Splunk SPLK-2003 Splunk SOAR Certified Automation Developer Exam Exam Practice Test

The correct answer is C because filter blocks can be used to select data for use by other blocks. Filter blocks can filter data from the container, artifacts, or custom lists based on various criteria, such as field name, value, operator, etc. Filter blocks can also join data from multiple sources using the join action. The output of the filter block can be used as input for other blocks, such as decision, format, prompt, etc. See Splunk SOAR Documentation for more details.

Filter blocks within Splunk SOAR playbooks are designed to sift through data and select specific pieces of information based on defined criteria. These blocks are crucial for narrowing down the data that subsequent blocks in a playbook will act upon. By applying filters, a playbook can focus on relevant data, thereby enhancing efficiency and ensuring that actions are taken based on precise, contextually relevant information. This capability is essential for tailoring the playbook's actions to the specific needs of the incident or workflow, enabling more targeted and effective automation strategies. Filters do not directly select blocks for container data access, choose assets by various administrative criteria, or select containers by attributes like severity or status; their primary function is to refine data within the playbook's operational context.

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

 The correct answer is B because an active playbook can be configured to operate on all containers that share a label. A label is a user-defined attribute that can be applied to containers to group them by a common characteristic, such as source, type, severity, etc. Labels can be used to filter containers and trigger active playbooks based on the label value. See Splunk SOAR Documentation for more details.

In Splunk SOAR, labels are used to categorize containers (such as incidents or events) based on their characteristics or the type of security issue they represent. An active playbook can be configured to trigger on all containers that share a specific label, enabling targeted automation based on the nature of the incident. This functionality allows for efficient and relevant playbook execution, ensuring that the automated response is tailored to the specific requirements of the container's category. Labels serve as a powerful organizational tool within SOAR, guiding the automated response framework to act on incidents that meet predefined criteria, thus streamlining the security operations process.

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 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.

References:

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

In Splunk SOAR, playbooks can execute actions either synchronously (waiting for one action to complete before starting the next) or asynchronously (allowing actions to run concurrently). If a playbook starts executing before the previous one has completed, it indicates that synchronous execution has not been properly configured between these playbooks. This is crucial when the output of one playbook is a dependency for the subsequent playbook. Options B, C, and D do not directly address the observed behavior of concurrent playbook execution, making option A the most accurate explanation for why the second playbook starts before the completion of the first.

synchronous execution is a feature of the SOAR automation engine that allows you to control the order of execution of playbook blocks. Synchronous execution ensures that a playbook block waits for the completion of the previous block before starting its execution. Synchronous execution can be enabled or disabled for each playbook block in the playbook editor, by toggling the Synchronous Execution switch in the block settings. Therefore, option A is the correct answer, as it states the cause of the behavior where the second playbook starts executing before the first one completes. Option B is incorrect, because the first playbook performing poorly is not the cause of the behavior, but rather a possible consequence of the behavior. Option C is incorrect, because the sleep option for the second playbook is not the cause of the behavior, but rather a workaround that can be used to delay the execution of the second playbook. Option D is incorrect, because the join configuration on the second playbook is not the cause of the behavior, but rather a way of merging multiple paths of execution into one.

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 default embedded search engine used by SOAR is the SOAR search engine, which is powered by the PostgreSQL database built-in to Splunk SOAR (Cloud). A Splunk SOAR (Cloud) Administrator can configure options for search from the Home menu, in Search Settings under Administration Settings. The SOAR search engine has been modified to accept the * wildcard and supports various operators and filters. For search syntax and examples, see Search within Splunk SOAR (Cloud)2.

Option A is incorrect, because the embedded Splunk search engine was used in earlier releases of Splunk SOAR (Cloud), but not in the current version. Option C is incorrect, because Django is a web framework, not a search engine. Option D is incorrect, because Elastic is a separate search engine that is not embedded in Splunk SOAR (Cloud).

1: Configure search in Splunk SOAR (Cloud) 2: Search within Splunk SOAR (Cloud)

Splunk SOAR utilizes its own embedded search engine by default, which is tailored to its security orchestration and automation framework. While Splunk SOAR can integrate with other search engines, like the Embedded Splunk search engine, for advanced capabilities and log analytics, its default setup comes with an embedded search engine optimized for the typical data and search patterns encountered within the SOAR platform.

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.

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.

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.

