NSE6_FSM_AN-7.4 Aktuelle Prüfung - NSE6_FSM_AN-7.4 Prüfungsguide & NSE6_FSM_AN-7.4 Praxisprüfung

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Fortinet NSE6_FSM_AN-7.4 Exam Syllabus Topics:

Section	Objectives
Machine Learning, UEBA, and ZTNA	- Advanced analytics integration 1. Integrate UEBA data into rules and dashboards 2. Describe ZTNA integration in FortiSIEM operations 3. Configure ML configuration tasks
Rules and Subpatterns	- Analytics rules configuration 1. Use rule subpatterns, aggregation, and group by 2. Identify rule components 3. Configure FortiSIEM analytics rules
Analytics	- Query and event analysis 1. Perform CMDB and lookup table queries 2. Build queries from search results and events 3. Apply group by and data aggregation on search results 4. Perform nested query lookups
FortiEDR Security Settings and Policies	- Security configuration 1. Configure playbooks 2. Configure communication control policy 3. Explain Fortinet Cloud Service (FCS) 4. Configure security policies
Incidents, Notifications, and Remediation	- Incident management 1. Configure notification policies 2. Configure remediation options 3. Manage and tune incidents

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Fortinet NSE 6 - FortiSIEM 7.4 Analyst NSE6_FSM_AN-7.4 Prüfungsfragen mit Lösungen (Q88-Q93):

88. Frage
An analyst wants to create a rule from a new analytic search they just performed. Which method is the most efficient way for you to create the rule?

A. Copy and paste the raw analytics search text into a rule subpattern.
B. Save the search as a template, and create a new rule from the template.
C. Manually re-create the analytics search in the rule configuration.
D. Make a new rule using the Create Rule option in the Actions menu.

Antwort: D

Begründung:
Using the Create Rule option directly from the Actions menu is the most efficient method because it automatically converts the existing analytic search into a rule structure without requiring manual reconfiguration.

89. Frage
When configuring machine learning (ML), in which step can you modify how the model fits the training data set?

A. Statistics
B. Prepare Data
C. Design
D. Train

Antwort: A

Begründung:
The Statistics step is where you tune statistical model parameters that affect how closely the machine learning model fits the training data set, such as deviation-related settings used for anomaly detection.

90. Frage
Refer to the exhibit. Which value will the FortiSIEM parser use to populate the Application Name field?

A. Network.Service
B. SSL
C. Network Service
D. Network.Service: SSL

Antwort: B

Begründung:
The raw FortiGate event includes app="SSL", and the parser uses this value to populate the Application Name field.

91. Frage
What can you use to send data to FortiSIEM for user and entity behavior analytics (UEBA)?

A. FortiSIEM worker
B. SNMP
C. FortiSIEM agent
D. SSH

Antwort: C

92. Frage
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?

A. The selection in Fields to use for Prediction and Field to Predict must match.
B. Only one AVG type field must be selected under Fields to use for Prediction.
C. The Train factor must be 70% or greater.
D. Run Mode must be set to ML.

Antwort: C

Begründung:
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.

93. Frage
......

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