SPLK-2003 Sample Questions Answers

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

 The main purpose of using a customized workbook is to guide user activity and coordination during event analysis and case operations. Workbooks can be customized to include different phases, tasks, and instructions for the users. The other options are not valid purposes of using a customized workbook. See Workbooks for more information.

Customized workbooks in Splunk SOAR are designed to guide users through the process of analyzing events and managing cases. They provide a structured framework for documenting investigations, tracking progress, and ensuring that all necessary steps are followed during incident response and case management. This helps in coordinating team efforts, maintaining consistency in response activities, and ensuring that all aspects of an incident are thoroughly investigated and resolved. Workbooks can be customized to fit the specific processes and procedures of an organization, making them a versatile tool for managing security operations.

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 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

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.

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.

The correct answer is B because the steps required to complete a full backup of a Splunk Phantom deployment are to first run the --backup --backup-type full command and then run the --setup command. The --backup command creates a backup file in the /opt/phantom/backup directory. The --backup-type full option specifies that the backup file includes all the data and configuration files of the Phantom server. The --setup command creates a configuration file that contains the encryption key and other information needed to restore the backup file. See Splunk SOAR Certified Automation Developer Track for more details.

Performing a full backup of a Splunk Phantom deployment involves using the command-line interface, primarily because Phantom's architecture and data management processes are designed to be managed at the server level for comprehensive backup and recovery. The correct sequence involves initiating a full backup first using the --backup --backup-type full option to ensure all configurations, data, and necessary components are included in the backup. Following the completion of the backup, the --setup option might be used to configure or verify the backup settings, although typically, the setup would precede backup operations in practical scenarios. This process ensures that all aspects of the Phantom deployment are preserved, including configurations, playbooks, cases, and other data, which is crucial for disaster recovery and system migration.

If, after configuring an external Splunk search head for search in SOAR, the search results do not include content that was previously returned, one possible issue could be that the user account configured on the SOAR side does not have the required permissions (such as the 'phantomsearch' capability) enabled on the Splunk side. This capability is necessary for the SOAR server to execute searches and retrieve results from the Splunk search head.

To limit the impact of custom code on the Visual Playbook Editor (VPE) in Splunk SOAR, custom code should be placed within a custom function block. Custom function blocks are designed to encapsulate code within a playbook, allowing users to input their own Python code and execute it as part of the playbook run. By confining custom code to these blocks, it maintains the VPE's performance and stability by isolating the custom code from the core functions of the playbook.

A custom function block is a way of adding custom Python code to your playbook, which can expand the functionality and processing of your playbook logic. Custom functions can also interact with the REST API in a customizable way. You can share custom functions across your team and across multiple playbooks to increase collaboration and efficiency. To create custom functions, you must have Edit Code permissions, which can be configured by an Administrator in Administration > User Management > Roles and Permissions. Therefore, option C is the correct answer, as it is the recommended way of placing custom code on the VPE, which limits the impact of custom code on the VPE performance and security. Option A is incorrect, because a custom container or a separate KV store are not valid ways of placing custom code on the VPE, but rather ways of storing data or artifacts. Option B is incorrect, because a separate code repository is not a way of placing custom code on the VPE, but rather a way of managing and versioning your code outside of Splunk SOAR. Option D is incorrect, because a separate container is not a way of placing custom code on the VPE, but rather a way of creating a new event or case.

1: Add custom code to your Splunk SOAR (Cloud) playbook with the custom function block using the classic playbook editor

 A step when configuring event forwarding from Splunk to Phantom is to create a Splunk alert that uses the event_forward.py script to send events to Phantom. This script will convert the Splunk events to CEF format and send them to Phantom as containers. The other options are not valid steps for event forwarding. See Forwarding events from Splunk to Phantom for more details.

Configuring event forwarding from Splunk to Phantom typically involves creating a Splunk alert that leverages a script (like event_forward.py) to automatically send triggered event data to Phantom. This setup enables Splunk to act as a detection mechanism that, upon identifying notable events based on predefined criteria, forwards these events to Phantom for further orchestration, automation, and response actions. This integration streamlines the process of incident management by connecting Splunk's powerful data analysis capabilities with Phantom's orchestration and automation framework.

DECIDED process is a core component of the SOAR automation engine that handles the execution of playbooks and actions. The DECIDED process can be restarted by restarting the automation service, which can be done from the command line using the service phantom restart command2. Restarting the automation service also restarts the playbook daemon, which is another core component of the SOAR automation engine that handles the loading and unloading of playbooks3. Therefore, option D is the correct answer, as it restarts both the DECIDED process and the playbook daemon. Option A is incorrect, because restarting the playbook daemon alone does not restart the DECIDED process. Option B is incorrect, because the System Health page does not provide an option to restart the DECIDED process or the automation service. Option C is incorrect, because the Administration > Server Settings page does not provide an option to restart the DECIDED process or the automation service.

