NSE7_SOC_AR-7.6 Practice Questions

Slot 1: data Slot 2: json_query Slot 3: ( " results[?type== ' FileHash-MD5 ' ] " ) Slot 4: value

Final Expression: {{ vars.artifacts.data | json_query( " results[?type== ' FileHash-MD5 ' ] " ) .value }}

In FortiSOAR 7.6 , advanced data manipulation within playbooks often requires the use of JMESPath queries via the json_query Jinja filter. To extract specific data from a complex JSON object (like the vars.artifacts dictionary shown in the exhibit), the analyst must follow the structural hierarchy:

Slot 1 (data): Based on the exhibit, the root of the artifact information is located under vars.artifacts.data. Therefore, " data " is the starting point for the filter.

Slot 2 (json_query): To perform advanced filtering (searching for a specific type), the json_query filter must be applied. This allows the playbook to traverse the list and find items matching a specific key-value pair.

Slot 3 ( " results[?type== ' FileHash-MD5 ' ] " ): This is the JMESPath expression. It looks into the results array and applies a filter [?...] to find only those objects where the type attribute exactly matches FileHash-MD5.

Slot 4 (value): Once the correct object(s) are found, the expression needs to return the actual hash. In the JSON exhibit, the MD5 string is stored in the key named value.

Why other options are incorrect:

tojson: This filter converts a dictionary/list into a JSON string, which would break the ability to further query the object for the " value " field.

results (as a standalone slot): While " results " is part of the path, it is handled inside the json_query string to allow for conditional filtering.

Understanding the Playbook Configuration:

The playbook named " Update Asset and Identity Database " is designed to update the FortiAnalyzer Asset and Identity database with endpoint and user information.

The exhibit shows the playbook with three main components: ON_SCHEDULE STARTER, GET_ENDPOINTS, and UPDATE_ASSET_AND_IDENTITY.

Analyzing the Components:

ON_SCHEDULE STARTER: This component indicates that the playbook is triggered on a schedule, not on-demand.

GET_ENDPOINTS: This action retrieves information about endpoints, suggesting it interacts with an endpoint management system.

UPDATE_ASSET_AND_IDENTITY: This action updates the FortiAnalyzer Asset and Identity database with the retrieved information.

Evaluating the Options:

Option A: The actions shown in the playbook are standard local actions that can be executed by the FortiAnalyzer, indicating the use of a local connector.

Option B: There is no indication that the playbook uses a FortiMail connector, as the tasks involve endpoint and identity management, not email.

Option C: The playbook is using an " ON_SCHEDULE " trigger, which contradicts the description of an on-demand trigger.

Option D: The action " GET_ENDPOINTS " suggests integration with an endpoint management system, likely FortiClient EMS, which manages endpoints and retrieves information from them.

Conclusion:

The playbook is configured to use a local connector for its actions.

It interacts with FortiClient EMS to get endpoint information and update the FortiAnalyzer Asset and Identity database.

In FortiSOAR 7.6 , the playbook engine utilizes the powerful ipaddr family of Jinja filters (derived from the Ansible netaddr library) to manipulate network data. To isolate public IPv6 addresses from a mixed list, the order of operations in the filter chain ensures the correct data is extracted:

Double Filtering Sequence (B): In the expression {{ vars.ip_list | ipaddr( ' public ' ) | ipv6 }}, the first filter ipaddr( ' public ' ) processes the entire list and retains only public addresses, including both IPv4 and IPv6 versions. The second filter in the pipe, | ipv6, then takes that subset of public addresses and filters them again to keep only those that conform to the IPv6 standard. The final result is a list containing only public IPv6 addresses.

Version-First Filtering (D): In the expression {{ vars.ip_list | ipv6 | ipaddr( ' public ' ) }}, the logic is reversed but equally effective. The first filter | ipv6 immediately strips all IPv4 and non-IP strings from the list, leaving only IPv6 addresses (both private and public). The subsequent filter | ipaddr( ' public ' ) then evaluates these IPv6 addresses and discards any that fall within the private/unique-local ranges (like ULA or link-local), resulting in the same set of public IPv6 addresses.

Why other options are incorrect:

A (ipv6addr ' public ' ): While ipv6addr is a valid filter in many Ansible environments, FortiSOAR ' s standard documentation for manual task creation and data manipulation primarily emphasizes the use of the generic ipaddr filter with specific flags or chained version filters (like | ipv6) to ensure cross-compatibility with the underlying Python libraries used by the SOAR engine.

C (!private syntax): The ipaddr filter utilizes specific keywords for classification. While " not private " is the logical requirement, the filter expects positive assertions such as ' public ' , ' private ' , or ' multicast ' . The !private syntax is not a supported or documented operator for this filter within the Fortinet SOC ecosystem.

Step 1: Create a parameter in the child playbook.

Step 2: Map data to the parameter in the Reference a Playbook step in the parent playbook.

Step 3: Apply the parameter to the Disable User Account connector action.

Exact Extract: “To make the child playbook function properly, you must pass information from the parent playbook to the child playbook. If the child playbook is not aware of the AD user queried in the parent playbook, the action to disable a user cannot be completed. The easiest way to accomplish this task is to create a parameter in the child playbook.”

Exact Extract: “After it is created, you can go back to your parent playbook to the Reference a Playbook step for the associated child playbook. You will find a new field to map data to. After the data is mapped in the parent playbook, when you return to the child playbook, you can see the parameter is available in the Dynamic Values window.”

The correct order is child parameter → parent mapping → child connector usage . The child playbook must first expose an input parameter, such as user_name, because a referenced child playbook does not automatically inherit every variable from the parent workflow. After the child parameter exists, the parent playbook’s Reference a Playbook step displays that parameter as a mappable input field. The parent can then pass the AD username discovered earlier in the workflow. Finally, inside the child playbook, that parameter is selected from Dynamic Values and applied to the Disable User Account Active Directory connector action.

The option “Create a parameter in the parent playbook” is not required. The parent only maps data into the child’s parameter. The option “Create a manual trigger and assign the user to a new variable” is also wrong because the parent playbook in the exhibit already starts from an On Create trigger and already retrieves the AD account details before referencing the child playbook.

Technical Deep Dive: Referenced playbooks are modular automation units. Their clean design depends on explicit input parameters, just like function arguments in programming. This avoids brittle dependencies on parent-step variable names and allows the same child playbook to be reused by multiple parent workflows. Hardware offloading such as FortiGate NP/CP acceleration is irrelevant here because this is FortiSOAR workflow orchestration, not FortiGate data-plane traffic processing.

Understanding Playbook Variables:

Playbook tasks in Security Operations Center (SOC) playbooks use variables to pass and manipulate data between different steps in the automation process.

Variables help in dynamically handling data, making the playbook more flexible and adaptive to different scenarios.

Types of Variables:

Input Variables:

Input variables are used to provide data to a playbook task. These variables can be set manually or derived from previous tasks.

They act as parameters that the task will use to perform its operations.

Output Variables:

Output variables store the result of a playbook task. These variables can then be used as inputs for subsequent tasks.

They capture the outcome of the task ' s execution, allowing for the dynamic flow of information through the playbook.

Other Options:

Create: Not typically referred to as a type of variable in playbook tasks. It might refer to an action but not a variable type.

Trigger: Refers to the initiation mechanism of the playbook or task (e.g., an event trigger), not a type of variable.

Conclusion:

The two types of variables used in playbook tasks are input and output .