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| Section | Objectives |
|---|---|
| Topic 1: Machine Learning, UEBA, and ZTNA | - Advanced analytics integration
|
| Topic 2: Incidents, Notifications, and Remediation | - Incident management
|
| Topic 3: FortiEDR Security Settings and Policies | - Security configuration
|
| Topic 4: Analytics | - Query and event analysis
|
| Topic 5: Rules and Subpatterns | - Analytics rules configuration
|
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NEW QUESTION # 40
Refer to the exhibits. Which information will this analytics search display?
Answer: C
Explanation:
The search filters for Event Type values in the Logon Failure group and matches Destination IP addresses against the Device IP values returned from the Server Inventory CMDB report. This displays failed login events involving servers contained in the Server Inventory CMDB report.
NEW QUESTION # 41
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: C
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 # 42
Refer to the exhibit.
What will happen when a device being analyzed by the machine learning configuration shown in the exhibit has consistently high memory utilization?
Answer: A
Explanation:
The exhibit shows a machine learning Regression configuration. In FortiSIEM regression, selected numerical fields are used as predictors, and another field is selected as the value to predict. The FortiSIEM 7.4 User Guide's Machine Learning section lists Regression separately and describes the training workflow, including selecting fields used by the algorithm. The exhibit shows Memory Utilization, Sent Bytes, and Received Bytes selected under Fields to use for Prediction, while CPU Utilization is selected under Field to Predict. This means Memory Utilization is an input variable used by the model. If memory utilization is consistently high during training or retraining, that higher value becomes part of the learned model pattern. FortiSIEM does not automatically trigger a high-memory incident merely because a regression input value is high; an incident would require inference behavior and an anomaly action or rule configuration. Option C is also wrong because FortiSIEM does not dynamically lower a CPU trigger threshold just because memory is high. The correct behavior is model update based on the higher observed input value.
NEW QUESTION # 43
You want to build an event query that displays only events to higher number destination ports (1024-65535). Which analytic search string is valid for this scenario?
Answer: B
Explanation:
FortiSIEM analytic searches support explicit comparison operators. Using greater-than-or-equal- to and less-than-or-equal-to conditions correctly defines the valid destination port range from
1024 through 65535.
NEW QUESTION # 44
Refer to the exhibit.
Which section contains the subpattern configuration that determines how many matching events are needed to trigger the rule?
Answer: A
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 # 45
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