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NEW QUESTION # 49
Refer to the exhibit.
What is the correct Jinja expression to filter the results to show only the MD5 hash values?
{{ [slot 1]|[slot 2] [slot 3].[slot 4] }}
Select the jinja expression in the left column, hold and drag it to a blank position on the right. Place the four correct steps in order, placing the first step in the first slot. Once you place an expression, you can move it again if you want to change your answer before moving to the next question. You need to drop four jinja expressions in the work area.
Select and drag the screen divider to change the viewable area of the source and work areas.
Answer:
Explanation:
Explanation:
Slot 1: vars.artifacts
Slot 2: json_query
Slot 3: ( " data.results[?type== ' FileHash-MD5 ' ]
Slot 4: value
Exact Extract: "You can assign specific fields from your connector action output to their own variables and further manipulate the data using Jinja filters. This approach allows you to efficiently extract, clean, and prepare information for use in later steps of your automation." Exact Extract: The guide shows the same Jinja pattern:
{{ vars.steps.Advanced_Search_Query.data.events | json_query( ' [].attributes.srcIpAddr ' ) | unique }} and explains that similar Jinja logic applies to extracting other variables from structured output.
The correct expression is {{ vars.artifacts | json_query( " data.results[?type== ' FileHash-MD5 ' ].value " ) }} because the JSON object stores artifact records under vars.artifacts.data.results. Each object in results has a type and a value. The filter condition [?type== ' FileHash-MD5 ' ] selects only records whose type is FileHash-MD5 , and .value returns only the MD5 hash strings, not the full objects.
tojson is unnecessary because the data is already structured and queryable. results, data, and value alone are incomplete because they do not filter by artifact type. The key operation is json_query , which uses a JMESPath-style expression to filter a list and project only the required field.
Technical Deep Dive: This is a classic FortiSOAR playbook parsing pattern. Use json_query when the object is already JSON-like and you know the path. The expression returns a list such as:
[ " 6aad63bcc3dd4e148f3724808955f912 " , " 9fd2b1c0e4a37658bca9d0f1e2c34567 " ] This is automation-layer data extraction. FortiGate NP/CP offloading is irrelevant because no packet inspection or firewall data-plane forwarding is involved.
NEW QUESTION # 50
You need to create a nested query in FortiSIEM that satisfies the following conditions:
Find all devices discovered by any FortiSIEM Windows Agent.
From those devices, identify those that have generated Windows Login Failure events.
Which two query components should be used for this nested query? Choose two answers.
Answer: A,C
Explanation:
Exact Extract: "The example on this slide shows a structured search that references the CMDB... Attribute:
Reporting IP Operator: IN Value: Devices: Windows... Attribute: Event Type Operator: IN Value:
EventTypes: Logon Failure."
Exact Extract: "FortiSIEM agents: File, log monitoring, and UEBA." The guide also explains that Windows systems can use the FortiSIEM Windows agent for log forwarding and monitoring.
The correct answers are A and C. The first requirement is CMDB-based: identify devices discovered by a FortiSIEM Windows Agent. That belongs in an inner CMDB query because it produces the device set. The second requirement is event-based: from that device set, find devices that generated Windows Login Failure events. That belongs in the outer Event Query, where the event condition can reference the device results from the inner CMDB query.
Technical Deep Dive: The clean nested-query logic is: inner query defines the population of relevant assets; outer query tests whether that population has produced the target events. FortiSIEM commonly uses CMDB- backed device groups with event filters such as Event Type IN EventTypes: Logon Failure. This avoids manually maintaining long IP lists and keeps detection tied to live inventory.
NEW QUESTION # 51
Which two types of variables can you use in playbook tasks? (Choose two.)
Answer: A,B
Explanation:
* 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 areinputandoutput.
References:
Fortinet Documentation on Playbook Configuration and Variable Usage.
General SOC Automation and Orchestration Practices.
NEW QUESTION # 52
Refer to the exhibit.
Which method most effectively reduces the attack surface of this organization? (Choose one answer)
Answer: A
Explanation:
Exact Extract: "Segment the network. Macrosegmentation: Isolate different networks and VLANs from one another. Microsegmentation: Isolate the workloads of individual applications." The guide further explains:
"With macrosegmentation, you can isolate broadcast domains and implement different levels of security based on the network and VLANs a device belongs to. For example, you can have a 'Guest' network with limited access, whereas the 'IT' network can access critical devices such as the 'Server' network." The correct answer is C because the exhibit shows a flat or broadly connected environment where multiple LAN departments-QA, Engineering, Sales, and IT-can reach a server network containing sensitive services such as web, file, email, DNS, and a domain controller. The most effective way to reduce the attack surface is macrosegmentation , meaning separation of major network zones or VLANs and enforcement of access policy between them. That limits unnecessary lateral movement and restricts which departments can access critical servers.
Option A improves visibility but does not reduce exposure by itself. Option B improves inspection depth but does not reduce which systems can communicate. Option D is a valid general hardening practice, but the exhibit does not show unused devices; it shows multiple business networks and server services requiring segmentation.
Technical Deep Dive: On FortiGate, macrosegmentation is normally implemented with VLANs, zones, firewall policies, and least-privilege rules between departments and server subnets. Example design:
separate QA, Engineering, Sales, IT, and Server VLANs; then allow only required traffic such as Sales to web services, IT to domain controllers, and DNS from approved clients. NP/CP offloading can still accelerate eligible firewall sessions, but once UTM/deep inspection is enabled, some traffic may be handled by CPU or CP depending on the model and inspection profile.
NEW QUESTION # 53
You need to create a nested query in FortiSIEM that satisfies the following conditions:
* Find all devices discovered by any FortiSIEM Windows Agent.
* From those devices, identify those that have generated Windows Login Failure events.
Which two query components should be used for this nested query? Choose two answers.
Answer: A,C
Explanation:
Exact Extract: "The example on this slide shows a structured search that references the CMDB...
Attribute: Reporting IP Operator: IN Value: Devices: Windows... Attribute: Event Type Operator: IN Value: EventTypes: Logon Failure." Exact Extract: "FortiSIEM agents: File, log monitoring, and UEBA." The guide also explains that Windows systems can use the FortiSIEM Windows agent for log forwarding and monitoring.
The correct answers are A and C. The first requirement is CMDB-based: identify devices discovered by a FortiSIEM Windows Agent. That belongs in an inner CMDB query because it produces the device set. The second requirement is event-based: from that device set, find devices that generated Windows Login Failure events. That belongs in the outer Event Query, where the event condition can reference the device results from the inner CMDB query.
Technical Deep Dive: The clean nested-query logic is: inner query defines the population of relevant assets; outer query tests whether that population has produced the target events. FortiSIEM commonly uses CMDB-backed device groups with event filters such as Event Type IN EventTypes: Logon Failure.
This avoids manually maintaining long IP lists and keeps detection tied to live inventory.
NEW QUESTION # 54
......
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