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Fortinet NSE6_FSM_AN-7.4 Exam Syllabus Topics:

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

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Latest Fortinet NSE 6 - FortiSIEM 7.4 Analyst exam dumps & NSE6_FSM_AN-7.4 braindumps2go vce

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Fortinet NSE 6 - FortiSIEM 7.4 Analyst Sample Questions (Q27-Q32):

NEW QUESTION # 27
Refer to the exhibit. Why are some of the fields highlighted in red?

Answer: C

Explanation:
The highlighted fields represent attributes that contain multiple unique values and therefore cannot be used together in a grouped aggregation display in the current configuration.


NEW QUESTION # 28
Rules on FortiSIEM are usually processed as events are collected (streaming). How can you create a rule to evaluate events over an 8-hour period?

Answer: A

Explanation:
Setting the Evaluation Mode to Scheduled allows FortiSIEM to evaluate accumulated events over a defined historical period, such as 8 hours, instead of processing events only in real time through streaming evaluation.


NEW QUESTION # 29
Refer to the exhibit. Which two things that happen when this automation policy triggers? (Choose two.)

Answer: A,B

Explanation:
The automation policy has Send Email/SMS/Webhook to the target users enabled, so an email notification is sent. It also has Run Remediation/Script enabled, so the configured remediation script is executed when the policy triggers.


NEW QUESTION # 30
Refer to the exhibit.

What is the Group: VPN Gateway value a reference to? (Choose one answer)

Answer: D

Explanation:
The correct answer is A. A configuration management database (CMDB) device group . In the exhibit, the analytics filter uses Source IP IN Group: VPN Gateway . In FortiSIEM analytics, values shown as Group:
for IP/device-related attributes commonly reference FortiSIEM CMDB groups, not firewall address groups or rule folders. The FortiSIEM 7.4 User Guide explains how CMDB groups are inserted into queries: to add a CMDB group, the user selects an attribute, selects an operator such as IN , and then selects a value from CMDB. The guide gives a direct example where a reporting IP is matched using a firewall device group, expressed as a condition equivalent to "reptDevIpAddr IN Firewall group." This matches the exhibit's structure: Source IP is the event attribute, IN is the operator, and Group:
VPN Gateway is the selected CMDB group value. A FortiSIEM watchlist is different; the Study Guide describes watchlists as containers of similar items that can be referenced in searches, rules, and reports, but they are managed under Resources > Watch Lists, not shown here as a CMDB-style device group value. A FortiGate address group exists on FortiGate, not as this FortiSIEM analytics CMDB group reference.


NEW QUESTION # 31
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 # 32
......

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