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

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Latest NSE6_FSM_AN-7.4 Exam Notes & NSE6_FSM_AN-7.4 Most Reliable Questions

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Fortinet NSE 6 - FortiSIEM 7.4 Analyst Sample Questions (Q45-Q50):

NEW QUESTION # 45
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 # 46
When selecting multiple rules at once on FortiSIEM, which actions can you perform?

Answer: B

Explanation:
FortiSIEM allows bulk management of rules, including changing severity levels and activating or deactivating multiple rules simultaneously to simplify administration and policy management.


NEW QUESTION # 47
Refer to the exhibit.

Which section contains the subpattern configuration that determines how many matching events are needed to trigger the rule?

Answer: B

Explanation:
The Aggregate section contains the condition COUNT(Matched Events) > = 1, which defines how many events must match the filter criteria for the rule to trigger. This is the subpattern configuration that determines the event threshold.
The correct answer is A. Aggregate . In FortiSIEM rule subpatterns, the Filter section defines which events are eligible for matching, but the Aggregate section defines the statistical or threshold condition that must be satisfied before the subpattern is considered matched. The Study Guide explains that rule conditions are built from subpatterns of event attribute filters and aggregation functions. It also states that a single-subpattern rule is formed by three fields: filters, aggregate, and group by. In the exhibit, the aggregate line is COUNT (Matched Events) > = 1. That expression directly specifies the number of matching events required to satisfy the subpattern. Group By only controls how matching events are partitioned into separate evaluation groups.
Actions define what happens after a rule triggers, such as incident generation or notification. Filters define the event type or attribute criteria, but they do not define the required count threshold. Therefore, the section that determines how many matching events are needed is the Aggregate section.


NEW QUESTION # 48
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: D

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 # 49
What are two required components of a rule? (Choose two.)

Answer: A,C

Explanation:
A FortiSIEM rule requires detection logic and at least one subpattern structure to evaluate matching events. The FortiSIEM Study Guide explains that a single subpattern rule example consists of filter, aggregate, and group-by sections. It states: "The subpattern, ExcessVPNLoginFailure, consists of three components: Filter, Aggregate, Group By." That confirms that a subpattern is a core rule component.
The FortiSIEM 7.4 User Guide also identifies Detection Technology as part of rule definition and built- in rule metadata. Detection Technology describes the detection method used by the rule, such as correlation, profiling, machine learning, or correlation using lookup tables. Exception policy is not required; it is used to suppress or tune matches under specific conditions. Clear policy is also not required; it controls how an incident can be cleared or auto-cleared after triggering. Therefore, the required components among the listed options are Subpattern and Detection Technology. Without them, the rule lacks both the event-detection structure and the detection-method classification needed for rule evaluation.


NEW QUESTION # 50
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