Splunk Core Certified Power User

References:
https://docs.splunk.com/Documentation/Splunk/8.0.3/Knowledge/FXSelectMeth
odstep The Field Extractor (FX) is a tool that helps you extract fields from your data using delimiters or regular expressions. Delimiters are characters or strings that separate fields in your data. The FX can detect some common delimiters automatically, such as pipes (|), spaces ( ), commas (,), semicolons (;), etc.The FX cannot detect tabs (\t) as delimiters automatically, but you can specify them manually in the FX interface.

One of the valid options to speed up reports is to edit acceleration, which means that you can enable summary indexing or data model acceleration for your reports to improve their performance. Summary indexing allows you to create reports that run over large amounts of data by storing the results of scheduled searches in a summary index and using that index for faster reporting. Data model acceleration allows you to create reports that use data models by creating and storing summaries of the data model datasets and using them for faster reporting. Therefore, option C is correct, while options A, B and D are incorrect because they are not options to speed up reports.

In Splunk, macros that accept arguments are defined with placeholders for those arguments in the format example(var1, var2). In the search example(100,200), "100" and "200" are the values passed for var1 and var2 respectively.

References:
Splunk Docs - Macros

The correct answer is C. _time.
The timechart command creates a time series chart with corresponding table of statistics, with time used as the X-axis.You can specify a split-by field, where each distinct value of the split-by field becomes a series in the chart.In this case, the split-by field is action, which means that the chart will have different lines for different actions, such as accept, reject, or fail.The count function will calculate the number of events for each action in each time bin.
For example, the following image shows a timechart of the count by action for a similar search: As you can see, the x-axis is populated by the_time field, which represents the time range of the search. The y-axis is populated by the count function, which represents the number of events for each action. The legend shows the different values of the action field, which are used to split the chart into different series.
References:
1:timechart - Splunk Documentation
2:Timechart Command In Splunk With Example - Mindmajix
3:timechart command examples - Splunk Documentation

References:
https://docs.splunk.com/Documentation/Splunk/8.0.3/Knowledge/Aboutdatamod
els Data models are collections of datasets that represent your data in a structured and hierarchical way. Data models define how your data is organized into objects and fields. Data models can be composed of one or more of the following datasets: Events datasets: These are the base datasets that represent raw events in Splunk. Events datasets can be filtered by constraints, such as search terms, sourcetypes, indexes, etc. Search datasets: These are derived datasets that represent the results of a search on events or other datasets. Search datasets can use any search command, such as stats, eval, rex, etc., to transform the data. Transaction datasets: These are derived datasets that represent groups of events that are related by fields, time, or both. Transaction datasets can use the transaction command or event types with transactiontype=true to create transactions.

References:
https://docs.splunk.com/Documentation/Splunk/8.0.3/Knowledge/WhatisSplunkk
nowledge Knowledge objects are entities that you create to add knowledge to your data and make it easier to search and analyze. Some examples of knowledge objects are field extractions, field aliases and lookups. Field extractions are methods that extract fields from your raw data using various techniques such as regular expressions, delimiters or key-value pairs. Field aliases are ways to assign alternativenames to existing fields without changing the original field names or values. Lookups are ways to enrich your data with additional information from external sources such as CSV files or databases. The order in which these knowledge objects/configurations are applied is as follows: field extractions, field aliases and then lookups. This means that Splunk first extracts fields from your raw data, then applies any aliases to the extracted fields and then performs any lookups on the aliased fields. Therefore, option B is correct, while options A, C and D are incorrect.

References:
https://docs.splunk.com/Documentation/Splunk/8.0.3/SearchReference/Transact
ion

The following statements about tags are true: tags are based on field/value pairs and tags categorize events based on a search. Tags are custom labels that can be applied to fields or field values to provide additional context or meaning for your data. Tags can be used to filter or analyze your data based on common concepts or themes. Tags can be created by using various methods, such as search commands, configuration files, user interfaces, etc. Some of the characteristics of tags are: Tags are based on field/value pairs: This means that tags are associated with a specific field name and a specific field value. For example, you can create a tag called "alert" for the field name "status" and the field value "critical". This means that only events that have status=critical will have the "alert" tag applied to them. Tags categorize events based on a search: This means that tags are defined by a search string that matches the events that you want to tag. For example, you can create a tag called "web" for the search string sourcetype=access_combined.
This means that only events that match the search string sourcetype=access_combined will have the "web" tag applied to them. The following statements about tags are false: tags are case-insensitive and tags are designed to make data more understandable. Tags are case-sensitive and tags are designed to make data more searchable. Tags are case-sensitive: This means that tags must match the exact case of the field name and field value that they are associated with. For example, if you create a tag called "alert" for the field name

"status" and the field value "critical", it will not apply to events that have status=CRITICAL or Status=critical.
Tags are designed to make data more searchable: This means that tags can help you find relevant events or patterns in your data by using common concepts or themes. For example, if you create a tag called "web" for the search string sourcetype=access_combined, you can use tag=web to find all events related to web activity.

When defining a search macro, the required elements are the name and the definition of the macro. The name is a unique identifier for the macro that can be used to invoke it in other searches. The definition is the search string that the macro expands to when referenced. The arguments, validation expression, and validation error message are optional elements that can be used to customize the macro behavior and input validation.
1: Splunk Core Certified Power User Track, page 9.
2: Splunk Documentation, Define search macros in Settings.