NSE6_FSM_AN-7.4 New Test Materials & NSE6_FSM_AN-7.4 Pass4sure Pass Guide

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Fortinet NSE6_FSM_AN-7.4 Exam Syllabus Topics:

SectionObjectives
Rules and Incident Management- Incidents and Notifications
  • 1. Configure remediation options
  • 2. Configure notification policies
  • 3. Manage and tune incidents
- Rules and Alerts
  • 1. Utilize rule subpatterns, aggregation, group by
  • 2. Configure FortiSIEM analytics rules
  • 3. Identify various rule components
FortiEDR and Security Policy Integration- FortiEDR Security Configuration
  • 1. Explain Fortinet Cloud Service (FCS)
  • 2. Configure communication control policy
  • 3. Configure security policies
  • 4. Configure playbooks
Analytics and Search- Query and Event Analysis
  • 1. Apply group by and data aggregation
  • 2. Perform nested query lookups
  • 3. Perform CMDB and lookup table queries
  • 4. Build queries from search results and events
Advanced Analytics and Integrations- ML, UEBA, and ZTNA
  • 1. Configure machine learning (ML) settings
  • 2. Describe ZTNA integration in FortiSIEM operations
  • 3. Integrate UEBA data into rules and dashboards

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Fortinet NSE 6 - FortiSIEM 7.4 Analyst Sample Questions (Q59-Q64):

NEW QUESTION # 59
How does FortiSIEM update the incident details if the same rule triggers repeatedly?

Answer: D

Explanation:
When the same rule triggers repeatedly for an existing incident, FortiSIEM updates the Incident Count and refreshes the Last Seen timestamp while maintaining the original incident record.


NEW QUESTION # 60
In FortiSIEM, which database stores discovery information?

Answer: C

Explanation:
FortiSIEM stores discovery information - such as device attributes, IPs, roles, and relationships - in the Configuration Management Database (CMDB). The CMDB maintains an up-to-date inventory of all discovered assets, serving as the central repository for device and infrastructure configuration data.


NEW QUESTION # 61
Which two data areas can you use for user and entity behavior analytics (EBA) machine learning models?
(Choose two.)

Answer: A,D

Explanation:
The correct answers are A. Process and C. Resources. FortiSIEM UEBA/EBA analytics uses endpoint and behavior-related telemetry to identify abnormal activity. The Study Guide explains that the UEBA incident dashboard shows incidents that the AI module creates based on alerts received from FortiInsight. The UEBA attribute list includes the incident name, host, application, user, tag, and activity. This aligns with process-oriented activity monitoring from endpoint agents. The performance and baseline lessons also explain that FortiSIEM collects performance and availability data from devices and applications, including resource-utilization metrics, and uses that information to build baselines and detect anomalous activity. Resource behavior includes CPU, memory, disk, I/O, and similar utilization patterns, which are valid behavioral-model inputs. Location and Network are important FortiSIEM context areas, but in this question's EBA machine learning model choices, the valid model areas are process behavior and resource behavior. Process reflects what is running or being executed; Resources reflects system or application utilization behavior. Together, these are the two data areas used for EBA machine learning models.


NEW QUESTION # 62
You want FortiSIEM to automatically add three zero trust network access (ZTNA) tags to a device when that device triggers a custom rule. You want FortiSIEM to push these ZTNA tags to multiple FortiClient EMS servers in the organization. How can you accomplish this?

Answer: B

Explanation:
A single playbook can contain multiple connectors, allowing FortiSIEM to push the required ZTNA tags simultaneously to multiple FortiClient EMS servers when the custom rule is triggered.


NEW QUESTION # 63
When configuring machine learning (ML), in which step can you modify how the model fits the training data set?

Answer: A

Explanation:
The correct answer is B. Train. In FortiSIEM machine learning, the Train step is where the model is built from the prepared dataset and where model-fitting behavior can be adjusted. The FortiSIEM 7.4 User Guide explains that after preparing data, the analyst goes to Analytics > Machine Learning > Train, selects the machine learning task, chooses the algorithm, selects the prediction/target fields when required, and chooses the Train factor, which determines how much data is used for training versus testing. The guide states that the Train factor should be greater than 70%, meaning 70% of the data is used for training and 30% for testing. It also explains that model quality metrics show how accurately the algorithm predicts the field. For regression, lower MAE means a better fit, and R2 shows how well predictions approximate real data points. Most importantly, the guide states: "If you want to change the algorithm parameters and re-train, then click Tune & Train, change the parameters and click Save
& Train." This confirms that modifying how the model fits the training dataset is done in the Train step, not Prepare Data, Statistics, or Design.


NEW QUESTION # 64
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