最新NSE6_FSM_AN-7.4|完璧なNSE6_FSM_AN-7.4トレーリングサンプル試験|試験の準備方法Fortinet NSE 6 - FortiSIEM 7.4 Analystシュミレーション問題集

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Fortinet NSE6_FSM_AN-7.4 Exam Syllabus Topics:

SectionObjectives
Topic 1: 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
Topic 2: Analytics and Search- Query and Event Analysis
  • 1. Perform nested query lookups
  • 2. Apply group by and data aggregation
  • 3. Build queries from search results and events
  • 4. Perform CMDB and lookup table queries
Topic 3: Advanced Analytics and Integrations- ML, UEBA, and ZTNA
  • 1. Integrate UEBA data into rules and dashboards
  • 2. Describe ZTNA integration in FortiSIEM operations
  • 3. Configure machine learning (ML) settings
Topic 4: 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

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Fortinet NSE 6 - FortiSIEM 7.4 Analyst 認定 NSE6_FSM_AN-7.4 試験問題 (Q33-Q38):

質問 # 33
Refer to the exhibit.

The configuration shown in the exhibit is incorrect.
What must you change to allow this configuration to be successfully applied to FortiSIEM?

正解:A

解説:
The correct answer is A . In FortiSIEM machine learning training, the Train factor controls how much of the prepared dataset is used for training and how much is reserved for testing. The FortiSIEM 7.4 User Guide states in the regression training procedure that the analyst must choose the Train factor and that it "should be greater than 70%." It also explains the meaning of the value: 70% of the data is used for training and 30% is used for testing. This directly supports option A. Option B is incorrect because the supported running modes include Local, Local Auto, and AWS; "ML" is not the required run mode. Option C is incorrect because regression can use multiple fields for prediction; the guide instructs the user to choose the fields to use for prediction and the field to predict. Option D is incorrect because prediction input fields and target field should not be the same. Regression builds a model to predict a target field based on other fields, so the configuration must use a valid Train factor rather than matching predictor and target fields.


質問 # 34
Refer to the exhibit.

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

正解:E

解説:
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.


質問 # 35
Several new internal servers are generating incidents and must be excluded from several FortiSIEM rules. How must you tune rules to exclude several undiscovered devices from rules?

正解:A

解説:
Creating a device group containing the servers and applying that group as a filter exclusion in the relevant rules is the proper tuning method for excluding multiple undiscovered devices from generating incidents across several rules.


質問 # 36
Which run mode takes the most time to perform machine learning tasks?

正解:A


質問 # 37
Refer to the exhibit.

What is this rule attempting to match? (Choose one answer)

正解:B

解説:
The rule is matching VPN logon failure events where the Source Country is outside the configured home country . In the exhibit, the filter section shows Event Type IN EventTypes: VPN Logon Failure and Source Country NOT IN GeoCountries: My Home . That means the source must be outside the home- country geo group. The aggregate condition shows COUNT(Matched Events) > = 3 , so the rule is looking for at least three matching failed VPN logon events. The Group By section uses Source IP and User , so FortiSIEM evaluates the count per unique source IP and user combination, not by different countries.
The FortiSIEM Study Guide explains that a rule subpattern contains three components: Filter , Aggregate , and Group By . It states that the filter identifies the matching event group, the aggregate function specifies how many events must match, and Group By combines events with the same grouped attributes into one row while the count tracks those events.
Option A is wrong because the rule does not count different countries. Options C and D are wrong because the source country is explicitly NOT IN My Home, not inside the home country.


質問 # 38
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