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Fortinet NSE6_FSM_AN-7.4 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Monitoring, Reporting and Integration	15%	- Integrating with security tools and ZTNA - Configuring dashboards and real-time monitoring - Generating compliance and operational reports
Topic 2: Incident Detection, Investigation and Response	15%	- Applying incident response workflows and escalation - Using dashboards and tools for incident investigation
Topic 3: Event Collection and Normalization	20%	- Normalizing, parsing, and standardizing event data - Collecting logs and data from multiple sources
Topic 4: Analytics	30%	- Performing CMDB and lookup table queries - Building queries from search results and events - Applying group by and data aggregation
Topic 5: Event Correlation and Rule Management	20%	- Creating and configuring correlation rules - Managing alerts, tuning rules, reducing false positives

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Fortinet NSE 6 - FortiSIEM 7.4 Analyst Sample Questions (Q66-Q71):

NEW QUESTION # 66
What are the four incident status values on FortiSIEM?

A. Active, auto closed, cleared manually, resolved
B. Active, closed, cleared, resolved
C. Active, auto cleared, cleared manually, system cleared
D. Active, cleared, cleared manually, false positive

Answer: C

Explanation:
FortiSIEM incidents can have four status values: Active, Auto Cleared, Cleared Manually, and System Cleared. These statuses track the lifecycle of an incident-from detection (Active) to resolution - whether it's cleared automatically by correlation logic or manually by an analyst.

NEW QUESTION # 67
Which three types of data can you use to train FortiSIEM machine learning (ML)? (Choose three.)

A. FortiSIEM analytical reports
B. FortiSIEM ML job
C. SQL database
D. Configuration management database (CMDB)
E. CSV files

Answer: A,C,E

Explanation:
FortiSIEM machine learning models can be trained using structured data from CSV files, FortiSIEM analytical reports, and SQL database sources. These sources provide the historical datasets needed to prepare, train, and evaluate the ML model.

NEW QUESTION # 68
Refer to the exhibit.

If a rule containing the automation policy shown in the exhibit triggers, what will happen?

A. Associated source IP addresses will be blocked on devices in the Aviation organization.
B. Associated source IP addresses will be blocked on devices in the Network CMDB group.
C. Associated source IP addresses will be blocked on all FortiGate firewalls.
D. Associated source IP addresses will be blocked on two FortiGate firewalls.

Answer: D

Explanation:
The automation policy is configured to run a remediation script named "Fortinet FortiOS - Block Source IP FortiOS via API". It specifies enforcement on two FortiGate devices: FortiGate508 and FortiGate90D. Therefore, associated source IP addresses will be blocked on those two FortiGate firewalls only.

NEW QUESTION # 69
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. Anomaly detection
C. Classification
D. Regression

Answer: D

Explanation:
Regression models predict the value of a target field using other fields as predictors. FortiSIEM can then generate anomalies when the observed value significantly deviates from the predicted value.

NEW QUESTION # 70
What can you use to send data to FortiSIEM for user and entity behavior analytics (UEBA)?

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

Answer: A

NEW QUESTION # 71
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