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| Section | Objectives |
|---|---|
| Topic 1: Rules and Subpatterns | - Analytics rules configuration
|
| Topic 2: FortiEDR Security Settings and Policies | - Security configuration
|
| Topic 3: Machine Learning, UEBA, and ZTNA | - Advanced analytics integration
|
| Topic 4: Incidents, Notifications, and Remediation | - Incident management
|
| Topic 5: Analytics | - Query and event analysis
|
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NEW QUESTION # 24
Which two data areas can you use for user and entity behavior analytics (EBA) machine learning models?
(Choose two.)
Answer: A,D
Explanation:
The correct answers are A. Process and C. Resources. FortiSIEM UEBA/EBA analytics uses endpoint and behavior-related telemetry to identify abnormal activity. The Study Guide explains that the UEBA incident dashboard shows incidents that the AI module creates based on alerts received from FortiInsight. The UEBA attribute list includes the incident name, host, application, user, tag, and activity. This aligns with process-oriented activity monitoring from endpoint agents. The performance and baseline lessons also explain that FortiSIEM collects performance and availability data from devices and applications, including resource-utilization metrics, and uses that information to build baselines and detect anomalous activity. Resource behavior includes CPU, memory, disk, I/O, and similar utilization patterns, which are valid behavioral-model inputs. Location and Network are important FortiSIEM context areas, but in this question's EBA machine learning model choices, the valid model areas are process behavior and resource behavior. Process reflects what is running or being executed; Resources reflects system or application utilization behavior. Together, these are the two data areas used for EBA machine learning models.
NEW QUESTION # 25
Refer to the exhibit.
An analyst wants to perform a KMeans machine learning (ML) job on this data.
How many N clusters would be a good fit for the data?
Answer: A
Explanation:
The scatter plot shows two visually distinct groupings of data points, making two clusters an appropriate fit for a KMeans ML job.
NEW QUESTION # 26
You want to build a query that includes only events that contain a username.
What is a valid analytics query that will perform this type of search?
Answer: A
Explanation:
A query using IS NOT NULL returns only events where the User attribute has a value. This is the valid way to filter analytics results to events that contain a username.
NEW QUESTION # 27
Refer to the exhibit.
Which statement about the time range settings defined in the nested query is accurate?
Answer: D
Explanation:
The main analytics search uses the last 10 minutes as the event time range. The nested query uses the Approved Devices report generated from the last 30 days, so FortiSIEM compares recent source IP addresses against that 30-day approved-device result set.
NEW QUESTION # 28
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 # 29
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