SPLK-2003 Exam Dumps - Splunk SOAR Certified Automation Developer Exam

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.

Within Splunk SOAR, containers (which represent incidents, cases, or events) have a lifecycle that is tracked through their status. The default statuses available without any customization are "New", "In Progress", and "Closed". These statuses help in organizing and managing the incident response process, allowing users to easily track the progress of investigations and responses from initial detection through to resolution.

The severity of a container in Splunk Phantom can be set manually or automatically during the ingestion process. In addition to these methods, playbooks can also change the severity of a container. Playbooks are automated workflows that define a series of actions based on certain triggers and conditions. Within a playbook, actions can be defined to adjust the severity level of a container depending on the analysis of the event data, the outcome of actions taken, or other contextual factors. This dynamic adjustment allows for a more accurate and responsive incident prioritization as new information becomes available during the investigation process.

The Splunk SOAR Automation Broker does not require inbound/ingress network connections from the Splunk SOAR (Cloud) or (On-premises) instance. Instead, it requires only outbound/egress connectivity. The Automation Broker is responsible for securely communicating with SOAR to execute actions, retrieve data, and send results, but this communication is initiated from the Automation Broker towards SOAR, using outbound connections (typically over TCP port 443). This ensures that no inbound connections need to be established, which simplifies firewall and security configurations.

Thus, option D is the incorrect statement, making it the right answer for this question.

References:

Splunk SOAR Documentation: Automation Broker Requirements.

Splunk SOAR Cloud and On-Premises Deployment Guide.

 The correct answer is A because after a successful POST to a Phantom REST endpoint to create a new object, the result returned is the new object ID. The object ID is a unique identifier for each object in Phantom, such as a container, an artifact, an action, or a playbook. The object ID can be used to retrieve, update, or delete the object using the Phantom REST API. The answer B is incorrect because after a successful POST to a Phantom REST endpoint to create a new object, the result returned is not the new object name, which is a human-readable name for the object. The object name can be used to search for the object using the Phantom web interface. The answer C is incorrect because after a successful POST to a Phantom REST endpoint to create a new object, the result returned is not the full CEF name, which is a standard format for event data. The full CEF name can be used to access the CEF fields of an artifact using the Phantom REST API. The answer D is incorrect because after a successful POST to a Phantom REST endpoint to create a new object, the result returned is not the PostGres UUID, which is a unique identifier for each row in a PostGres database. The PostGres UUID is not exposed to the Phantom REST API. Reference: Splunk SOAR REST API Guide, page 17. When a POST request is made to a Phantom REST endpoint to create a new object, such as an event, artifact, or container, the typical response includes the ID of the newly created object. This ID is a unique identifier that can be used to reference the object within the system for future operations, such as updating, querying, or deleting the object. The response does not usually include the full name or other specific details of the object, as the ID is the most important piece of information needed immediately after creation for reference purposes.

The Splunk App for Phantom is designed to facilitate the integration between Splunk Enterprise Security and Splunk SOAR (Phantom), enabling the seamless forwarding of notable events from Splunk to Phantom. This app allows users to leverage the analytical and data processing capabilities of Splunk ES and utilize Phantom for automated orchestration and response. The app typically includes mechanisms for specifying which notable events to send to Phantom, formatting the data appropriately, and ensuring secure communication between the two platforms. This integration is crucial for organizations looking to combine the strengths of Splunk's SIEM capabilities with Phantom's automation and orchestration features to enhance their security operations.

In Splunk SOAR (formerly known as Phantom), enabling the Logging option for a playbook's settings primarily affects how logging information is displayed on the Investigation page. When this option is enabled, more detailed logging information is made available on the Investigation page, which can be crucial for troubleshooting and understanding the execution flow of the playbook. This detailed information can include execution steps, actions taken, and conditional logic paths followed during the playbook run.

It's important to note that enabling logging does not affect the audit logs or the debug window directly, nor does it write execution details to the spawn.log. Instead, it enhances the visibility and granularity of logs displayed on the specific Investigation page related to the playbook's execution.

References:

Splunk Documentation and SOAR User Guides typically outline the impacts of enabling various settings within the playbook configurations, explaining how these settings affect the operation and logging within the system. For specific references, consulting the latest Splunk SOAR documentation would provide the most accurate and detailed guidance.

Enabling the Logging option for a playbook’s settings in Splunk SOAR indeed affects the level of detail provided on the Investigation page. Here’s a comprehensive explanation of its impact:

Investigation Page Logging:

The Investigation page serves as a centralized location for reviewing all activities related to an incident or event within Splunk SOAR.