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 correct answer is C because after a playbook has run, the results are stored in the container that triggered the playbook. The container is a data object that represents an event or a case in Phantom. The container contains information such as the name, the description, the severity, the status, the owner, and the labels of the event or case. The container also contains the artifacts, the action results, the comments, the notes, and the phases and tasks associated with the event or case. The answer A is incorrect because after a playbook has run, the results are not stored in a Splunk index, which is a data structure that stores events from various data sources in Splunk. The Splunk index is not directly accessible by Phantom, but can be queried by Phantom using the Splunk app. The answer B is incorrect because after a playbook has run, the results are not stored in a case, which is a type of container that represents a security incident in Phantom. The case is a subset of the container, and not all containers are cases. The answer D is incorrect because after a playbook has run, the results are not stored in a log file, which is a file that records the activities or events that occur in a system or a process. The log file is not a data object in Phantom, but can be a data source for Phantom. Reference: Splunk SOAR User Guide, page 19. In Splunk Phantom, after a playbook has been executed, the results of the actions within that playbook are stored in the container associated with the event. A container is a data structure that encapsulates all relevant information and data for an incident or event within Phantom, including action results, artifacts, notes, and more. The container allows users to see a consolidated view of all the data and activity related to a particular event. These results are not stored in the Splunk Index, a separate case, or a log file as their primary storage but may be sent to a Splunk index for further analysis.

The Splunk App for Phantom allows users to run Splunk queries directly from within the Phantom platform. This app facilitates the integration between Splunk and Phantom, enabling users to post data to Splunk as events, update notable events, run SPL (Search Processing Language) queries, and pull events from Splunk into Phantom. By configuring the asset settings and ingest settings in the configured asset, users can leverage the full capabilities of Splunk within the Phantom environment1.

References:

Integrating Splunk Phantom with Splunk Enterprise - TekStream Solutions

To get playbook results for a single artifact, a user can utilize the contextual menu option directly from the artifact itself. This method allows for targeted execution of a playbook on just that artifact, facilitating a focused analysis or action based on the data within that specific artifact. This approach is particularly useful when a user needs to drill down into the details of an individual piece of evidence or data point within a larger incident or case, allowing for granular control and execution of playbooks in the Splunk SOAR environment.

In Splunk SOAR, once an artifact is entered, it cannot be modified. An artifact refers to a piece of data associated with a specific container, such as log files, emails, or other relevant information in an incident. The immutable nature of artifacts ensures the integrity and forensic value of the data. By preventing modification after creation, SOAR maintains a secure and audit-compliant environment, ensuring that data remains trustworthy throughout the incident's lifecycle. However, containers, comments, and notes can be updated or modified, making artifacts unique in their immutability.

References:

Splunk SOAR User Guide: Artifacts and Containers.

Splunk SOAR Best Practices for Incident Management.

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.

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 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.

When configuring the Splunk app on the search head to search SOAR (Splunk's Security Orchestration, Automation, and Response) searchable content, two key components are required:

User Accounts: The user accounts are necessary to authenticate and authorize users who are accessing SOAR data through the Splunk app. These accounts manage permissions and access levels to ensure the proper users can search and interact with the data coming from SOAR.

HTTP Event Collector (HEC) Token: The HEC token is crucial because it allows the Splunk app to receive data from Splunk SOAR. SOAR sends events and other data to the Splunk platform via HEC. This token is used for secure communication and authentication between Splunk and SOAR. The token must be configured in the Splunk app to allow it to collect and search SOAR data seamlessly.

Other options like syslog, REST API, or a universal forwarder are commonly used methods for ingesting data into Splunk but are not specific requirements for setting up the Splunk app to search SOAR content. The HTTP Event Collector is the primary method for this setup, along with the correct user accounts.

References:

Splunk Documentation on HTTP Event Collector and SOAR Integration.

Splunk SOAR App Setup Guide for Splunk Search Head Configuration.

Indicators in Splunk SOAR (formerly Phantom) are crucial elements used to detect and respond to security incidents. Let’s break down what indicators are and their significance:

Definition of Indicators:

Indicators are data points or patterns that suggest the presence of malicious activity or potential security threats.

They can be anything from IP addresses, domain names, file hashes, URLs, email addresses, or other observable artifacts.

Indicators help security teams identify and correlate events across different sources to understand the scope and impact of an incident.

