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Fortinet NSE6_FSM_AN-7.4 Exam Syllabus Topics:

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

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Fortinet NSE 6 - FortiSIEM 7.4 Analyst Sample Questions (Q85-Q90):

NEW QUESTION # 85
You want to build a query that includes only events that contain a username.
What is a valid analytics query that will perform this type of search?

Answer: A

Explanation:
A query using IS NOT NULL returns only events where the User attribute has a value. This is the valid way to filter analytics results to events that contain a username.


NEW QUESTION # 86
What feature defines when an incident is created by FortiSIEM?

Answer: C


NEW QUESTION # 87
What are four incident status options on FortiSIEM?

Answer: A

Explanation:
FortiSIEM incidents can use status values that track the incident lifecycle, including active investigation, closure, clearing, and resolution. These statuses help analysts manage whether an incident is still open, has been cleared by conditions, has been closed, or has been resolved.


NEW QUESTION # 88
Refer to the exhibit.

You want to create a dashboard like the one shown in the exhibit on your FortiSIEM device.
Which item defines the data that these widgets display?

Answer: B

Explanation:
FortiSIEM dashboard widgets display data based on reports. Each widget uses an underlying report or analytics query to define the dataset, aggregation, and visualization shown on the dashboard.


NEW QUESTION # 89
Refer to the exhibit.

What will happen when a device being analyzed by the machine learning configuration shown in the exhibit has consistently high memory utilization?

Answer: A

Explanation:
The exhibit shows a machine learning Regression configuration. In FortiSIEM regression, selected numerical fields are used as predictors, and another field is selected as the value to predict. The FortiSIEM 7.4 User Guide's Machine Learning section lists Regression separately and describes the training workflow, including selecting fields used by the algorithm. The exhibit shows Memory Utilization, Sent Bytes, and Received Bytes selected under Fields to use for Prediction, while CPU Utilization is selected under Field to Predict. This means Memory Utilization is an input variable used by the model. If memory utilization is consistently high during training or retraining, that higher value becomes part of the learned model pattern. FortiSIEM does not automatically trigger a high-memory incident merely because a regression input value is high; an incident would require inference behavior and an anomaly action or rule configuration. Option C is also wrong because FortiSIEM does not dynamically lower a CPU trigger threshold just because memory is high. The correct behavior is model update based on the higher observed input value.


NEW QUESTION # 90
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