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Fortinet NSE6_FSM_AN-7.4 Exam Syllabus Topics:

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

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Fortinet NSE 6 - FortiSIEM 7.4 Analyst Sample Questions (Q43-Q48):

NEW QUESTION # 43
Refer to the exhibit.

An analyst is troubleshooting the rule shown in the exhibit. It is not generating any incidents, but the filter parameters are generating events on the Analytics tab.
What is wrong with the rule conditions?

Answer: D

Explanation:
The Group By attributes - Destination IP and User - cause the aggregation (COUNT(Source IP) >= 2) to apply within each unique combination of those groupings. This restricts the count calculation and can prevent the rule from triggering incidents, even if matching events exist in the Analytics tab.


NEW QUESTION # 44
Refer to the exhibit.

An analyst wants to perform a KMeans machine learning (ML) job on this data.
How many N clusters would be a good fit for the data?

Answer: C

Explanation:
The scatter plot shows two visually distinct groupings of data points, making two clusters an appropriate fit for a KMeans ML job.


NEW QUESTION # 45
Refer to the exhibit.

You are investigating an issue with two destination IP addresses, but you are not getting any results from the search.
Based on the filters shown in the exhibit, why is this search returning no results?

Answer: C

Explanation:
The search is using an AND condition between two different values for the same Destination IP attribute. A single event cannot have both destination IP addresses at the same time, so the filter returns no results. The condition should use OR to search for events matching either destination IP address.


NEW QUESTION # 46
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?

Answer: B


NEW QUESTION # 47
Refer to the exhibit.


You are attempting to tune an anomaly detection machine learning (ML) job. The chart shows there are no anomalies detected, but you are not satisfied with the fit of the ML model.
Which adjustment must you make to train the model and ensure a better fit?

Answer: D

Explanation:
Increasing the number of windows gives the ML job more historical data windows to use during training. This improves the model's baseline calculation and helps produce a better statistical fit for anomaly detection.


NEW QUESTION # 48
......

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