generation.

The values function in the stats command in Splunk is used to return a sorted list of unique field values (Option A). This function is particularly useful for summarizing data by listing all unique values of a specified field across the events returned by the search, which can provide insights into the diversity and distribution of the data associated with that field.

The process for tokenizing event data in Splunk is best described as breaking the event data up by major breakers and then further breaking it up by minor breakers (Option B). Major breakers typically identify the boundaries of events, while minor breakers further segment the event data intofields. This hierarchical approach to tokenization allows Splunk to efficiently parse and structure the incoming data for analysis.

The correct syntax to exclude all entries contained in the lookup file baditems.csv from search results is NOT [inputlookup baditems.csv]. This syntax uses a subsearch with the inputlookup command to retrieve the contents of the baditems.csv lookup file and then uses the NOT operator to exclude those results from the main search. This approach is efficient for filtering out unwanted data based on a predefined list of criteria stored in a lookup file.

In a Splunk search containing a subsearch, the inner subsearch executes first (Option B). The result of the subsearch is then passed to the outer search. This is because the outer search often depends on the results of the inner subsearch to complete its execution. For example, a subsearch might be used to identify a list of relevant terms or values which are then used by the outer search to filter or manipulate the main dataset.

The typeof function in Splunk returns a string that represents the data type of the evaluated expression. The potential string results include "Number", "String", and "Null" (Option C). These indicate whether the evaluated expression is a numerical value, a string, or a null value, respectively, helping users understand the data types they are working with in their searches andscripts.

The erex command in Splunk is used to generate regular expressions based on example data, and these generated regular expressions can then be edited and utilized with the rex command in subsequent searches (Option A). The erex command is helpful for users who may not be familiar with regular expression syntax, as it provides a starting point that can be refined and customized with rex for more precise field extraction.

When using a nested search macro in Splunk, an argument value can be passed to the inner macro by specifying the argument in the outer macro's invocation (Option A). This allows the outer macro to accept arguments from the user or another search command and then pass those arguments into the inner macro, enabling dynamic and flexible macro compositions that can adapt based on input parameters.

Contextual drilldown (Option D) is the type of drilldown that allows passing a value from a user click (e.g., from a table row or chart element) into another dashboard or an external page. This feature enables the creation of interactive dashboards where clicking on a specific element dynamically updates another part of the dashboard or navigates to a different page with relevant information, using the clicked value as a context for the subsequent view.

The fieldsummary command in Splunk generates statistical summaries of fields in the search results, including the count of events that contain the field (count) and the distinct count of field values (dc). These summaries provide insights into the prevalence and distribution of fields within the dataset, which can be valuable for understanding the data's structure and content. Standard deviation (stdev) and mean (mean) are not directly provided by fieldsummary but can be calculated using other commands like stats for fields that contain numerical data.

The eventstats command in Splunk is used to compute and add summary statistics to all events in the search results, similar to the stats command, but without grouping the results into a single event(Option C). This command adds the computed summary statistics as new fields to each event, allowing those fields to be used in subsequent search operations or for display purposes. Unlike the transaction command, which groups events into transactions, eventstats retains individual events while enriching them with statistical information.

The coalesce function in Splunk is used to evaluate each argument in order and return the first non-null value. This function can be used within an eval expression to create a new field in the results set, which will contain the first non-null value from the list of fields provided as arguments to coalesce. This makes it particularly useful in situations where data may be missing or inconsistently populated across multiple fields, as it allows for a fallback mechanism to ensure that some value is always presented.

Wildcards can be used in the from clause of the tstats command in Splunk (Option C). The from clause specifies the data model or dataset from which to retrieve the statistics, and using wildcards here allows users to query across multiple data models or datasets that share a common naming pattern, making the search more flexible and encompassing.