When the Logging option is enabled, it enhances the level of detail available on this page, providing a granular view of the playbook’s execution.

This includes detailed information about each action’s execution, such as parameters used, results obtained, and any conditional logic that was evaluated.

Benefits of Detailed Logging:

Troubleshooting: It becomes easier to diagnose issues within a playbook when you can see a detailed log of its execution.

Incident Analysis: Analysts can better understand the sequence of events and the decisions made by the playbook during an incident.

Playbook Optimization: Developers can use the detailed logs to refine and improve the playbook’s logic and performance.

Non-Impacted Areas:

The audit log, which tracks changes to the playbook itself, is not affected by the Logging option.

The debug window, used for real-time debugging during playbook development, also remains unaffected.

The spawn.log file, which contains internal operational logs for the Splunk SOAR platform, does not receive detailed execution information from playbooks.

Best Practices:

Enable detailed logging during the development and testing phases of a playbook to ensure thorough analysis and debugging.

Consider the potential impact on storage and performance when enabling detailed logging in a production environment.

References:

For the most accurate and up-to-date guidance on playbook settings and their effects, I recommend consulting the latest Splunk SOAR documentation and user guides. These resources provide in-depth information on configuring playbooks and understanding the implications of various settings within the Splunk SOAR platform.

In summary, the Logging option is a powerful feature that enhances the visibility of playbook operations on the Investigation page, aiding in incident analysis and ensuring that playbooks are functioning correctly. It is an essential tool for security teams to effectively manage and respond to incidents within their environment. 😊

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.

https://docs.splunk.com/Documentation/SOAR/current/Playbook/DecisionBlock

In Splunk SOAR playbooks, the blocks that can discern which path to take next are the prompt and decision blocks. The prompt block allows the playbook to pause and wait for user input, which can then determine the subsequent path of execution based on the response provided. The decision block evaluates conditions based on data within the playbook and directs the flow to different paths accordingly11.

The decision block is used to change the flow of artifacts by performing IF, ELSE IF, or ELSE functions. When an artifact meets a True condition, it is passed downstream to the corresponding block in the playbook flow11. The prompt block, on the other hand, interacts with users to make decisions during playbook execution, which can also influence the direction of the playbook’s flow.

References:

Splunk SOAR documentation on using decisions to send artifacts to a specific downstream action in your playbook

The correct answer is D because the default ports that must be configured on Splunk to allow connections from Phantom are SplunkWeb (8000), SplunkD (8089), and HTTP Collector (8088). SplunkWeb is the port used to access the Splunk web interface. SplunkD is the port used to communicate with the Splunk server. HTTP Collector is the port used to send data to Splunk using the HTTP Event Collector (HEC). These ports must be configured on Splunk and Phantom to enable the integration between the two products. See Splunk SOAR Documentation for more details.

To allow connections from Splunk Phantom to Splunk, certain default ports need to be open and properly configured. The default ports include SplunkWeb (8000) for web access, SplunkD (8089) for Splunk's management port, and the HTTP Event Collector (HEC) on port 8088, which is used for ingesting data into Splunk. These ports are essential for the communication between Splunk Phantom and Splunk, facilitating data exchange, search capabilities, and the integration of various functionalities between the two platforms.

The error message "an empty parameters list was passed to phantom.act()" typically indicates that the action being called by the playbook does not have the required parameters to execute. This can happen if the playbook expects certain data to be present in the container's artifacts but finds none. Artifacts in Splunk SOAR (Phantom) are data elements associated with a container (such as an event or alert) that playbooks can act upon. If a playbook action is designed to use data from artifacts as parameters and those artifacts are missing or do not contain the expected data, the playbook cannot execute the action properly, leading to this error.

The Files tab on the Investigate page allows the user to upload, download, and view files related to an investigation. A user can upload the output from a detonate action to the Files tab for further investigation, such as analyzing the file metadata, content, or hash. Files tab items and artifacts are not the only data sources that can populate active cases, as cases can also include events, tasks, notes, and comments. Files tab items can be added to investigations by using the add file action block or the Add File button on the Files tab. Phantom memory requirements may increase depending on the Files tab usage, as files are stored in the Phantom database. 

The Files tab on the Investigate page in Splunk Phantom is an area where users can manage and analyze files related to an investigation. Users can upload files, such as outputs from a 'detonate file' action which analyzes potentially malicious files in a sandbox environment. The files tab allows users to store and further investigate these outputs, which can include reports, logs, or any other file types that have been generated or are relevant to the investigation. The Files tab is an integral part of the investigation process, providing easy access to file data for analysis and correlation with other incident data.

