Explanation
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.

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 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.

In Splunk SOAR, the Investigation page is where users can delve into the details of containers, artifacts, and actions. It provides a comprehensive view of the incident or event under investigation, including the JSON data associated with containers. This JSON data represents the structured information about the container, including its attributes, artifacts, and actions taken within the playbook. Options A, C, and D do not typically provide a direct view of the container’s JSON data, making option B the correct answer for where a user can view this information within SOAR.
A container is the top-level data structure that SOAR playbook APIs operate on. Every container is a structured JSON object which can nest more arbitrary JSON objects, that represent artifacts. A container is the top-level object against which automation is run. To view the JSON data for a container, you need to navigate to the Investigation page, which shows the details of a container, such as its name, label, owner, status, severity, and artifacts. On the Investigation page, you can click on the JSON tab, which displays the JSON representation of the container and its artifacts. Therefore, option B is the correct answer, as it states where in SOAR a user can view the JSON data for a container. Option A is incorrect, because the analyst queue is not where a user can view the JSON data for a container, but rather where a user can view the list of containers assigned to them or their team. Option C is incorrect, because the data ingestion display is not where a user can view the JSON data for a container, but rather where a user can view the status and configuration of the data sources that ingest data into SOAR. Option D is incorrect, because the audit log is not where a user can view the JSON data for a container, but rather where a user can view the history of actions performed on the SOAR system, such as creating, updating, or deleting objects.
1: Understanding containers in Splunk SOAR (Cloud)

Explanation
An individual asset action can be manually started with the > action button in the Investigation page. This allows the user to select an asset and an action to perform on it. The other options are not valid ways to start an asset action manually. See Performing asset actions for more information.

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 default embedded search engine used by Splunk SOAR (formerly known as Phantom) is the embedded Splunk search engine. Here’s a detailed explanation:
Embedded Splunk Search Engine:
Splunk SOAR uses an embedded, preconfigured version of Splunk Enterprise as its native search engine.
This integration allows for powerful searching capabilities within Splunk SOAR, leveraging Splunk’s robust search and indexing features.
Search Configuration:
While the embedded Splunk search engine is the default, organizations have the option to configure Splunk SOAR to use a different Splunk Enterprise deployment or an external Elasticsearch instance.
This flexibility allows organizations to tailor their search infrastructure to their specific needs and existing environments.
Search Capabilities:
The embedded Splunk search engine enables users to perform complex searches, analyze data, and generate reports directly within the Splunk SOAR platform.
It supports the full range of Splunk’s search processing language (SPL) commands, functions, and visualizations.
References:
Splunk SOAR Documentation: Configure search in Splunk Phantom1.
Splunk SOAR Documentation: Configure search in Splunk SOAR (On-premises)2.
In summary, the embedded Splunk search engine is the default search engine in Splunk SOAR, providing a seamless and powerful search experience for users within the platform.

In Splunk SOAR, if a user needs to run two different on_poll searches for a Splunk Cloud instance, the way to achieve this is to configure a second Splunk asset specifically for the second query. Each asset can be configured with its own on_poll search, allowing multiple searches to be run at their respective intervals. This method provides flexibility and ensures that each search can be managed and configured individually.
The correct way to run two different on_poll searches from a Splunk Cloud instance to Splunk SOAR is to configure a second Splunk asset with the second query. Each Splunk asset in Splunk SOAR can only have one query for the on_poll event, which defines which events to pull in and when to pull them in1. Therefore, if you need to run two different queries, you need to create two separate Splunk assets and configure them with the respective queries. The other options are either not possible or not effective for this purpose. For example:
*Installing a second Splunk app in Splunk SOAR will not help, as the app is just a container for the actions and assets, not the source of the data2.
*Configuring the second query in the Splunk App for SOAR Export will not work, as this app is used to forward events from the Splunk platform to Splunk SOAR, not to pull them in3.
*Entering the two queries in the asset as comma separated values will not work, as the asset will only accept one valid query for the on_poll event1.

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.
References:
* Splunk SOAR Playbook Development Guide.
* Splunk SOAR Documentation on Decision Blocks and Action Result Evaluation.

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 default log level for system health debug logs in Splunk SOAR is typically set to INFO. This log level provides a balance between verbosity and relevance, offering insights into the operational status of the system without the detailed granularity of DEBUG or the limited scope of WARN and ERROR levels.
The default log level for system health debug logs is INFO. This means that only informational messages and higher severity messages (such as WARN, ERROR, or CRITICAL) are written to the log files. You can adjust the logging level for each daemon running in Splunk SOAR to help debug or troubleshoot issues. For more details, see Configure the logging levels for Splunk SOAR (On-premises) daemons.

Explanation
The Logging option for a playbook’s settings enables more detailed logging information to be available in the Investigation page. This can help with debugging and troubleshooting the playbook execution. The other options are not related to the Logging option. See Playbook settings for more information.

Explanation
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.

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.

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.

Custom CEF fields can be created with a name and a data type. The name must be unique and the data type must be one of the following: string, int, float, bool, or list. The severity is not a valid option for custom CEF fields. See Creating custom CEF fields for more details. When creating Custom Common Event Format (CEF) fields in Splunk SOAR (formerly Phantom), the essential values you need to specify are the “Name” of the field and the “Data Type.” The “Name” is the identifier for the field, while the “Data Type” specifies the kind of data the field will hold, such as string, integer, IP address, etc. This combination allows for the structured and accurate representation of data within SOAR, ensuring that custom fields are compatible with the platform’s data processing and analysis mechanisms.