[2026年02月21日] SPLK-1002 PDFで最近更新された問題です集試験点数を伸ばそう
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Splunk SPLK-1002試験は、Splunkを使用してデータを分析および監視する専門知識を証明したい個人を対象としています。この試験は、Splunk Core Certified User認定を取得し、実際にプロダクション環境でSplunkを使用した経験があるSplunkユーザーを対象としています。SPLK-1002試験は、候補者がSplunkを使用して検索パフォーマンスを最適化し、高度なダッシュボードとレポートを作成し、一般的な問題をトラブルシューティングする能力を測定します。
SPLK-1002認定試験は、Splunkを定期的に使用する個人の高度な知識とスキルをテストするように設計されています。この認定は、IT業界で非常に尊敬されており、多くの雇用主によってSplunkの候補者の専門知識の検証として認識されています。この認定を獲得することで、新しいキャリアの機会を開き、Splunkで働く個人の収益の可能性を高めることができます。
質問 # 82
A calculated field maybe based on which of the following?
- A. Lookup tables
- B. Regular expressions
- C. Fields generated within a search string
- D. Extracted fields
正解：D
解説：
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.
質問 # 83
Which group of users would most likely use pivots?
- A. Administrators
- B. Architects
- C. Users
- D. Knowledge Managers
正解：C
解説：
Reference:
A pivot is a tool that allows you to create reports and dashboards using data models without writing any SPL commands2. You can use pivots to explore, filter, split and visualize your data using a graphical interface2. Pivots are designed for users who want to analyze and report on their data without having to learn the SPL syntax or the underlying structure of the data2. Therefore, option A is correct, while options B, C and D are incorrect because they are not the typical group of users who would use pivots.
質問 # 84
Which of the following commands support the same set of functions?
- A. transaction, chart, timechart
- B. stats, eval, table
- C. stats, chart, timechart
- D. search, where, eval
正解：C
質問 # 85
These kinds of fields are identified in you data at INDEX time.
- A. Default fields
- B. Data-specific fields
正解：A
質問 # 86
Complete the search, .... | _____ failure>successes
- A. If
- B. Where
- C. Search
- D. Any of the above
正解：B
解説：
The where command can be used to complete the search below.
... | where failure>successes
The where command is a search command that allows you to filter events based on complex or custom criteri a. The where command can use any boolean expression or function to evaluate each event and determine whether to keep it or discard it. The where command can also compare fields or perform calculations on fields using operators such as >, <, =, +, -, etc. The where command can be used after any transforming command that creates a table or a chart.
The search string below does the following:
It uses ... to represent any search criteria or commands before the where command.
It uses the where command to filter events based on a comparison between two fields: failure and successes.
It uses the greater than operator (>) to compare the values of failure and successes fields for each event.
It only keeps events where failure is greater than successes.
質問 # 87
When using the transaction command, how are evicted transactions identified?
- A. Closed_txn field is set to o, or false.
- B. Txn_field is set to 1, or true.
- C. Max_txn field is set to O, or false.
- D. open_txn field is set to 1, or true.
正解：A
解説：
The transaction command is a Splunk command that finds transactions based on events that meet various constraints1.
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 member1.
The transaction command adds some fields to the raw events that are part of the transaction12. These fields are:
duration: The difference, in seconds, between the timestamps for the first and last events in the transaction12.
eventcount: The number of events in the transaction12.
closed_txn: A Boolean field that indicates whether the transaction is closed or evicted2. A transaction is closed if it meets one of the following conditions: maxevents, maxpause, maxspan, or startswith2. A transaction is evicted if it does not meet any of these conditions and exceeds the memory limit specified by maxopentxn or maxopenevents23.
Therefore, evicted transactions can be distinguished from non-evicted transactions by checking the value of the closed_txn field. The closed_txn field is set to 0, or false, for evicted transactions and 1, or true for non-evicted, or closed, transactions23.
質問 # 88
What are the expected results for a search that contains the command | where A=B?
- A. Events that contain the string value A=B.
- B. Events where values of field are equal to values of field B.
- C. Events where field A contains the string value B.
- D. Events that contain the string value where A=B.
正解：B
解説：
The correct answer is C. Events where values of field A are equal to values of field B.
The where command is used to filter the search results based on an expression that evaluates to true or false. The where command can compare two fields, two values, or a field and a value. The where command can also use functions, operators, and wildcards to create complex expressions1.
The syntax for the where command is:
| where <expression>
The expression can be a comparison, a calculation, a logical operation, or a combination of these. The expression must evaluate to true or false for each event.
To compare two fields with the where command, you need to use the field names without any quotation marks. For example, if you want to find events where the values for the field A match the values for the field B, you can use the following syntax:
| where A=B
This will return only the events where the two fields have the same value.
