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  1. Home
  2. Fortinet Certification
  3. FCP_FSM_AN-7.2 Exam
  4. Fortinet.FCP_FSM_AN-7.2.v2026-06-02.q31 Dumps
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Question 1

Refer to the exhibit.

According to the automation policy configuration shown in the exhibit, what happens if an associated rule triggers?

Correct Answer: B
When an associated rule triggers, FortiSIEM performs all selected actions in the automation policy. In this case, it will send an email/SMS/webhook, run the remediation script, invoke the integration policy (even if none is currently defined), and create a case. All checked actions are executed.
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Question 2

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?

Correct Answer: A
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Question 3

Refer to the exhibit.

As shown in the exhibit, why are some of the fields highlighted in red?

Correct Answer: A
The fields are highlighted in red because unique values such as Event Receive Time and Raw Event Log cannot be used in group-by operations. Grouping requires aggregatable or consistent values across events, while these fields are unique to each event, making them incompatible for grouping.
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Question 4

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

Correct Answer: B,C
A Subpattern defines the specific conditions or event patterns the rule is designed to detect, and the Detection Technology specifies the type of detection logic (e.g., real-time, historical). Both are essential for a rule to function in FortiSIEM.
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Question 5

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?

Correct Answer: A
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.
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