SPLK-1002 Exam Dumps

The stats command is faster and more efficient than the transaction command, especially in large environments. The stats command is used to calculate summary statistics on the events, such as count, sum, average, etc. The stats command can group events by one or more fields or by time buckets. The stats command does not create new events from groups of events, but rather creates new fields with statistical values. The transaction command is used to group events into transactions based on some common characteristics, such as fields, time, or both. The transaction command creates new events from groups of events that share one or more fields. The transaction command also creates some additional fields for each transaction, such as duration, eventcount, startime, etc. The transaction command is slower and more resource-intensive than the stats command because it has to process more data and create more events and fields.

Explanation: As mentioned before, a calculated field is a field that you create based on the value of another field or fields2. A calculated field can be based on extracted fields, which are fields that are extracted from your raw data using various methods such as regular expressions, delimiters or key-value pairs2. Therefore, option B is correct, while options A, C and D are incorrect because they are not types of fields that a calculated field can be based on.

Explanation: The eval command is used to create new fields or modify existing fields based on an expression2. The sort command is used to sort the results by one or more fields in ascending or descending order2. If you want to convert numeric field values to strings and also sort on those values, you should use the sort command first, then use the eval command to convert the values to strings2. This way, the sort command will use the original numeric values for sorting, rather than the converted string values which may not sort correctly. Therefore, option C is correct, while options A, B and D are incorrect.

Explanation: Field aliases are alternative names for fields in Splunk. Field aliases can be used to normalize data across different sources and sourcetypes that have different field names for the same concept. For example, you can create a field alias for src_ip that maps to clientip, source_address, or any other field name that represents the source IP address in different sourcetypes. Field aliases can also be used in lookup file definitions to map fields in your data to fields in the lookup file. For example, you can use a field alias for src_ip to map it to ip_address in a lookup file that contains geolocation information for IP addresses. Field alias names do not replace the original field name, but rather create a copy of the field with a different name. Field alias names are case sensitive when used as part of a search, meaning that src_ip and SRC_IP are different fields.

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. Some of the delimiters that will work with FX are:
Tabs: horizontal spaces that align text in columns.
Pipes: vertical bars that often indicate logical OR operations.
Spaces: blank characters that separate words or symbols.
Therefore, the delimiters A, B, and D will work with FX.

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.

Explanation: In this search, count will appear on the y-axis2. This search uses the chart command to create a chart of the count of events over host for events that have status not equal to 2002. The chart command creates a table with one column for each value of the field after the over clause and one row for each value of the field after the by clause (if any)2. The values in the table are calculated by applying the function before the over clause to the events in each group2. In this case, the chart command creates a table with one column for each host and one row for the count of events for each host. The y-axis of the chart shows the values of the count function applied to each host. Therefore, option C is correct, while options A and B are incorrect because they appear on the x-axis or as labels of the chart.