In Splunk SOAR, if the DECIDED process, which is responsible for playbook execution, needs to be restarted, this can typically be done by restarting the automation (or phantom) service. This service manages the automation processes, including playbook execution. Restarting it can reset the DECIDED process, resolving issues related to playbook execution or process hangs.

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

The correct answer is D because synchronous execution has not been configured. Synchronous execution is a feature that allows you to control the order of execution of playbook blocks. By default, Phantom executes playbook blocks asynchronously, meaning that it does not wait for one block to finish before starting the next one. This can cause problems when you have dependencies between blocks or when you call other playbooks. To enable synchronous execution, you need to use the sync action in the run playbook block and specify the name of the next block to run after the called playbook completes. See Splunk SOAR Documentation for more details.

In Splunk SOAR, playbooks can be executed either synchronously or asynchronously. Synchronous execution ensures that a playbook waits for a called playbook to complete before proceeding to the next step. If the second playbook starts executing before the first one completes, it indicates that synchronous execution was not configured for the playbooks. Without synchronous execution, playbooks will execute independently of each other's completion status, leading to potential overlaps in execution. This behavior can be controlled by properly configuring the playbook execution settings to ensure that dependent playbooks complete their tasks in the desired order.

In 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.

In Splunk SOAR, when working on a case and analyzing events, items marked as significant evidence are aggregated for review. These evidence items can be collectively viewed on the Investigation page under the Evidence tab. This centralized view allows analysts to easily access and review all marked evidence related to a case, facilitating a streamlined analysis process and ensuring that key information is readily available for investigation and decision-making.

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 working with complex data paths in Splunk SOAR, particularly within playbooks, the dot (.) operator is used to access sub-elements within a larger data structure. This operator allows for the navigation through nested data, such as dictionaries or objects within JSON responses, enabling playbook actions and decision blocks to reference specific pieces of data within the artifacts or action results. This capability is crucial for extracting and manipulating relevant information from complex data sets during incident analysis and response automation.

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).

In Splunk Phantom, labels are used to categorize containers and trigger specific automated responses. When a container is created, labels can be assigned to it based on the nature of the event, type of incident, or other criteria. These labels are then matched against playbooks, which have label conditions defined within them. When the conditions are met, the corresponding playbooks are automatically executed. Labels do not directly control service level agreements, default severity, ownership, sensitivity, or app execution permissions.

The image shows a user interface of “splunk>phantom” with a search bar at the top, where a search for “Malware” has been initiated. The tabs labeled “Events,” “Indicators,” “Cases,” and “Tasks” suggest that the search functionality could span across various container fields within the Splunk SOAR environment. Typically, the search would include fields that are most relevant to the user’s query, which in this case, are likely to be the Event Name and Artifact Names. These fields are central to identifying and categorizing events and artifacts within Splunk SOAR, making them primary targets for a search term like “Malware” which is commonly associated with security events and indicators17.

References:

• Understanding containers - Splunk Documentation

In scenarios where there's a need to run different on_poll searches for a Splunk Cloud instance from Splunk SOAR, configuring a second Splunk asset for the additional query is a practical solution. Splunk SOAR's architecture allows for multiple assets of the same type to be configured with distinct settings. By setting up a second Splunk asset specifically for the second on_poll search query, users can maintain separate configurations and ensure that each query is executed in its intended context without interference. This approach provides flexibility in managing different data collection or monitoring needs within the same SOAR environment.

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.

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.

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:
• Search Configuration:
• Search Capabilities:
• References:

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, an event is a security occurrence that may require a response. It is ingested from a third-party source and can be labeled to group related events together. The default label for containers is “Events,” which signifies potential threats13. A case, on the other hand, is a container that holds several containers, consolidating multiple events into one logical management unit. Cases can include artifacts and external evidence such as screen captures, analyst notes, and event data from third-party products22. They are used to manage and analyze investigation data tied to specific security events and incidents, providing a structured approach to incident response34.

References:

• Manage the status, severity, and resolution of events in Splunk SOAR (Cloud) - Splunk Documentation
• Managing cases in SOAR - Splunk Lantern
• What is Splunk Phantom (Renamed to Splunk SOAR)? - BlueVoyant
• Overview of cases - Splunk Documentation