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Fortinet NSE6_FSM_AN-7.4 Exam Syllabus Topics:

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

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Fortinet NSE 6 - FortiSIEM 7.4 Analyst Sample Questions (Q30-Q35):

NEW QUESTION # 30
Refer to the exhibits. Which information will this analytics search display?

Answer: B

Explanation:
The search filters for Event Type values in the Logon Failure group and matches Destination IP addresses against the Device IP values returned from the Server Inventory CMDB report. This displays failed login events involving servers contained in the Server Inventory CMDB report.


NEW QUESTION # 31
Refer to the exhibit.

Which value would you expect the FortiSIEM parser to use to populate the Application Name field?

Answer: A

Explanation:
The correct answer is C. SSL . FortiSIEM receives raw logs, processes them through parsers, normalizes the extracted fields, classifies the event, and stores the structured data. The Study Guide explains the FortiSIEM process flow: data is collected, processed by the parsing engine, normalized, classified, and then stored. It further states that normalization extracts individual fields from raw events and maps those fields to a common schema. The FortiSIEM 7.4 User Guide describes a parser as a file containing instructions for the parser module to convert a raw log into event attributes. In the exhibit, the raw FortiGate log includes values such as profiletype= " applist " , appcat= " Network.Service " , and app= " SSL " . The field that directly represents the application value is app= " SSL " . Therefore, the parser would use SSL to populate the normalized Application Name field. applist describes the profile type, Network.Service is the application category, and wan1 is the interface, not the application name.


NEW QUESTION # 32
Refer to the exhibit.

If you group the events by Reporting Device , Reporting IP , and Application Category , how many results will FortiSIEM display?

Answer: E

Explanation:
FortiSIEM grouping works by combining events that have the same values for all selected Group By attributes. The Study Guide's rule and subpattern example states that when multiple events have the same Group By values, "they are grouped together in one row, and the count column tracks the number of events for each of those rows." In the exhibit, the selected grouping fields are Reporting Device, Reporting IP, and Application Category. The table contains six raw rows, but two rows share the same grouped combination:
FW01 / 10.1.1.1 / DB . Those two rows are collapsed into one grouped result. The other combinations are unique: FW02 / 10.1.1.2 / WebApp, FW01 / 10.1.1.1 / SSH, FW03 / 10.1.1.3 / DB, and FW04 / 10.1.1.4 / SSH. That creates five grouped rows in total. FortiSIEM does not display six because grouping removes duplicate combinations, and it does not display four because only one duplicate combination exists.


NEW QUESTION # 33
Refer to the exhibit. When the subpattern is matched, what does the time condition of 60 seconds mean?

Answer: A

Explanation:
The 60-second time window defines the aggregation and evaluation period for the rule. FortiSIEM evaluates whether the specified subpattern conditions occur within that 60-second interval before triggering the rule.


NEW QUESTION # 34
When configuring machine learning (ML), in which step can you modify how the model fits the training data set?

Answer: C

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 # 35
......

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