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| Section | Objectives |
|---|---|
| FortiEDR and Security Policy Integration | - FortiEDR Security Configuration
|
| Advanced Analytics and Integrations | - ML, UEBA, and ZTNA
|
| Analytics and Search | - Query and Event Analysis
|
| Rules and Incident Management | - Incidents and Notifications
|
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NEW QUESTION # 41
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?
Answer: A
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 # 42
You want to create a rule with multiple subpatterns but trigger an incident only if three different subpatterns are matched over a 24-hour period.
Where must you define the time period that the rule uses to evaluate all the subpatterns? (Choose one answer)
Answer: A
Explanation:
The time period must be defined under the Define Condition tab of the rule. The FortiSIEM Study Guide states that a rule has three components: General , Condition , and Action . The General section defines the overall purpose and scope of the rule, the Condition section specifies which conditions trigger the rule, and the Action section configures what happens after the rule is triggered. The Study Guide further explains that on the Define Condition page, FortiSIEM specifies "the conditions-event attributes and thresholds-that will trigger the rule and create an incident," including "the time window" within which the subpatterns must match for the rule condition to be satisfied. It also states that when there is more than one subpattern, you must define the logic between subpatterns and their relationships or constraints.
For a 24-hour multiple-subpattern rule, the guide gives an example using an 86400-second time window, which equals 24 hours, and shows it under Resource > Rules > Define Condition. Therefore, the time period is not configured inside each individual subpattern, not under General, and not under Define Action.
NEW QUESTION # 43
Refer to the exhibit.
If you group the events by Reporting Device , Reporting IP , and Application Category , how many results will FortiSIEM display?
Answer: E
Explanation:
FortiSIEM grouping works by combining events that have the same values for all selected Group By attributes. The Study Guide's rule and subpattern example states that when multiple events have the same Group By values, "they are grouped together in one row, and the count column tracks the number of events for each of those rows." In the exhibit, the selected grouping fields are Reporting Device, Reporting IP, and Application Category. The table contains six raw rows, but two rows share the same grouped combination:
FW01 / 10.1.1.1 / DB . Those two rows are collapsed into one grouped result. The other combinations are unique: FW02 / 10.1.1.2 / WebApp, FW01 / 10.1.1.1 / SSH, FW03 / 10.1.1.3 / DB, and FW04 / 10.1.1.4 / SSH. That creates five grouped rows in total. FortiSIEM does not display six because grouping removes duplicate combinations, and it does not display four because only one duplicate combination exists.
NEW QUESTION # 44
Refer to the exhibit. What is the Group: VPN Gateway value referring to?
Answer: B
Explanation:
The value Group: VPN Gateway refers to a CMDB device group in FortiSIEM. This group represents a collection of devices categorized as VPN Gateways in the Configuration Management Database. By filtering with this group, the query retrieves events where the Source IP matches any device included in the CMDB group "VPN Gateway."
NEW QUESTION # 45
Which two data areas can you use for user and entity behavior analytics (UEBA) machine learning models? (Choose two.)
Answer: A,D
Explanation:
FortiSIEM's UEBA models analyze user and entity behavior by correlating data such as location (for detecting unusual logins or access patterns) and network activity (for identifying abnormal communication or traffic behaviors). These data areas enable the system to build baseline profiles and detect anomalies indicating potential insider threats or compromised accounts.
NEW QUESTION # 46
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