The other options are not correct because they use different syntax or fields that are not related to the where command. These options are:
A) Events that contain the string value where A=B: This option uses the string value where A=B as a search term, which is not valid syntax for the where command. This option will return events that have the literal text "where A=B" in them.
B) Events that contain the string value A=B: This option uses the string value A=B as a search term, which is not valid syntax for the where command. This option will return events that have the literal text "A=B" in them.
D) Events where field A contains the string value B: This option uses quotation marks around the value B, which is not valid syntax for comparing fields with the where command. Quotation marks are used to enclose phrases or exact matches in a search2. This option will return events where the field A contains the string value "B".
Reference:
where command usage
Search command cheatsheet
質問 # 89
What is the relationship between data models and pivots?
- A. Pivots and data models have no relationship.
- B. Pivots provide the datasets for data models.
- C. Data models provide the datasets for pivots.
- D. Pivots and data models are the same thing.
正解：C
解説：
The relationship between data models and pivots is that data models provide the datasets for pivots. 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. Pivots are user interfaces that allow you to create
data visualizations that present different aspects of a data model. Pivots let you select options from menus and
forms to create charts, tables, maps, etc., without writing any SPL code. Pivots use datasets from data models
as their source of data. Pivots and data models are not the same thing, as pivots are tools for visualizing data
models. Pivots do not provide datasets for data models, but rather use them as inputs.
Therefore, only statement A is true about the relationship between data models and pivots.
質問 # 90
In the Field Extractor, when would the regular expression method be used?
- A. When events contain comma-separated data.
- B. When events contain JSON data.
- C. When events contain table-based data.
- D. When events contain unstructured data.
正解：D
解説：
Explanation
The correct answer is C. When events contain unstructured data.
The regular expression method works best with unstructured event data, such as log files or text messages, where the fields are not separated by a common delimiter, such as a comma or space1. You select a sample event and highlight one or more fields to extract from that event, and the field extractor generates a regular expression that matches similar events in your dataset and extracts the fields from them1. The regular expression method provides several tools for testing and refining the accuracy of the regular expression. It also allows you to manually edit the regular expression1.
The delimiters method is designed for structured event data: data from files with headers, where all of the fields in the events are separated by a common delimiter, such as a comma or space1. You select a sample event, identify the delimiter, and then rename the fields that the field extractor finds1. This method is simpler and faster than the regular expression method, but it may not work well with complex or irregular data formats1.
Reference:
1: Build field extractions with the field extractor - Splunk Documentation
質問 # 91
Select this in the fields sidebar to automatically pipe you search results to the rare command
- A. top values by time
- B. events with this field
- C. rare values
- D. top values
正解：C
解説：
The fields sidebar is a panel that shows the fields that are present in your search results2. The fields sidebar
has two sections: selected fields and interesting fields2. Selected fields are fields that you choose to display in
your search results by clicking on them in the fields sidebar or by using the fields command2. Interesting fields
are fields that appear in at least 20 percent of events or have high variability among values2. For each field in
the fields sidebar, you can select one of the following options: events with this field, rare values, top values by
time or top values2. If you select rare values, Splunk will automatically pipe your search results to the rare
command, which shows the least common values of a field2. Therefore, option B is correct, while options A,
C and D are incorrect because they do not pipe your search results to the rare command.
質問 # 92
What is needed to define a calculated field?
- A. Event type
- B. Eval expression
- C. Data model
- D. Regular expression
正解：B
解説：
A calculated field in Splunk is created using an eval expression, which allows users to perform calculations or transformations on field values during search time.
References:
Splunk Docs - Calculated fields
質問 # 93
During the validation step of the Field Extractor workflow:
Select your answer.
- A. You can validate where the data originated from
- B. You cannot modify the field extraction
- C. You can remove values that aren't a match for the field you want to define
正解：C
解説：
During the validation step of the Field Extractor workflow, you can remove values that aren't a match for the field you want to define2. The validation step allows you to review and edit the values that have been extracted by the FX and make sure they are correct and consistent2. You can remove values that aren't a match by clicking on them and selecting Remove Value from the menu2. This will exclude them from your field extraction and update the regular expression accordingly2. Therefore, option A is correct, while options B and C are incorrect because they are not actions that you can perform during the validation step of the Field Extractor workflow.
質問 # 94
How could the following syntax for the chart command be rewritten to remove the OTHER category? (select all that apply)
- A. | chart count over CurrentStanding by Action usenull-f useother-t
- B. | chart count over CurrentStanding by Action useother=f
- C. | chart count over CurrentStanding by Action limit-10
- D. | chart count over CurrentStanding by Action limit=10 useother=f
正解：B、D
解説：
In Splunk, when using the chart command, the useother parameter can be set to false (f) to remove the
'OTHER' category, which is a bucket that Splunk uses to aggregate low-cardinality groups into a single group to simplify visualization. Here's how the options break down:
A: | chart count over CurrentStanding by Action useother=fThis command correctly sets the useother parameter to false, which would prevent the 'OTHER' category from being displayed in the resulting visualization.