Types of Indicators:

Observable Indicators: These are directly observable artifacts, such as IP addresses, domain names, or file hashes.

Behavioral Indicators: These describe patterns of behavior, such as failed login attempts, lateral movement, or suspicious network traffic.

Contextual Indicators: These provide additional context around an event, such as the user account associated with an action or the time of occurrence.

Use Cases for Indicators:

Threat Detection: Security analysts create rules or playbooks that trigger based on specific indicators. For example, an indicator like a known malicious IP address can trigger an alert.

Incident Response: During an incident, indicators help identify affected systems, track lateral movement, and prioritize response efforts.

Threat Intelligence Sharing: Organizations share indicators with each other to improve collective security posture.

Multiple Containers:

Indicators can appear in multiple containers (playbooks, actions, etc.) within Splunk SOAR.

For example, an IP address associated with a suspicious domain might appear in both a threat intelligence playbook and an incident response playbook.

Artifact Values vs. Indicators:

While artifact values are related, they are not the same as indicators.

Artifact values represent specific data extracted from an artifact (e.g., extracting an IP address from an email header).

Indicators encompass a broader range of data points and are used for detection and correlation.

References:

Splunk SOAR Documentation: Indicators

Splunk SOAR Community: Understanding Indicators

To securely store a compressed version of an email attachment suspected of containing malware for future analysis, the most effective approach within Splunk SOAR is to use the Upload action of the Secure Store app. This app is specifically designed to handle sensitive or potentially dangerous files by securely storing them within the SOAR database, allowing for controlled access and analysis at a later time. This method ensures that the file is not only safely contained but also available for future forensic or investigative purposes without risking exposure to the malware. Options A, B, and C do not provide the same level of security and functionality for handling suspected malware files, making option D the most appropriate choice.

Secure Store app is a SOAR app that allows you to store files securely in the SOAR database. The Secure Store app provides two actions: Upload and Download. The Upload action takes a file as an input and stores it in the SOAR database in a compressed and encrypted format. The Download action takes a file ID as an input and retrieves the file from the SOAR database and decrypts it. The Secure Store app can be used to store files that contain sensitive or malicious data, such as email attachments with suspected malware, for future analysis. Therefore, option D is the correct answer, as it states the action that will store a compressed, secure version of an email attachment with suspected malware for future analysis. Option A is incorrect, because copying and pasting the attachment into a note will not store the file securely, but rather expose the file content to anyone who can view the note. Option B is incorrect, because adding a link to the file in a new artifact will not store the file securely, but rather create a reference to the file location, which may not be accessible or reliable. Option C is incorrect, because using the Files tab on the Investigation page to upload the attachment will not store the file securely, but rather store the file in the SOAR file system, which may not be encrypted or compressed.

The primary objective of using the I2A2 playbook design methodology in Splunk SOAR is to create playbooks that are simple, reusable, and modular. This design philosophy emphasizes the creation of playbooks that can be easily understood and maintained, encourages the reuse of playbook components in different scenarios, and fosters the development of playbooks that can be modularly connected or used independently as needed.

I2A2 design methodology is a framework for designing playbooks that consists of four components:

•Inputs: The data that is required for the playbook to run, such as artifacts, parameters, or custom fields.

•Interactions: The blocks that allow the playbook to communicate with users or other systems, such as prompts, comments, or emails.

•Actions: The blocks that execute the core logic of the playbook, such as app actions, filters, decisions, or utilities.

•Artifacts: The data that is generated or modified by the playbook, such as new artifacts, container fields, or notes.

The I2A2 design methodology helps you to plan, structure, and test your playbooks in a modular and efficient way. The primary objective of using the I2A2 design methodology is to create simple, reusable, modular playbooks that can be easily maintained, shared, and customized. Therefore, option D is the correct answer, as it states the primary objective of using the I2A2 design methodology. Option A is incorrect, because creating detailed playbooks is not the primary objective of using the I2A2 design methodology, but rather a possible outcome of following the framework. Option B is incorrect, because creating playbooks that customers will not edit is not the primary objective of using the I2A2 design methodology, but rather a potential risk of not following the framework. Option C is incorrect, because meeting customer requirements using a single playbook is not the primary objective of using the I2A2 design methodology, but rather a challenge that can be overcome by using the framework.

1: Use a playbook design methodology in Administer Splunk SOAR (Cloud).

