SPLK-2003 Exam Dumps

C.   A format block.

Explanation: In Splunk SOAR playbook development, the format block is used to assemble commands and data into a valid Splunk search query. This block allows users to structure and manipulate strings, dynamically inserting variables, and constructing the precise format needed for a search query. By using a format block, playbooks can integrate data from various sources and ensure that it is assembled correctly before passing it to subsequent actions, such as executing a Splunk search.
Other blocks, like action, filter, and prompt blocks, serve different purposes (e.g., running actions, filtering data, or prompting for user input), but the format block is specifically designed for building structured data or queries like Splunk searches.

C.   The child playbook has access to the parent playbook's container and the parent's action result data.

Explanation: In Splunk SOAR, a child playbook can access both the container data and the action result data from the parent playbook. This capability allows child playbooks to continue processing data or actions that were initiated by the parent playbook, ensuring smooth data flow and facilitating complex workflows across multiple playbooks. When a parent playbook calls a child playbook, the container (which holds the event and artifact data) and action results (which hold the outputs of previously executed actions) are passed to the child playbook.

C.   The ability to automate Splunk searches within Phantom.

Explanation: The correct answer is C because configuring Phantom search to use an external Splunk server allows you to automate Splunk searches within Phantom using the run query action. This action can be used to run any Splunk search command on the external Splunk server and return the results to Phantom. You can also use the format results action to parse the results and use them in other blocks. See Splunk SOAR Documentation for more details.
Configuring Phantom (now known as Splunk SOAR) to use an external Splunk server enhances the automation capabilities within Phantom by allowing the execution of Splunk searches as part of the automation and orchestration processes. This integration facilitates the automation of tasks that involve querying data from Splunk, thereby streamlining security operations and incident response workflows. Splunk SOAR's ability to integrate with over 300 third-party tools, including Splunk, supports a wide range of automatable actions, thus enabling a more efficient and effective security operations center (SOC) by reducing the time to respond to threats and by making repetitive tasks more manageable.

B.   Reduce large complex playbooks which become difficult to maintain.

C.   Encourages code reuse in a more compartmentalized form.

D.   To avoid duplication of code across multiple playbooks.

Explanation: Creating smaller and more focused playbooks in Splunk SOAR is considered good design practice for several reasons:

• B: It reduces complexity, making playbooks easier to maintain. Large, complex playbooks can become unwieldy and difficult to troubleshoot or update.
• C: Encourages code reuse, as smaller playbooks can be designed to handle specific tasks that can be reused across different scenarios.
• D: Avoids duplication of code, as common functionalities can be centralized within specific playbooks, rather than having the same code replicated across multiple playbooks.

This approach has several benefits, such as:

• Reducing large complex playbooks which become difficult to maintain. Smaller playbooks are easier to read, debug, and update1.
• Encouraging code reuse in a more compartmentalized form. Smaller playbooks can be used as building blocks for multiple scenarios, reducing the need to write duplicate code12.
• Improving performance and scalability. Smaller playbooks can run faster and consume less resources than larger playbooks2.

The other options are not valid reasons for creating smaller and more focused playbooks. Reducing the amount of playbook data stored in each repo is not a significant benefit, as the playbook data is not very large compared to other types of data in Splunk SOAR. Avoiding duplication of code across multiple playbooks is a consequence of code reuse, not a separate goal.

C.   A custom function block.

Explanation: To limit the impact of custom code on the Visual Playbook Editor (VPE) in Splunk SOAR, custom code should be placed within a custom function block. Custom function blocks are designed to encapsulate code within a playbook, allowing users to input their own Python code and execute it as part of the playbook run. By confining custom code to these blocks, it maintains the VPE's performance and stability by isolating the custom code from the core functions of the playbook.
A custom function block is a way of adding custom Python code to your playbook, which can expand the functionality and processing of your playbook logic. Custom functions can also interact with the REST API in a customizable way. You can share custom functions across your team and across multiple playbooks to increase collaboration and efficiency. To create custom functions, you must have Edit Code permissions, which can be configured by an Administrator in Administration > User Management > Roles and Permissions. Therefore, option C is the correct answer, as it is the recommended way of placing custom code on the VPE, which limits the impact of custom code on the VPE performance and security. Option A is incorrect, because a custom container or a separate KV store are not valid ways of placing custom code on the VPE, but rather ways of storing data or artifacts. Option B is incorrect, because a separate code repository is not a way of placing custom code on the VPE, but rather a way of managing and versioning your code outside of Splunk SOAR. Option D is incorrect, because a separate container is not a way of placing custom code on the VPE, but rather a way of creating a new event or case. 1: Add custom code to your Splunk SOAR (Cloud) playbook with the custom function block using the classic playbook editor

D.   By restarting the automation service.

Explanation: DECIDED process is a core component of the SOAR automation engine that handles the execution of playbooks and actions. The DECIDED process can be restarted by restarting the automation service, which can be done from the command line using the service phantom restart command2. Restarting the automation service also restarts the playbook daemon, which is another core component of the SOAR automation engine that handles the loading and unloading of playbooks3. Therefore, option D is the correct answer, as it restarts both the DECIDED process and the playbook daemon. Option A is incorrect, because restarting the playbook daemon alone does not restart the DECIDED process.
Option B is incorrect, because the System Health page does not provide an option to restart the DECIDED process or the automation service. Option C is incorrect, because the Administration > Server Settings page does not provide an option to restart the DECIDED process or the automation service.
In Splunk SOAR, if the DECIDED process, which is responsible for playbook execution, needs to be restarted, this can typically be done by restarting the automation (or phantom) service. This service manages the automation processes, including playbook execution. Restarting it can reset the DECIDED process, resolving issues related to playbook execution or process hangs.

A.   By adding parameters to the URL similar to the following: phantom/rest/container?_filter_tags_contains="sumo".

Explanation: Django filter queries in Splunk SOAR are performed by appending filter parameters directly to the REST API URL. This allows users to refine their search and retrieve specific data. For example, to filter containers by tags containing the word "sumo", the following URL structure would be used:
https:///rest/container?_filter_tags_contains="sumo". This format enables users to construct dynamic queries that can filter results based on specified criteria within the Django framework used by Splunk SOAR.
The correct way to perform a Django filter query in Splunk SOAR is to add parameters to the URL similar to the following: phantom/rest/container?_filter_tags_contains=“sumo”.
This will return a list of containers that have the tag “sumo” in them. You can use various operators and fields to filter the results according to your needs. For more details, see Query for Data and Use filters in your Splunk SOAR (Cloud) playbook to specify a subset of artifacts before further processing. The other options are either incorrect or irrelevant for this question. For example:
•phantom/rest/search/app/contains/“sumo” is not a valid URL for a Django filter query. It will return an error message saying “Invalid endpoint”.
•There is no Django Filter Query Editor in the Administration panel of Splunk SOAR. You can use the REST API Tester to test your queries, but not to edit them.
•There is no SOAR Django App that needs to be installed or configured for performing Django filter queries. Splunk SOAR uses the Django framework internally, but you do not need to install or use any additional apps for this purpose.