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Fortinet NSE6_FSM_AN-7.4 Exam Syllabus Topics:

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

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Fortinet NSE 6 - FortiSIEM 7.4 Analyst Sample Questions (Q62-Q67):

NEW QUESTION # 62
In an automation policy, which two methods can you use to notify analysts when an incident is triggered? (Choose two.)

A. Pop-up window
B. Email
C. Syslog
D. FortiSIEM Case

Answer: B,D

NEW QUESTION # 63
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?

A. Add them to the global exclusion list.
B. Add the devices to a rule exclusion automation policy.
C. Add them to a device group that is being filtered by the rules.
D. Add their associated discovery credentials.

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 # 64
You need a model for predicting a target field based on other fields in a dataset and then trigger an anomaly if the value does not match the prediction. Which machine learning algorithm will build this type of model?

A. Clustering
B. Regression
C. Regression
D. Forecasting

Answer: C

Explanation:
A Regression algorithm is used when predicting a continuous or numeric target field based on other features in the dataset. In FortiSIEM, regression-based machine learning models establish expected values, and an anomaly is triggered when the actual observed value significantly deviates from the regression prediction.

NEW QUESTION # 65
Refer to the exhibit.

Which statement about the time range settings defined in the nested query is accurate?

A. FortiSIEM will search in real time using 10 minute blocks for a source IP address that is not in the Approved Devices report from the last 30 days.
B. FortiSIEM will list source IP addresses found the last 10 minutes of events from each day in the Approved Devices report from the last 30 days.
C. FortiSIEM will search the last 10 minutes of events for a source IP address that is not in the Approved Devices report from the last 30 days.
D. FortiSIEM will search the last 30 days of events for a source IP address that is not in the Approved Devices report.

Answer: C

Explanation:
The main analytics search uses the last 10 minutes as the event time range. The nested query uses the Approved Devices report generated from the last 30 days, so FortiSIEM compares recent source IP addresses against that 30-day approved-device result set.

NEW QUESTION # 66
What must match when referencing an inner query from an outer query?

A. Both must be CMDB lookups.
B. Both must reference IP addresses.
C. Both must be event queries.
D. Both must have the same data type.

Answer: D

Explanation:
When creating an inner query in FortiSIEM, the referenced attribute in the outer and inner queries must share the same data type (for example, IP address, string, or integer). This ensures the system can properly correlate and filter results between the two queries during execution.

NEW QUESTION # 67
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