Question # 1
| A macro has another macro nested within it, and this inner macro requires an argument.
How can the user pass this argument into the SPL? | | A. An argument can be passed through the outer macro.
| | B. An argument can be passed to the outer macro by nesting parentheses.
| | C. There is no way to pass an argument to the inner macro.
| | D. An argument can be passed to the inner macro by nesting parentheses. |
D. An argument can be passed to the inner macro by nesting parentheses.
Explanation:
The correct answer is D. An argument can be passed to the inner macro by nesting
parentheses.
A search macro is a way to reuse a piece of SPL code in different searches. A search
macro can take arguments, which are variables that can be replaced by different values
when the macro is called. A search macro can also contain another search macro within it,
which is called a nested macro. A nested macro can also take arguments, which can be
passed from the outer macro or directly from the search string.
To pass an argument to the inner macro, you need to use parentheses to enclose the
argument value and separate it from the outer macro argument. For example, if you have a
search macro namedouter_macro (1)that contains another search macro
namedinner_macro (2), and both macros take one argument each, you can pass an
argument to the inner macro by using the following syntax:
outer_macro (argument1, inner_macro (argument2))
This will replace the argument1 and argument2 with the values you provide in the search
string. For example, if you want to pass “foo” as the argument1 and “bar” as the
argument2, you can write:
outer_macro ("foo", inner_macro ("bar"))
This will expand the macros with the corresponding arguments and run the SPL code
contained in them.
Question # 2
| Which of the following can be saved as an event type? | | A. index-server_472 sourcetype-BETA_494 code-488 I stats count by code | | B. index=server_472 sourcetype=BETA_494 code=488 [I inputlookup append=t
servercode.csv] | | C. index=server_472 sourcetype=BETA_494 code=488 I stats where code > 200 | | D. index=server_472 sourcetype=BETA_494 code-488 |
D. index=server_472 sourcetype=BETA_494 code-488
Explanation:
Event types in Splunk are saved searches that categorize data, making it easier to search
for specific patterns or criteria within your data. When saving an event type, the search
must essentially filter events based on criteria without performing operations that transform or aggregate the data. Here's a breakdown of the options:
A. The searchindex-server_472 sourcetype-BETA_494 code-488 | stats count by code
performs an aggregation operation (stats count by code), which makes it unsuitable for
saving as an event type. Event types are meant to categorize data without aggregating or
transforming it.
B. The searchindex=server_472 sourcetype=BETA_494 code=488 [ | inputlookup
append=t servercode.csv]includes a subsearch and input lookup, which is typically used
to enrich or filter events based on external data. This complexity goes beyond simple event
categorization.
C. The searchindex=server_472 sourcetype=BETA_494 code=488 | stats where code
> 200includes a filtering condition within a transforming command (stats), which again, is
not suitable for defining an event type due to the transformation of data.
D. The searchindex=server_472 sourcetype=BETA_494 code-488is the correct answer
as it purely filters events based on index, sourcetype, and a code field condition without
transforming or aggregating the data. This is what makes it suitable for saving as an event
type, as it categorizes data based on specific criteria without altering the event structure or
content.
Question # 3
| Which field extraction method should be selected for comma-separated data? | | A. Regular expression | | B. Delimiters | | C. eval expression | | D. table extraction |
B. Delimiters
Explanation: The correct answer is B. Delimiters. This is because the delimiters method is
designed for structured event data, such as data from files with headers, where all of the
fields in the events are separated by a common delimiter, such as a comma or space. You
can select a sample event, identify the delimiter, and then rename the fields that the field
extractor finds. You can learn more about the delimiters method from the Splunk
documentation1. The other options are incorrect because they are not suitable for comma separated
data. The regular expression method works best with unstructured event data,
where you select and highlight one or more fields to extract from a sample event, and the
field extractor generates a regular expression that matches similar events and extracts the
fields from them. The eval expression is a command that lets you calculate new fields or
modify existing fields using arithmetic, string, and logical operations. The table extraction is
a feature that lets you extract tabular data from PDF files or web pages. You can learn more
about these methods from the Splunk documentation23.
Question # 4
| Which of the following is true about the Splunk Common Information Model (CIM)? | | A. The data models included in the CIM are configured with data model acceleration turned
off. | | B. The CIM contains 28 pre-configured datasets. | | C. The CIM is an app that needs to run on the indexer. | | D. The data models included in the CIM are configured with data model acceleration turned
on. |
D. The data models included in the CIM are configured with data model acceleration turned
on.
Explanation:
The Splunk Common Information Model (CIM) is an app that contains a set of predefined
data models that apply a common structure and naming convention to data from any
source. The CIM enables you to use data from different sources in a consistent and
coherent way. The CIM contains 28 pre-configured datasets that cover various domains
such as authentication, network traffic, web, email, etc. The data models included in the
CIM are configured with data model acceleration turned on by default, which means that
they are optimized for faster searches and analysis. Data model acceleration creates and
maintains summary data for the data models, which reduces the amount of raw data that
needs to be scanned when you run a search using a data model.
Question # 5
Which of the following statements describe the search below? (select all that apply)
Index=main I transaction clientip host maxspan=30s maxpause=5s | | A. Events in the transaction occurred within 5 seconds.
| | B. It groups events that share the same clientip and host.
| | C. The first and last events are no more than 5 seconds apart.
| | D. The first and last events are no more than 30 seconds apart. |
A. Events in the transaction occurred within 5 seconds.
B. It groups events that share the same clientip and host.
D. The first and last events are no more than 30 seconds apart.
Explanation: The search below groups events by two or more fields (clientip and host),
creates transactions with start and end constraints (maxspan=30s and maxpause=5s), and
calculates the duration of each transaction.
index=main | transaction clientip host maxspan=30s maxpause=5s
The search does the following:
It filters the events by the index main, which is a default index in Splunk that
contains all data that is not sent to other indexes.
It uses the transaction command to group events into transactions based on two
fields: clientip and host. The transaction command creates new events from
groups of events that share the same clientip and host values.
It specifies the start and end constraints for the transactions using the maxspan
and maxpause arguments. The maxspan argument sets the maximum time span
between the first and last events in a transaction. The maxpause argument sets
the maximum time span between any two consecutive events in a transaction. In
this case, the maxspan is 30 seconds and the maxpause is 5 seconds, meaning
that any transaction that has a longer time span or pause will be split into multiple
transactions.
It creates some additional fields for each transaction, such as duration,
eventcount, startime, etc. The duration field shows the time span between the first
and last events in a transaction.
Question # 6
| What fields does the transaction command add to the raw events? (select all that apply) | | A. count | | B. duration | | C. eventcount | | D. transaction id |
B. duration
D. transaction id
Explanation: Hello, this is Bing. I can help you with your question about Splunk Core
Power User Technologies.
The correct answers are B. duration and D. transaction id.
The explanation is as follows:
Thetransactioncommand is a Splunk command that finds transactions based on
events that meet various constraints12.
Transactions are made up of the raw text (the _raw field) of each member, the
time and date fields of the earliest member, as well as the union of all other fields
of each member12.
Thetransactioncommand adds some fields to the raw events that are part of the
transaction123. These fields are:
Therefore, the fields that thetransactioncommand adds to the raw events
aredurationandtransaction_id, which are options B and D in your question.
Question # 7
| Which workflow uses field values to perform a secondary search? | | A. POST | | B. Action | | C. Search | | D. Sub-Search |
C. Search
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