Question 1

After enabling multi-tenancy, which of the Mowing is the first configuration step?

Accepted Answer

D

Explanation: 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.

Question 2

When writing a custom function that uses regex to extract the domain name from a URL, a user
wants to create a new artifact for the extracted domain. Which of the following Python API calls will
create a new artifact?

Accepted Answer

C

Explanation: 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.

Question 3

In addition to full backups. Phantom supports what other backup type using backup?

Accepted Answer

B

Explanation: 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.

Question 4

A user has written a playbook that calls three other playbooks, one after the other. The user notices
that the second playbook starts executing before the first one completes. What is the cause of this
behavior?

Accepted Answer

D

Explanation: 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.

Question 5

Two action blocks, geolocate_ip 1 and file_reputation_2, are connected to a decision block. Which of the following is a correct configuration for making a decision on the action results from one of the given blocks?

Accepted Answer

A

Explanation: 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.

Question 6

In a playbook, more than one Action block can be active at one time. What is this called?

Accepted Answer

B

Explanation: 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.

Question 7

If no data matches any filter conditions, what is the next block run by the playbook?

Accepted Answer

D

Explanation: 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

Question 8

Is it possible to import external Python libraries such as the time module?

Accepted Answer

C

Explanation: 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.

Question 9

Which Phantom API command is used to create a custom list?

Accepted Answer

B

Explanation: 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.

Question 10

How can a child playbook access the parent playbook's action results?

Accepted Answer

C

Explanation: 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.

Question 11

Configuring Phantom search to use an external Splunk server provides which of the following benefits?

Accepted Answer

C

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 https://www.splunk.com/en_us/products/splunk-security-orchestration-and-automationfeatures.html

Question 12

How is it possible to evaluate user prompt results?

Accepted Answer

C

Explanation: In Splunk Phantom, user prompts are actions that require human input. To evaluate the results of a user prompt, you can set the response requirement in the action result summary. By setting action_result.summary.response to required, the playbook ensures that it captures the user's input and can act upon it. This is critical in scenarios where subsequent actions depend on the choices made by the user in response to a prompt. Without setting this, the playbook would not have a defined way to handle the user response, which might lead to incorrect or unexpected playbook behavior.

Question 13

Some of the playbooks on the Phantom server should only be executed by members of the admin role. How can this rule be applied?

Accepted Answer

C

Explanation: The correct answer is C because the best way to restrict the execution of playbooks to members of the admin role is to make sure the Execute Playbook capability is removed from all roles except admin. The Execute Playbook capability is a permission that allows a user to run any playbook on any container. By default, all roles have this capability, but it can be removed or added in the Phantom UI by going to Administration > User Management > Roles. Removing this capability from all roles except admin will ensure that only admin users can execute playbooks. See Splunk SOAR Documentation for more details. To ensure that only members of the admin role can execute specific playbooks on the Phantom server, the most effective approach is to manage role-based access controls (RBAC) directly. By configuring the system to remove the "Execute Playbook" capability from all roles except for the admin role, you can enforce this rule. This method leverages Phantom's built- in RBAC mechanisms to restrict playbook execution privileges. It is a straightforward and secure way to ensure that only users with the necessary administrative privileges can initiate the execution of sensitive or critical playbooks, thus maintaining operational security and control.

Question 14

Phantom supports multiple user authentication methods such as LDAP and SAML2. What other user authentication method is supported?

Accepted Answer

B

Explanation: Splunk SOAR supports multiple user authentication methods to ensure secure access to the platform. Apart from LDAP (Lightweight Directory Access Protocol) and SAML2 (Security Assertion Markup Language 2.0), SOAR also supports PIV (Personal Identity Verification) and CAC (Common Access Card) as authentication methods. These are particularly used in government and military organizations for secure and authenticated access to systems, providing a high level of security through physical tokens or cards that contain encrypted user credentials.

Question 15

How is a Django filter query performed?

Accepted Answer

A

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.

Free Splunk SPLK-2003 Actual Exam Questions