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Fortinet NSE6_FSM_AN-7.4 Exam Syllabus Topics:

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

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NSE6_FSM_AN-7.4 Latest Study Materials - Exam Sample NSE6_FSM_AN-7.4 Questions

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Fortinet NSE 6 - FortiSIEM 7.4 Analyst Sample Questions (Q42-Q47):

NEW QUESTION # 42
What are the four incident status values on FortiSIEM?

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 # 43
You need to model for predicting a target based on other fields in the dataset and then trigger an anomaly if the value does not match the prediction.
Which machine learning algorithm will build this type of model?

Answer: A

Explanation:
The correct answer is C. Regression. Regression is the machine learning task used when a model predicts a target value based on other fields in a dataset. In FortiSIEM's machine learning workflow, regression configuration includes selecting Fields to use for Prediction and a Field to Predict. The User Guide explains that during regression training, the analyst chooses the fields used for prediction and the field being predicted, then trains the model with a Train factor. This matches the question exactly:
the model predicts a target based on other fields, and the inference phase can identify anomalies when observed values differ significantly from predicted values. Classification is used to assign records to discrete classes or categories, not to predict a continuous target metric from other variables. Clustering groups similar observations without a predefined target field. Forecasting predicts future values over time, normally based on time-series behavior. Because the question specifically describes predicting a target field from other dataset fields and detecting mismatch from the prediction, the correct machine learning method is Regression.


NEW QUESTION # 44
Refer to the exhibit.

What happens when an analyst clears an incident generated by a rule containing the automation policy shown in the exhibit?

Answer: B

Explanation:
The correct answer is B because the automation policy shown has the email/SMS/webhook notification action enabled, and the setting that suppresses notification for manual incident clearing is not selected. The FortiSIEM Study Guide explains that automation policies define actions taken when incident-related policy criteria match. It states that notification policies are defined by criteria such as severity, associated rules, time range, affected items, and actions. The guide also states that FortiSIEM can send email notifications and SMS messages to individuals or groups as part of an automation policy. In the exhibit, the options Do not notify when an incident is cleared automatically and Do not notify when an incident is cleared by system are selected, but Do not notify when an incident is cleared manually is not selected. Because the analyst clears the incident manually, the suppression condition does not apply. Therefore, FortiSIEM sends the configured email notification to the target user, identified in the question as the SOC manager.


NEW QUESTION # 45
Refer to the exhibit.

Which value would you expect the FortiSIEM parser to use to populate the Application Name field?

Answer: C

Explanation:
The correct answer is C. SSL . FortiSIEM receives raw logs, processes them through parsers, normalizes the extracted fields, classifies the event, and stores the structured data. The Study Guide explains the FortiSIEM process flow: data is collected, processed by the parsing engine, normalized, classified, and then stored. It further states that normalization extracts individual fields from raw events and maps those fields to a common schema. The FortiSIEM 7.4 User Guide describes a parser as a file containing instructions for the parser module to convert a raw log into event attributes. In the exhibit, the raw FortiGate log includes values such as profiletype= " applist " , appcat= " Network.Service " , and app= " SSL " . The field that directly represents the application value is app= " SSL " . Therefore, the parser would use SSL to populate the normalized Application Name field. applist describes the profile type, Network.Service is the application category, and wan1 is the interface, not the application name.


NEW QUESTION # 46
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: A

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 # 47
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