The admin and automation users are included by default. Comprehensive Explanation and References of Correct Answer: According to the Splunk SOAR (On-premises) default credentials, script options, and sample configuration files documentation1, the default credentials on a new installation of Splunk SOAR (On-premises) are:

Web Interface Username: soar_local_admin password: password

On Splunk SOAR (On-premises) deployments which have been upgraded from earlier releases the user account admin becomes a normal user account with the Administrator role.

The automation user is a special user account that is used by Splunk SOAR (On-premises) to run actions and playbooks. It has the Automation role, which grants it full access to all objects and data in Splunk SOAR (On-premises).

The other options are incorrect because they either omit the automation user or include users that are not created by default. For example, option B includes the power and user users, which are not part of the default installation. Option C only includes the admin user, which ignores the automation user. Option D claims that no users are included by default, which is false.

In a new installation of Splunk SOAR, two default user accounts are typically created: admin and automation. The admin account is intended for system administration tasks, providing full access to all features and settings within the SOAR platform. The automation user is a special account used for automated processes and scripts that interact with the SOAR platform, often without requiring direct human intervention. This user has specific permissions that can be tailored for automated tasks. Options B, C, and D do not accurately represent the default user accounts included in a new SOAR installation, making option A the correct answer.

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.

The correct answer is C because the last step of the 12A2 design methodology is to list the outputs of the playbook design. The outputs are the expected results or outcomes of the playbook execution, such as sending an email, creating a ticket, blocking an IP, etc. The outputs should be aligned with the objectives and goals of the playbook. See Splunk SOAR Certified Automation Developer for more details.

The 12A2 design methodology in the context of Splunk SOAR (formerly Phantom) refers to a structured approach to developing playbooks. The last step in this methodology focuses on defining the outputs of the playbook design. This step is crucial as it outlines what the expected results or actions the playbook should achieve upon its completion. These outputs can vary widely, from sending notifications, creating tickets, updating statuses, to generating reports. Defining the outputs is essential for understanding the playbook's impact on the security operation workflows and how it contributes to resolving security incidents or automating tasks.

 The correct answer is A because the _filter parameter is used to filter the results based on a field value, and the icontain operator is used to perform a case-insensitive substring match. The filePath field is part of the Common Event Format (CEF) standard, and the cef_ prefix is used to access CEF fields in the REST API. The answer B is incorrect because it uses the wrong syntax for the REST API. The answer C is incorrect because it uses the wrong endpoint (result instead of artifact) and the wrong syntax for the REST API. The answer D is incorrect because it uses the wrong syntax for the REST API and the wrong spelling for the icontains operator. Reference: Splunk SOAR REST API Guide, page 18.

To query and display all artifacts that contain the term "results" in a filePath CEF (Common Event Format) value, using the REST API endpoint with a filter parameter is effective. The filter _filter_cef_filePath_icontain="results" is applied to search within the artifact data for filePath fields that contain the term "results", disregarding case sensitivity. This method allows users to precisely locate and work with artifacts that meet specific criteria, aiding in the investigation and analysis processes within Splunk SOAR.

System Health Display is a dashboard that shows the status and performance of the SOAR processes and components, such as the automation service, the playbook daemon, the DECIDED process, and the REST API. Some of the metrics that can be seen from the System Health Display are:

•Memory Usage: The percentage of memory used by the system and the processes.

•Disk Usage: The percentage of disk space used by the system and the processes.

•Load Average: The average number of processes in the run queue or waiting for disk I/O over a period of time.

Therefore, options B, C, and D are the correct answers, as they are the metrics that can be seen from the System Health Display. Option A is incorrect, because Playbook Usage is not a metric that can be seen from the System Health Display, but rather a metric that can be seen from the Playbook Usage dashboard, which shows the number of playbooks and actions run over a period of time.

1: Web search results from search_web(query="Splunk SOAR Automation Developer System Health Display")

The System Health Display in Splunk SOAR provides several metrics to help monitor and manage the health of the system. These typically include:

•B: Memory Usage - This metric shows the amount of memory being used by the SOAR platform, which is important for ensuring that the system does not exceed available resources.

•C: Disk Usage - This metric indicates the amount of storage space being utilized, which is crucial for maintaining adequate storage resources and for planning capacity.

•D: Load Average - This metric provides an indication of the overall load on the system over a period of time, which helps in understanding the system's performance and in identifying potential bottlenecks or issues.

Playbook Usage is generally not a metric displayed on the System Health page; instead, it's more related to the usage analytics of playbooks rather than system health metrics.

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.

