SPLK-1004 Sample Questions Answers

To treat a multivalue field product="a, b, c, d" in Splunk, the correct command is ...| makemv delim="," product (Option D).The makemv command with the delim argument specifies the delimiter (in this case, a comma) to split the field values into a multivalue field. This allows for easier manipulation and analysis of each value within the product field as separate entities.

Predefined drilldown tokens in Splunk vary by visualization type (Option B). These tokens are placeholders that capture dynamic values based on user interactions with dashboard elements, such as clicking on a chart segment or table row. The specific tokens available and their meanings can differ depending on the type of visualization, as each visualization type may present and interact with data differently.

Distributable streaming commands in Splunk can run on both search heads and indexers (Option D). These commands operate on each event independently and can be distributed across indexers for parallel execution, which enhances search efficiency and scalability. This category includes commands like search, where, eval, and many others that do not require the entire dataset to be available to produce their output.

The foreach command in Splunk is used to apply a processing step to each field in a set of related fields, making it ideal for performing repetitive tasks across multiple fields without having to specify each field individually. This command can process a template of commands or functions to apply to each specified field, thereby streamlining operations that need to be applied uniformly across multiple data points.

A cascading input is used as a way to filter other input selections within a dashboard or form (Option C). It enables a dynamic user interface where the selection made in one input (e.g., a dropdown menu) determines the available options in another input. This setup allows for more intuitive and relevant user interactions, as each choice narrows down the subsequent options to ensure they are contextually appropriate.

Repeating JSON data structures within a single event in Splunk are extracted as multivalue fields (Option C). Multivalue fields allow a single field to contain multiple distinct values, which is common with JSON data structures that include arrays or repeated elements. Splunk's field extraction capabilities automatically recognize and parse these structures, allowing users to work with each value within the multivalue field for analysis and reporting

The four types of event actions in Splunk are eval, link, change, and clear (Option C). These actions can be used in dashboard panel configurations to dynamically interact with or manipulate event data based on user inputs or other criteria. Eval is used for calculating fields, link for creating hyperlinks, change for modifying field values, and clear for removing field values or other data elements.

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 output of an append command in Splunk search results is added to the end of the search results (Option D). The append command is used to concatenate the results of a subsearch to the end of the current search results, effectively extending the result set with additional data. This can be particularly useful for combining related datasets or adding contextual information to the existing search results.

To generate a summary index containing a count of events by productId, the correct search command would be | stats count by productId (Option A). This command aggregates the events by productId, counting the number of events for each unique productId value. The stats command is a fundamental Splunk command used for aggregation and summarization, making it suitable for creating summary data like counts by specific fields.

When Splunk's lookup feature finds fewer than the minimum matches specified for each lookup value, it returns the default value NULL for those unmatched entries until the minimum match threshold is reached (Option A). This behavior ensures that lookups return consistent and expected results, even when the available data does not meet the specified criteria for a minimum number of matches.

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 result of the xyseries command in Splunk is to transform a stats-like output into chart-like output (Option B). The xyseries command restructures the search results so that each row represents a unique combination of x and y values, suitable for plotting in a chart, making it easier to visualize complex relationships between multiple data points.

To disable the inspect button on a dashboard panel in Splunk, you can set the link.inspect.visible attribute to 0 (Option B) in the panel's source code. This attribute controls the visibility of the inspect button, and setting it to 0 hides the button, preventing users from accessing the search inspector for that panel.

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.

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.

When referencing multiple CSS files in a Splunk dashboard view (within Simple XML), the correct approach is to include separate stylesheet attributes for each CSS file. The syntax for this would be similar to (Option C). This method allows the dashboard to load and apply the styles from both CSS files, enhancing the dashboard's visual appearance and user interface design.