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| Section | Objectives |
|---|---|
| Topic 1: Machine Learning, UEBA, and ZTNA | - Advanced analytics integration
|
| Topic 2: FortiEDR Security Settings and Policies | - Security configuration
|
| Topic 3: Incidents, Notifications, and Remediation | - Incident management
|
| Topic 4: Rules and Subpatterns | - Analytics rules configuration
|
| Topic 5: Analytics | - Query and event analysis
|
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NEW QUESTION # 14
What can you use to send data to FortiSIEM for user and entity behavior analytics (UEBA)?
Answer: B
Explanation:
The correct answer is A. FortiSIEM agent. The FortiSIEM Study Guide identifies FortiSIEM agents as the component responsible for "file, log monitoring, and UEBA." It also explains that FortiSIEM agents can be installed on endpoints or servers to provide data collection functions that native syslog may not provide. For Windows systems specifically, the guide states that Windows servers do not natively send syslog messages and that a FortiSIEM Windows agent can be installed to perform that function. The FortiSIEM 7.4 User Guide also confirms that FortiInsight UEBA functionality runs as an integrated module within the FortiSIEM Windows Agent in newer releases. SSH and SNMP are access or monitoring protocols; they can support discovery or performance monitoring, but they are not the UEBA data-sending component. A FortiSIEM worker performs analysis and search functions inside the FortiSIEM architecture; it is not installed on endpoints to collect UEBA telemetry. Therefore, the FortiSIEM agent is the correct mechanism for sending UEBA-relevant endpoint data to FortiSIEM.
NEW QUESTION # 15
Refer to the exhibits.
Three events are collected over 10 minutes from two servers: Server A and Server B.
Based on the settings for the rule subpattern and a 10-minute condition window, how many incidents will the servers generate?
Answer: A
Explanation:
The correct answer is D because Server A satisfies the rule's threshold and count requirements, while Server B does not. The Study Guide explains that a FortiSIEM subpattern consists of Filter , Aggregate , and Group By components. It also states that the Aggregate function defines how many or what metric values must match during the time window, while Group By controls how the matching events are grouped into rows. The performance metrics lesson explains that FortiSIEM collects performance and availability data, converts polling results into logs, and uses those metrics for performance, availability, resource utilization, and baselining. In the exhibit, the aggregate evaluates CPU utilization against the device's CPU critical threshold and also requires a matched-event count of at least two within the 10-minute window. Server A has CPU values above its critical threshold enough times in the window, so it generates one incident. Server B has a lower configured threshold but its collected CPU values do not satisfy the required aggregate condition for the grouped server. Therefore, only Server A generates an incident.
NEW QUESTION # 16
Refer to the exhibit. Why are some of the fields highlighted in red?
Answer: D
Explanation:
The highlighted fields represent attributes that contain multiple unique values and therefore cannot be used together in a grouped aggregation display in the current configuration.
NEW QUESTION # 17
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 # 18
When configuring machine learning (ML), in which step can you modify how the model fits the training data set?
Answer: A
Explanation:
The correct answer is B. Train. In FortiSIEM machine learning, the Train step is where the model is built from the prepared dataset and where model-fitting behavior can be adjusted. The FortiSIEM 7.4 User Guide explains that after preparing data, the analyst goes to Analytics > Machine Learning > Train, selects the machine learning task, chooses the algorithm, selects the prediction/target fields when required, and chooses the Train factor, which determines how much data is used for training versus testing. The guide states that the Train factor should be greater than 70%, meaning 70% of the data is used for training and 30% for testing. It also explains that model quality metrics show how accurately the algorithm predicts the field. For regression, lower MAE means a better fit, and R2 shows how well predictions approximate real data points. Most importantly, the guide states: "If you want to change the algorithm parameters and re-train, then click Tune & Train, change the parameters and click Save
& Train." This confirms that modifying how the model fits the training dataset is done in the Train step, not Prepare Data, Statistics, or Design.
NEW QUESTION # 19
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