Fortinet NSE6_FSM_AN-7.4 Latest Test Simulator - NSE6_FSM_AN-7.4 Valid Exam Book

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Fortinet NSE6_FSM_AN-7.4 Exam Syllabus Topics:

SectionWeightObjectives
Analytics30%- Applying group by and data aggregation
- Building queries from search results and events
- Performing CMDB and lookup table queries
Event Correlation and Rule Management20%- Managing alerts, tuning rules, reducing false positives
- Creating and configuring correlation rules
Event Collection and Normalization20%- Collecting logs and data from multiple sources
- Normalizing, parsing, and standardizing event data
Monitoring, Reporting and Integration15%- Generating compliance and operational reports
- Integrating with security tools and ZTNA
- Configuring dashboards and real-time monitoring
Incident Detection, Investigation and Response15%- Applying incident response workflows and escalation
- Using dashboards and tools for incident investigation

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Fortinet NSE 6 - FortiSIEM 7.4 Analyst Sample Questions (Q14-Q19):

NEW QUESTION # 14
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?

Answer: A

Explanation:
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.


NEW QUESTION # 15
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?

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 # 16
When configuring machine learning (ML), in which step can you modify how the model fits the training data set?

Answer: D

Explanation:
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.


NEW QUESTION # 17
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?

Answer: C

Explanation:
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.


NEW QUESTION # 18
Which statement about thresholds is true?

Answer: C

Explanation:
FortiSIEM supports both global thresholds and per-device/per-device-object thresholds for some performance events. The Study Guide states that FortiSIEM can define "per-device-object thresholds or global thresholds" for performance events, and separately explains that global thresholds are referenced by the rules engine by default. Therefore, the correct statement is that FortiSIEM uses global and per-device thresholds for performance metrics. Options A, B, and D are too restrictive or false because FortiSIEM does not use only one fixed threshold model.


NEW QUESTION # 19
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