Examinations NSE6_FSM_AN-7.4 Actual Questions - Your Powerful Weapon to Pass 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. Configure machine learning (ML) settings 2. Describe ZTNA integration in FortiSIEM operations 3. Integrate UEBA data into rules and dashboards
Analytics and Search	- Query and Event Analysis 1. Apply group by and data aggregation 2. Perform nested query lookups 3. Perform CMDB and lookup table queries 4. Build queries from search results and events
FortiEDR and Security Policy Integration	- FortiEDR Security Configuration 1. Explain Fortinet Cloud Service (FCS) 2. Configure playbooks 3. Configure security policies 4. Configure communication control policy
Rules and Incident Management	- Rules and Alerts 1. Configure FortiSIEM analytics rules 2. Identify various rule components 3. Utilize rule subpatterns, aggregation, group by - Incidents and Notifications 1. Manage and tune incidents 2. Configure notification policies 3. Configure remediation options

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Fortinet NSE 6 - FortiSIEM 7.4 Analyst Sample Questions (Q87-Q92):

NEW QUESTION # 87
Refer to the exhibit.
The configuration for a machine learning (ML) dataset using anomaly detection is shown.

If data for this model is generated every hour, how long must the FortiSIEM device be up before it can produce a valid training set?

A. 3 hours
B. 10 hours
C. 24 hours
D. 30 hours

Answer: B

Explanation:
The Windows parameter is set to 10, meaning FortiSIEM requires 10 data windows to build a valid training baseline. Since data is generated every hour, the device must collect 10 hours of data before producing a valid training set.

NEW QUESTION # 88
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 Train factor must be 70% or greater.
B. Only one AVG type field must be selected under Fields to use for Prediction.
C. Run Mode must be set to ML.
D. The selection in Fields to use for Prediction and Field to Predict must match.

Answer: A

Explanation:
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.

NEW QUESTION # 89
Refer to the exhibit. Why would the two entries shown in the exhibit be included in an incident action history?

A. Multiple new incidents have occurred and have triggered the rule threshold.
B. The system cleared the incident and is configured to Send Email/SMS/Webhook to the target users.
C. An administrator has enabled the Notify on Incident Cleared option.
D. An analyst resolved and cleared the incident and the Do not notify when an incident is cleared by system option is not enabled in the automation policy.

Answer: B

Explanation:
The action history shows that the system cleared the incident and then sent an email notification.
This occurs when the automation policy is configured to send an Email/SMS/Webhook notification to the target users when the incident is cleared by the system.

NEW QUESTION # 90
What is one difference between a lookup table and a watchlist?

A. A lookup table can have multiple columns, and a watchlist only has one.
B. A watchlist can be updated through an API, and a lookup table cannot.
C. Watch list entries do not expire, lookup table entries have a defined lifetime.
D. A lookup table requires a parser to be able to query and interpret the data, a watchlist does not require a parser.

Answer: A

Explanation:
A lookup table supports multiple columns and structured datasets, allowing more complex mappings and queries. A watchlist contains only a single column of values used for direct matching operations.

NEW QUESTION # 91
A critical server is sending traffic that is triggering a high severity outbound intrusion prevention system (IPS) permitted IPS exploit rule. This traffic must be allowed. Which two items must you configure to prevent this sever from triggering the incident? (Choose two.)

A. Create a rule exception for the IP address.
B. Change the time window on the rule conditions.
C. Modify the group by parameters to exclude the IP address.
D. Modify the subpattern filter to exclude the IP address.
E. Modify the aggregate count in the subpattern.

Answer: A,D

Explanation:
To stop a known allowed server from generating this incident, you can tune the rule logic by excluding the server IP address in the subpattern filter. You can also create a rule exception for that IP address, which suppresses incident generation for matching traffic from the approved server while leaving the rule active for other sources.

NEW QUESTION # 92
......

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