SPLK-2003 Practice Questions

Upon enabling multi-tenancy in Splunk SOAR, the first step in configuration typically involves setting

up the default tenant. This foundational step is critical as it establishes the primary operating

environment under which subsequent tenants can be created and managed. The default tenant

serves as the template for permissions, settings, and configurations that might be inherited or

customized by additional tenants. Proper configuration of the default tenant ensures a stable and

consistent framework for multi-tenancy operations, allowing for segregated environments within the

same SOAR instance, each tailored to specific operational needs or organizational units.

In the Splunk SOAR platform, when writing a custom function in Python to handle data such as

extracting a domain name from a URL, you can create a new artifact using the Python API call

phantom.create_artifact(). This function allows you to specify the details of the new artifact, such as

the type, CEF (Common Event Format) data, container it belongs to, and other relevant information

necessary to create an artifact within the system.

Splunk Phantom supports incremental backups in addition to full backups. An incremental backup is

a type of backup that only copies the data that has changed since the last backup (whether that was a

full backup or another incremental backup). This method is more storage-efficient than a full backup

because it does not repeatedly back up the same data, reducing the amount of storage required and

speeding up the backup process. Differential backups, which record the changes since the last full

backup, and partial backups, which allow the selection of specific data to back up, are not standard

backup types offered by Splunk Phantom according to its documentation.

The correct answer is D because synchronous execution has not been configured. Synchronous

execution is a feature that allows you to control the order of execution of playbook blocks. By

default, Phantom executes playbook blocks asynchronously, meaning that it does not wait for one

block to finish before starting the next one. This can cause problems when you have dependencies

between blocks or when you call other playbooks. To enable synchronous execution, you need to use

the sync action in the run playbook block and specify the name of the next block to run after the

called playbook completes. See Splunk SOAR Documentation for more details.

In Splunk SOAR, playbooks can be executed either synchronously or asynchronously. Synchronous

execution ensures that a playbook waits for a called playbook to complete before proceeding to the

next step. If the second playbook starts executing before the first one completes, it indicates that

synchronous execution was not configured for the playbooks. Without synchronous execution,

playbooks will execute independently of each other's completion status, leading to potential

overlaps in execution. This behavior can be controlled by properly configuring the playbook

execution settings to ensure that dependent playbooks complete their tasks in the desired order.

In the given decision block, you are trying to evaluate the results of two action blocks: geolocate_ip_1 and file_reputation_2. The correct configuration for making a decision based on the result of geolocate_ip_1 is by checking the country_iso_code field from the action result and setting the evaluation option to != (not equal), with no specific value provided in the "Select Value" box. This essentially checks whether a valid country ISO code exists in the action result and proceeds if it's not empty or different from a specific value. This is a common check when working with geolocation results to see if a response has been returned. Other options (B, C, and D) include response codes or list comparisons, which do not align with the decision structure mentioned, which needs to operate based on a country_iso_code field. Reference: Splunk SOAR Playbook Development Guide. Splunk SOAR Documentation on Decision Blocks and Action Result Evaluation.

In Splunk SOAR, when a playbook is designed such that more than one Action block is active at the

same time, it is referred to as 'Parallel Processing'. This allows for multiple actions to be executed

concurrently, which can significantly speed up the execution of a playbook as it does not have to wait

for one action to complete before starting another. Parallel processing enables more efficient use of

resources and time, particularly in complex playbooks that perform numerous actions.

In a Splunk SOAR playbook, if no data matches the conditions specified within a filter block, the

playbook execution will proceed to the next block that is configured to follow the filter block. The

"next block" refers to whatever action or decision block is designed to be next in the sequence

according to the playbook’s logic.

Filters in Splunk SOAR are used to make decisions based on data conditions, and they control the

flow of the playbook. If the conditions in a filter block are not met, the playbook does not simply end

or restart; rather, it continues to execute the subsequent blocks that have been set up to handle

situations where the filter conditions are not met.

A filter block will typically have different paths for different outcomes—matching and non-matching.

If the conditions are matched, one set of blocks will execute, and if not, another set of blocks, which

could simply be the next one in the sequence, will execute. This allows for complex logic and

branching within the playbook to handle a wide range of scenarios.

In a Splunk SOAR playbook, when no data matches any filter conditions, the playbook continues to

run by proceeding to the next block in the sequence. The filter block is designed to specify a subset

of artifacts before further processing, and only artifacts matching the specified condition are passed

along to downstream blocks for processing1. If no artifacts meet the conditions, the playbook does

not end or restart; instead, it moves on to the next block, which could be any type of block

depending on the playbook’s design1.

Reference:

Use filters in your Splunk SOAR (Cloud) playbook to specify a subset of artifacts before further

processing - Splunk Documentation

In Splunk SOAR, it is possible to import external Python libraries, such as the time module, within

the scope of a playbook's global code block. The global block allows users to define custom Python

code, including imports of standard Python libraries that are included in the Phantom platform's

Python environment. This capability enables the extension of playbooks' functionality with additional

Python logic, making playbooks more powerful and versatile in their operations.

The Phantom API command to create a custom list is phantom.create_list(). This command takes a

list name and an optional description as parameters and returns a list ID if successful. The other

commands are not valid Phantom API commands. phantom.add_list() is a Python function that can

be used in custom code blocks to add data to an existing list.  To create a custom list in Splunk

Phantom, the appropriate API command used is phantom.create_list(). This function allows for the

creation of a new list that can be used to store data such as IP addresses, file hashes, or any other

information that you want to track or reference across multiple playbooks or within different parts of

the Phantom platform. The custom list is a flexible data structure that can be leveraged for various

use cases within Phantom, including data enrichment, persistent storage of information, and cross-

playbook data sharing.

In Splunk Phantom, child playbooks can access the action results of a parent playbook through the

use of the Scope parameter. When a parent playbook calls a child playbook, it can pass certain data

along by setting the Scope parameter to include the desired action results. This parameter is

configured within the playbook block that initiates the child playbook. By specifying the appropriate

scope, the parent playbook effectively determines what data the child playbook will have access to,

allowing for a more modular and organized flow of information between playbooks.