To configure the Analyst Queue to only show new events that are assigned to the current user "pat", the correct filter would involve two conditions:

status=new: This ensures that only new events are displayed.

owner=pat: This ensures that the displayed events are specifically assigned to the user "pat."

By applying both of these filters, the user will only see events that are both in the "new" status and assigned to them. The other options, such as filtering for "label" or using "or" in the filter, would either result in showing incorrect data or broader results that are not restricted to new events assigned to the user.

References:

Splunk SOAR Documentation: Analyst Queue Filters.

Splunk SOAR User Guide for Customizing Event Views.

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.

Splunk SOAR (formerly Phantom) does not natively run on Windows servers as it is primarily designed for Linux environments. However, it can be deployed on a Windows server through virtualization. By running the Phantom OVA (Open Virtualization Appliance) as a virtual machine, users can utilize virtualization platforms like VMware or VirtualBox on a Windows server to host the Phantom environment. This approach allows for the deployment of Phantom in a Windows-centric infrastructure by leveraging virtualization technology to encapsulate the Phantom application within a supported Linux environment provided by the OVA.

A filter block with only one condition configured which states: artifact.*.cef .sourceAddress !- , would permit only non-null IP addresses to pass forward to the next block. The !- operator means “is not null”. The other options are not valid because they either include null values or other fields than sourceAddress. See Filter block for more details. A filter block in Splunk SOAR that is configured with the condition artifact.*.cef.sourceAddress != (assuming the intention was to use "!=" to denote 'not equal to') is designed to allow data that has non-null sourceAddress values to pass through to subsequent blocks. This means that any artifact data within the container that includes a sourceAddress field with a defined value (i.e., an actual IP address) will be permitted to move forward in the playbook. The filter effectively screens out any artifacts that do not have a source address specified, focusing the playbook's actions on those artifacts that contain valid IP address information in the sourceAddress field.

The correct answer is A because assets provide location, credentials, and other parameters needed to run actions. Assets are configurations that define how Phantom connects to external systems or devices, such as firewalls, endpoints, or threat intelligence sources. Assets specify the app, the IP address or hostname, the username and password, and any other settings required to run actions on the target system or device. The answer B is incorrect because assets do not provide hostnames, passwords, and other artifacts needed to run actions, which are data objects that can be created or retrieved by playbooks. The answer C is incorrect because assets do not provide Python code, REST API, and other capabilities needed to run actions, which are provided by apps. The answer D is incorrect because assets do not provide firewall, network, and data sources needed to run actions, which are external systems or devices that can be connected to by assets. Reference: Splunk SOAR Admin Guide, page 45. Assets in Splunk Phantom are configurations that contain the necessary information for apps to connect to external systems and services. This information can include IP addresses, domain names, credentials like usernames and passwords, and other necessary parameters such as API keys or tokens. These parameters enable the apps to perform actions like running queries, executing commands, or gathering data. Assets do not provide the actual Python code, REST API capabilities, or network infrastructure; they are the bridge between the apps and the external systems with the configuration data needed for successful communication and action execution

The Splunk App for SOAR Export can be downloaded from both GitHub and Splunkbase. Splunkbase is the official source for Splunk apps, where users can find, try, and download apps that enhance and extend the capabilities of Splunk, including the Splunk App for SOAR Export1. GitHub is also a common platform for sharing and collaborating on code, including Splunk apps and integrations. It is important to ensure that you are downloading from the official repository or author to avoid any security risks.

References:

Splunkbase, the official source for downloading the Splunk App for SOAR Export

For Splunk SOAR to connect with Splunk Enterprise, certain default ports must be configured to facilitate communication between the two platforms. Typically, SplunkWeb, which serves the Splunk Enterprise web interface, uses port 8000. SplunkD, the Splunk daemon that handles most of the back-end services, listens on port 8089. The HTTP Event Collector (HEC), which allows HTTP clients to send data to Splunk, typically uses port 8088. These ports are essential for the integration, allowing SOAR to send data to Splunk for indexing, searching, and visualization. Options A, B, and D list incorrect port configurations for this purpose, making option C the correct answer based on standard Splunk configurations.

These are the default ports used by Splunk SOAR (On-premises) to communicate with the embedded Splunk Enterprise instance. SplunkWeb is the web interface for Splunk Enterprise, SplunkD is the management port for Splunk Enterprise, and HTTP Collector is the port for receiving data from HTTP Event Collector (HEC). The other options are either incorrect or not default ports. For example, option B has the SplunkWeb and SplunkD ports reversed, and option D has arbitrary port numbers that are not used by Splunk by default.