B: | chart count over CurrentStanding by Action usenull=f useother=tThis command has useother set to true (t), which means the 'OTHER' category would still be included, so this is not a correct option.
C: | chart count over CurrentStanding by Action limit=10 useother=fSimilar to option A, this command also sets useother to false, additionally imposing a limit to the top 10 results, which is a way to control the granularity of the chart but also to remove the 'OTHER' category.
D: | chart count over CurrentStanding by Action limit-10This command has a syntax error (limit-10 should be limit=10) and does not include the useother=f clause. Therefore, it would not remove the 'OTHER' category, making it incorrect.
The correct answers to rewrite the syntax to remove the 'OTHER' category are options A and C, which explicitly set useother=f.
質問 # 95
How is a macro referenced in a search?
- A. By enclosing the macro name in backtick characters (').
- B. By using the macroname command.
- C. By enclosing the macro name in single-quote characters (').
- D. By using the macro command.
正解：A
解説：
The correct answer is C. By enclosing the macro name in backtick characters (`).
A macro is a way to reuse a piece of SPL code in different searches. A macro can take arguments, which are
variables that can be replaced by different values when the macro is called.A macro can also contain another
macro within it, which is called a nested macro1.
To reference a macro in a search, you need to enclose the macro name in backtick characters (). For example,
if you have a macro namedmy_macro` that takes one argument, you can reference it in a search by using the
following syntax:
|my_macro(argument)| ...
This will replace the macro name and argument with the SPL code contained in the macro definition. For
example, if the macro definition is:
[my_macro(argument)] search sourcetype=$argument$
And you reference it in a search with:
index=main |my_macro(web)| stats count by host
This will expand the macro and run the following SPL code:
index=main | search sourcetype=web | stats count by host
References:
Use search macros in searches
質問 # 96
Data model fields can be added using the Auto-Extracted method. Which of the following statements describe
Auto-Extracted fields? (select all that apply)
- A. Auto-Extracted fields can be added if they already exist in the dataset with constraints.
- B. Auto-Extracted fields can have their data type changed.
- C. Auto-Extracted fields can be given a friendly name for use in Pivot.
- D. Auto-Extracted fields can be hidden in Pivot.
正解：A、B、C、D
解説：
Data model fields are fields that describe the attributes of a dataset in a data model2. Data model fields can be
added using various methods such as Auto-Extracted, Evaluated or Lookup2. Auto-Extracted fields are fields
that are automatically extracted from your raw data using various techniques such as regular expressions,
delimiters or key-value pairs2. Auto-Extracted fields can be hidden in Pivot, which means that you can choose
whether to display them or not in the Pivot interface2. Therefore, option A is correct. Auto-Extracted fields
can have their data type changed, which means that you can specify whether they are strings, numbers,
booleans or timestamps2. Therefore, option B is correct. Auto-Extracted fields can be given a friendly name
for use in Pivot, which means that you can assign an alternative name to them that is more descriptive or
user-friendly than the original field name2. Therefore, option C is correct. Auto-Extracted fields can be added
if they already exist in the dataset with constraints, which means that you can include them in your data model
even if they are already extracted from your raw data by applying filters or constraints to limit the scope of
your dataset2. Therefore, option D is correct.
質問 # 97
What does the fillnull command replace null values with, it the value argument is not specified?
- A. 0
- B. N/A
- C. NaN
- D. NULL
正解：A
解説：
Reference:
https://answers.splunk.com/answers/653427/fillnull-doesnt-work-without-specfying-a-field.html
質問 # 98
The macro weekly_sales (2) contains the search string:
index=games | eval ProductSales = $Price$ * $AmountSold$
Which of the following will return results?
- A. 'weekly_sales (3.99, 10)'
- B. 'weekly sales (3)'
- C. 'weekly_sales($3.995, $108)'
- D. 'weekly sales (3.99, 10)'
正解：A
解説：
To use a search macro in a search string, you need to place a back tick character (`) before and after the macro
name1. You also need to use the same number of arguments as defined in the macro2. The macro weekly sales
(2) has two arguments:PriceandAmountSold. Therefore, you need to provide two values for these arguments
when you call the macro.
The option A is incorrect because it uses parentheses instead of back ticks around the macro name. The option
B is incorrect because it uses underscores instead of spaces in the macro name. The option D is incorrect
because it uses spaces instead of commas to separate the argument values.
Reference:1Use search macros in searches - Splunk Documentation2Define search macros in Settings - Splunk
Documentation
質問 # 99
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