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Splunk SPLK-1002 Exam Questions
- Topic 1: Using Transforming Commands for Visualizations/ Use the Chart Command/ Use the Timechart Command
- Topic 2: Filtering and Formatting Results/ The Eval Command/ Use the Search and where Commands to Filter Results/ The Fillnull Command
- Topic 3: Correlating Events/ Identify Transactions/ Group Events Using Fields/ Group Events Using Fields and Time
- Topic 4: Search with Transactions/ Report on Transactions/ Determine When to Use Transactions vs. Stats
- Topic 5: Creating and Managing Fields/ Perform Regex Field Extractions Using the Field Extractor/ Perform Delimiter Field Extractions Using the FX
- Topic 6: Creating Field Aliases and Calculated Fields/ Describe, Create, and Use Field Aliases/ Describe, Create, and Use Calculated Fields
- Topic 7: Creating Tags and Event Types/ Create and Use Tags/ Describe Event Types and Their Uses/ Create an Event Type
- Topic 8: Creating and Using Macros/ Describe Macros/ Create and Use a Basic Macro/ Define Arguments and Variables for a Macro/ Add and Use Arguments with a Macro
- Topic 9: Creating and Using Workflow Actions/ Describe the Function of GET, POST, and Search Workflow Actions/ Create a GET Workflow Action, a POST Workflow Action, a Search Workflow Action
- Topic 10: Creating Data Models/ Describe the Relationship Between Data Models and Pivot/ Identify Data Model Attributes/ Create a Data Model
- Topic 11: Using the Common Information Model/ List the Knowledge Objects Included with the Splunk CIM Add-On/ Use the CIM Add-On to Normalize data
Free Splunk SPLK-1002 Exam Actual Questions
Note: Premium Questions for SPLK-1002 were last updated On Oct. 09, 2026 (see below)
Which of the following statements describe the search string below?
| datamodel Application_State All_Application_State search
The search string below returns events from the data model named Application_State.
| datamodel Application_State All_Application_State search
The search string does the following:
It uses the datamodel command to access a data model in Splunk. The datamodel command takes two arguments: the name of the data model and the name of the dataset within the data model.
It specifies the name of the data model as Application_State. This is a predefined data model in Splunk that contains information about web applications.
It specifies the name of the dataset as All_Application_State. This is a root dataset in the data model that contains all events from all child datasets.
It uses the search command to filter and transform the events from the dataset. The search command can use any search criteria or command to modify the results.
Therefore, the search string returns events from the data model named Application_State.
By default, how is acceleration configured in the Splunk Common Information Model (CIM) add-on?
By default, acceleration is determined automatically based on the data source in the Splunk Common Information Model (CIM) add-on. The Splunk CIM Add-on is an app that provides common data models for various domains, such as network traffic, web activity, authentication, etc. The CIM Add-on allows you to normalize and enrich your data using predefined fields and tags. The CIM Add-on also allows you to accelerate your data models for faster searches and reports. Acceleration is a feature that pre-computes summary data for your data models and stores them in tsidx files. Acceleration can improve the performance and efficiency of your searches and reports that use data models.
By default, acceleration is determined automatically based on the data source in the CIM Add-on. This means that Splunk will decide whether to enable or disable acceleration for each data model based on some factors, such as data volume, data type, data model complexity, etc. However, you can also manually enable or disable acceleration for each data model by using the Settings menu or by editing the datamodels.conf file.
Which of the following fields should be normalized using the Splunk Common Information Model (CIM) based on their relationship?
Normalization in CIM aligns differing field names that represent the same type of data across multiple sources.
Extract: ''Field aliases and tags are used to map fields with different names but equivalent meaning, such as source_ip and src_ip.''
Thus, src_ip and source_ip should be normalized to represent a common standardized field.
Which of the following is a feature of the Pivot tool?
The correct answer is C. Creates reports without using SPL. This is because the Pivot tool is a feature of Splunk that allows you to report on a specific data set without using the Splunk Search Processing Language (SPL). You can use a drag-and-drop interface to design and generate pivots that present different aspects of your data in the form of tables, charts, and other visualizations. You can learn more about the Pivot tool from the Splunk documentation1 or watch a video tutorial2. The other options are incorrect because they do not describe the features of the Pivot tool. The Pivot tool requires data models and datasets to define the data that you want to work with. Data models and datasets are designed by the knowledge managers in your organization. You can learn more about data models and datasets from the Splunk documentation3. The Pivot tool does not create lookups, which are tables that match field values to other field values. You can create lookups using SPL or the Lookup Editor. You can learn more about lookups from the Splunk documentation.
The transaction command allows you to __________ events across multiple sources
The transaction command allows you to correlate events across multiple sources. The transaction command is a search command that allows you to group events into transactions based on some common characteristics, such as fields, time, or both. A transaction is a group of events that share one or more fields that relate them to each other. A transaction can span across multiple sources or sourcetypes that have different formats or structures of data. The transaction command can help you correlate events across multiple sources by using the common fields as the basis for grouping. The transaction command can also create some additional fields for each transaction, such as duration, eventcount, startime, etc.
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