RELIABLE SPLK-1002 TEST PRACTICE - REALISTIC QUIZ SPLUNK LATEST SPLUNK CORE CERTIFIED POWER USER EXAM EXAM QUESTIONS VCE

Reliable SPLK-1002 Test Practice - Realistic Quiz Splunk Latest Splunk Core Certified Power User Exam Exam Questions Vce

Reliable SPLK-1002 Test Practice - Realistic Quiz Splunk Latest Splunk Core Certified Power User Exam Exam Questions Vce

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We're committed to ensuring you have access to the best possible SPLK-1002 questions. We offer SPLK-1002 dumps in PDF, web-based practice tests, and desktop practice test software. We provide these SPLK-1002 questions in all three formats since each has useful features of its own. If you prepare with Splunk Core Certified Power User Exam (SPLK-1002) actual dumps, you will be fully prepared to pass the test on your first attempt.

The SPLK-1002 exam is a computer-based exam that consists of 65 multiple-choice and practical lab questions. Candidates have two hours to complete the exam, and they must achieve a minimum score of 70% to pass. SPLK-1002 exam is available in English, Japanese, and Simplified Chinese, and it can be taken at any Pearson VUE testing center worldwide.

Splunk SPLK-1002 exam is an online, proctored exam that consists of 60 multiple-choice questions. Candidates have 90 minutes to complete the exam, and they must achieve a passing score of 70% or higher. SPLK-1002 Exam can be taken at any time, and candidates can schedule the exam according to their availability.

The SPLK-1002 certification exam is intended for IT professionals, system administrators, and data analysts who have experience in working with Splunk. SPLK-1002 exam covers topics such as searching and analyzing data, creating dashboards and reports, and configuring alerts and tags. SPLK-1002 exam consists of 65 multiple-choice questions and has a duration of 90 minutes. The passing score for the exam is 70%.

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Splunk Core Certified Power User Exam Sample Questions (Q223-Q228):

NEW QUESTION # 223
Where are the results of eval commands stored?

  • A. In an index.
  • B. In a KV Store.
  • C. In a field.
  • D. In a database.

Answer: C

Explanation:
https://docs.splunk.com/Documentation/Splunk/8.0.2/SearchReference/Eval The eval command calculates an expression and puts the resulting value into a search results field.
If the field name that you specify does not match a field in the output, a new field is added to the search results.
If the field name that you specify matches a field name that already exists in the search results, the results of the eval expression overwrite the values in that field.


NEW QUESTION # 224
What other syntax will produce exactly the same results as | chart count over vendor_action by user?

  • A. | chart count over user by vendor_action
  • B. | chart count by vendor_action, user
  • C. | chart count over vendor_action, user
  • D. | chart count by vendor_action over user

Answer: B

Explanation:
Explanation
https://docs.splunk.com/Documentation/Splunk/8.1.2/SearchReference/Chart


NEW QUESTION # 225
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 given a friendly name for use in Pivot.
  • B. Auto-Extracted fields can have their data type changed.
  • C. Auto-Extracted fields can be added if they already exist in the dataset with constraints.
  • D. Auto-Extracted fields can be hidden in Pivot.

Answer: A,C,D

Explanation:
Auto-Extracted fields in Splunk Data Models are derived directly from the indexed data based on the existing fields within the events. These fields are identified and extracted by Splunk automatically, without the need for explicit field extractions configured by the user. Understanding the characteristics of Auto-Extracted fields is crucial for effectively managing Data Models and utilizing them in Pivot tables for analysis.
A: Auto-Extracted fields can be hidden in Pivot. This is true. When building a Data Model, you have the option to hide certain fields from appearing in Pivot, making the Pivot table cleaner and more focused on the fields that are most relevant for analysis. This helps in reducing clutter and focusing on the data that matters most to the users.
B: Auto-Extracted fields can have their data type changed. This statement is not typically accurate for Auto- Extracted fields. The data type of an Auto-Extracted field is determined by Splunk based on the field's content in the indexed data. While you can assign a type to a field when you manually create a field in a data model, the inherent data type of Auto-Extracted fields is not something that is changed within the Data Model itself.
C: Auto-Extracted fields can be given a friendly name for use in Pivot. This is correct. Within Data Models, you can assign a more user-friendly, descriptive name to an Auto-Extracted field. This feature is particularly useful in making Data Models more intuitive and easier to use forthose who may not be familiar with the original field names or when the original field names are not descriptive or user-friendly.
D: Auto-Extracted fields can be added if they already exist in the dataset with constraints. This is true. Auto- Extracted fields are based on fields that already exist in the data. When you define a dataset within a Data Model, you can apply constraints to narrow down the events that the dataset includes. The Auto-Extracted fields are then identified from this constrained dataset. This means that the fields must already be present in the data that meets the dataset's constraints to be available for auto-extraction.
In summary, Auto-Extracted fields in Splunk Data Models offer a flexible and efficient way to utilize existing data fields within Pivot tables, with options to rename them for clarity and hide unnecessary fields to streamline data analysis.


NEW QUESTION # 226
Which delimiters can the Field Extractor (FX) detect? (select all that apply)

  • A. Commas
  • B. Spaces
  • C. Tabs
  • D. Pipes

Answer: A,B,D

Explanation:
Reference:
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.


NEW QUESTION # 227
Which of the following knowledge objects represents the output of an oval expression?

  • A. Eval fields
  • B. Calculated lookups
  • C. Field extractions
  • D. Calculated fields

Answer: D

Explanation:
Reference:https://docs.splunk.com/Splexicon:Calculatedfield


NEW QUESTION # 228
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