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.

Splunk Phantom supports different types of backups to safeguard data. Full backups create a complete copy of the current state of the system, while incremental backups only save the changes made since the last backup. This approach allows for efficient use of storage space and faster backups after the initial full backup. Delta backups, which would save changes since the last full or incremental backup, are not a standard part of Phantom's backup capabilities according to available documentation. Therefore, the complete list of backups supported by Phantom would be Full and Incremental backups.

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 C because the default tenant’s ID is 1. The tenant ID is a unique identifier for each tenant on a multi-tenant Phantom server. The default tenant is the tenant that is created when Phantom is installed and contains all the existing data and assets. The default tenant’s ID is always 1 and cannot be changed. Other tenants have IDs that are assigned sequentially starting from 2. See Splunk SOAR Documentation for more details. In a multi-tenant Splunk SOAR environment, the default tenant is typically assigned an ID of 1. This ID is system-generated and is used to uniquely identify the default tenant within the SOAR database and system configurations. The default tenant serves as the primary operational environment before any additional tenants are configured, and its ID is crucial for database operations, API calls, and internal reference within the SOAR platform. Understanding and correctly using tenant IDs is essential for managing resources, permissions, and data access in a multi-tenant SOAR setup.

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.

Wildcards are used within artifact data paths in Splunk SOAR playbooks to simplify the process of accessing data. They allow playbooks to reference dynamic or variable data structures without needing to specify exact paths, which can vary between artifacts. This flexibility makes it easier to write playbooks that work across different events and scenarios, without hard-coding data paths.

SOAR uses wildcards within artifact data paths to make data access in playbooks easier. A data path is a way of specifying the location of a piece of data within an artifact. For example, artifact.cef.sourceAddress is a data path that refers to the source address field of the artifact. A wildcard is a special character that can match any value or subfield within a data path. For example, artifact.*.cef.sourceAddress is a data path that uses a wildcard to match any field name before the cef subfield. This allows the playbook to access the source address data regardless of the field name, which can vary depending on the app or source that generated the artifact. Therefore, option C is the correct answer, as it explains why SOAR uses wildcards within artifact data paths. Option A is incorrect, because wildcards do not make playbooks more specific, but more flexible and adaptable. Option B is incorrect, because wildcards do not make playbooks filter out nulls, but match any value or subfield. Option D is incorrect, because wildcards do not make decision execution in playbooks run faster, but make data access in playbooks easier.

1: Understanding datapaths in Administer Splunk SOAR (Cloud)

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.

I2A2 design methodology is a framework for designing playbooks that consists of four components:

•Inputs: The data that is required for the playbook to run, such as artifacts, parameters, or custom fields.

•Interactions: The blocks that allow the playbook to communicate with users or other systems, such as prompts, comments, or emails.

•Actions: The blocks that execute the core logic of the playbook, such as app actions, filters, decisions, or utilities.

•Artifacts: The data that is generated or modified by the playbook, such as new artifacts, container fields, or notes.

The I2A2 design methodology helps you to plan, structure, and test your playbooks in a modular and efficient way. Therefore, option B is the correct answer, as it lists the correct components of the I2A2 design methodology. Option A is incorrect, because apps are not a component of the I2A2 design methodology, but a source of actions that can be used in the playbook. Option C is incorrect, for the same reason as option A. Option D is incorrect, because assets are not a component of the I2A2 design methodology, but a configuration of app credentials that can be used in the playbook.

1: Use a playbook design methodology in Administer Splunk SOAR (Cloud)

The I2A2 design methodology is an approach used in Splunk SOAR to structure and design playbooks. The acronym stands for Inputs, Interactions, Actions, and Artifacts. This methodology guides the creation of playbooks by focusing on these four key components, ensuring that all necessary aspects of an automated response are considered and effectively implemented within the platform.

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.

In Splunk SOAR, roles serve multiple purposes, including granting users permission to access system functionality or restricting access to parts of the system1. Creating a new role is often necessary when there is a need to define a specific set of users who have access to a restricted app. This allows for granular control over who can interact with certain apps, ensuring that only authorized users can use them. While roles can also be used to manage access to labels, reports, and tags, the primary reason for creating a new role is typically related to controlling access to apps and their associated functionalities within the SOAR platform1.

References:

Splunk SOAR documentation on managing roles and permissions1.