ROI Settings dashboard allows you to configure the parameters used to estimate the data displayed in the Automation ROI Summary dashboard. One of the settings that can be configured is the FTE Gained, which is the number of full time employees (FTEs) that are freed up by automation. To calculate this value, Splunk SOAR divides the number of actions run by automation by the number of expected actions an analyst would take, based on minutes per action and analyst hours per day. Therefore, option A is the correct answer, as it is one of the settings that can be configured in the ROI Settings dashboard. Option B is incorrect, because time lost is not a setting that can be configured in the ROI Settings dashboard, but a metric that is calculated by Splunk SOAR based on the difference between the analyst minutes per action and the actual minutes per action. Option C is incorrect, because analyst hours per month is not a setting that can be configured in the ROI Settings dashboard, but a value that is derived from the analyst hours per day setting. Option D is incorrect, because annual analyst salary is a setting that can be configured in the ROI Settings dashboard, but not the one that is asked in the question.

1: Configure the ROI Settings dashboard in Administer Splunk SOAR (On-premises)

ROI (Return on Investment) Settings within Splunk SOAR are used to estimate the efficiency and financial impact of the SOAR platform. One of the configurable parameters in these settings is the 'Analyst hours per month'. This parameter helps in calculating the time saved through automation, which in turn can be translated into cost savings and efficiency gains. It reflects the direct contribution of the SOAR platform to operational productivity.

In Splunk SOAR, tasks within workbooks can be performed by any user whose role has the 'Perform Task' capability enabled. This capability is assigned within the role configuration and allows users with the appropriate permissions to execute tasks. It is not limited to users with write access or the container owner; rather, it is based on the specific permissions granted to the role with which the user is associated.

 When configuring Splunk Phantom to integrate with an external Splunk Enterprise instance, it is typically required to have user accounts with sufficient privileges to access data and perform necessary actions. The roles of "superuser" and "administrator" in Splunk provide the broad set of permissions needed for such integration, enabling comprehensive access to data, management capabilities, and the execution of searches or actions that Phantom may require as part of its automated playbooks or investigations.

When building a playbook in Splunk SOAR, if the desired artifact value does not appear in the auto-populated list of input parameters for an action, users have the option to manually enter the Common Event Format (CEF) datapath for that value. This allows for greater flexibility and customization in playbook design, ensuring that specific data points can be targeted even if they're not immediately visible in the interface. This manual entry of CEF datapaths allows users to directly reference the necessary data within artifacts, bypassing limitations of the auto-populated list. Options B, C, and D suggest alternative methods that are not typically used for this purpose, making option A the correct and most direct approach to entering an unlisted artifact value in a playbook action.

When assigning an input parameter to an action while building a playbook, a user can use the auto-populated list of artifact values that match the expected data type for the parameter. The auto-populated list is based on the contains parameter of the action inputs and outputs, which enables contextual actions in the SOAR user interface. However, the auto-populated list may not include all the possible artifact values that can be used as parameters, especially if the artifact values are nested or have uncommon data types. In that case, the user can type the CEF datapath in manually, using the syntax artifact.., where field is the name of the artifact field, such as cef, and key is the name of the subfield within the artifact field, such as sourceAddress. Typing the CEF datapath in manually allows the user to enter the unlisted artifact value as an input parameter to the action. Therefore, option A is the correct answer, as it states how it is possible to enter the unlisted artifact value. Option B is incorrect, because deleting and recreating the artifact is not a way to enter the unlisted artifact value, but rather a way to lose the existing artifact data. Option C is incorrect, because editing the artifact to enable the List as Parameter option for the CEF value is not a way to enter the unlisted artifact value, but rather a way to make the artifact value appear in the auto-populated list. Option D is incorrect, because editing the container to allow CEF parameters is not a way to enter the unlisted artifact value, but rather a way to modify the container properties, which are not related to the action parameters.

Filter blocks are used to add information to custom lists in Phantom VPE. Filter blocks allow the user to specify a list name and a filter expression to select the data to be added to the list. Action blocks are used to execute app actions, API blocks are used to make REST API calls, and decision blocks are used to evaluate conditions and branch the playbook execution. In the Phantom Visual Playbook Editor (VPE), an API block is used to interact with various external APIs, including custom lists within Phantom. Custom lists are key-value stores that can be used to maintain state, aggregate data, or track information across multiple playbook runs. API blocks allow the playbook to make GET, POST, PUT, and DELETE requests to these lists, facilitating the addition, retrieval, update, or removal of information. This makes API blocks a versatile tool in managing custom list data within playbooks.