最有效的NSE6_FSM_AN-7.4考古題分享-最新考試題庫幫助妳壹次性通過考試NSE6_FSM_AN-7.4：Fortinet NSE 6 - FortiSIEM 7.4 Analyst

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Fortinet NSE6_FSM_AN-7.4 Exam Syllabus Topics:

Section	Objectives
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
Analytics and Search	- Query and Event Analysis 1. Apply group by and data aggregation 2. Perform nested query lookups 3. Build queries from search results and events 4. Perform CMDB and lookup table queries
FortiEDR and Security Policy Integration	- FortiEDR Security Configuration 1. Configure playbooks 2. Explain Fortinet Cloud Service (FCS) 3. Configure security policies 4. Configure communication control policy
Rules and Incident Management	- Incidents and Notifications 1. Configure notification policies 2. Manage and tune incidents 3. Configure remediation options - Rules and Alerts 1. Utilize rule subpatterns, aggregation, group by 2. Identify various rule components 3. Configure FortiSIEM analytics rules

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最新的 NSE 6 Network Security Specialist NSE6_FSM_AN-7.4 免費考試真題 (Q24-Q29):

問題 #24
Which two data areas can you use for user and entity behavior analytics (UEBA) machine learning models? (Choose two.)

A. resources
B. location
C. process
D. network

答案：B,D

解題說明：
FortiSIEM's UEBA models analyze user and entity behavior by correlating data such as location (for detecting unusual logins or access patterns) and network activity (for identifying abnormal communication or traffic behaviors). These data areas enable the system to build baseline profiles and detect anomalies indicating potential insider threats or compromised accounts.

問題 #25
You need a model that predicts a target field based on other fields in a dataset and then triggers an anomaly if the value does not match the prediction. Which machine learning (ML) algorithm will you use to build this type of model?

A. Forecasting
B. Classification
C. Regression
D. Anomaly detection

答案：C

問題 #26
Refer to the exhibit. Why are some of the fields highlighted in red?

A. Multiple unique values cannot be grouped.
B. The attribute COUNT(Matched Events) is an invalid expression.
C. No Raw Event Log attribute information is available.
D. The Event Receive Time attribute is a valid choice to sort by.

答案：A

解題說明：
The highlighted fields represent attributes that contain multiple unique values and therefore cannot be used together in a grouped aggregation display in the current configuration.

問題 #27
Refer to the exhibit. You want to use a machine learning (ML) model to train data with the following characteristics shown in the exhibit. Which ML model is the best fit to match the data in the exhibit?

A. Forecasting
B. Elliptical envelope
C. Clustering
D. Anomaly detection

答案：C

解題說明：
The data points form several distinct groups, so a clustering model is the best fit for identifying natural groupings within the dataset.

問題 #28
Refer to the exhibit.

How was this incident cleared?

A. FortiSIEM cleared the incident automatically after 24 hours.
B. The endpoint was rebooted and sent an all-clear signal to FortiSIEM.
C. The incident was cleared automatically by the rule.
D. The analyst manually cleared the incident from the incident table.

答案：C

解題說明：
The Incident Status shows " Auto Cleared " , and the Cleared Reason states: " Rule has not been triggered for
20 minutes. " This indicates that the incident was automatically cleared by the rule logic after a defined period of inactivity.
The correct answer is C because the exhibit shows the incident status as Auto Cleared and the cleared reason indicates that the rule condition was no longer being triggered. The Study Guide explains that FortiSIEM supports clear conditions and auto-clearing behavior at the rule level. It states that if a time-based clear condition is configured, FortiSIEM can auto-clear the incident after the last occurrence if the trigger condition no longer exists. It also explains pattern-based clear behavior: FortiSIEM evaluates clear-condition subpatterns and compares attributes from the clear condition with the original incident attributes. If the configured attributes match, the incident status is set to auto cleared. In the exhibit, the cleared reason says the rule has not been triggered for a defined number of minutes. That is not a manual action by the analyst and not an endpoint-generated all-clear signal. It is FortiSIEM's rule-based clearing logic. Option B is also wrong because the exhibit shows a specific rule inactivity period, not a generic 24-hour timeout.

問題 #29
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NSE6_FSM_AN-7.4信息資訊: https://www.vcesoft.com/NSE6_FSM_AN-7.4-pdf.html