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| Section | Objectives |
|---|---|
| Topic 1: FortiEDR Security Settings and Policies | - Security configuration
|
| Topic 2: Machine Learning, UEBA, and ZTNA | - Advanced analytics integration
|
| Topic 3: Analytics | - Query and event analysis
|
| Topic 4: Incidents, Notifications, and Remediation | - Incident management
|
| Topic 5: Rules and Subpatterns | - Analytics rules configuration
|
>> NSE6_FSM_AN-7.4 Formal Test <<
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NEW QUESTION # 55
When configuring machine learning (ML), in which step can you modify how the model fits the training data set?
Answer: B
NEW QUESTION # 56
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 # 57
Refer to the exhibit.
The analyst is troubleshooting the analytics query shown in the exhibit.
Why is this search not producing any results?
Answer: D
Explanation:
The search fails because nested analytics queries require the outer query attribute type to match the inner query display-column data type. The FortiSIEM Study Guide explicitly explains this rule in the Nested Query section: "Another important aspect to understand is that the data value types must match." It further explains that each inner query display column has a data value type, such as IP, string, or integer, and the outer query attribute must match that type. The FortiSIEM 7.4 User Guide states the same operational requirement: for a nested query to work correctly, "the data type of the filter attribute in the outer query must match up with the data type of one certain display column in the inner query." Therefore, if the inner query returns a string attribute but the outer query compares it against an IP-type attribute, FortiSIEM cannot produce a valid match.
The issue is not the time range, not the use of User and Event Type together, and not the Boolean operator. It is an attribute type mismatch between the query and subquery.
NEW QUESTION # 58
Refer to the exhibit. Why would the two entries shown in the exhibit be included in an incident action history?
Answer: A
Explanation:
The action history shows that the system cleared the incident and then sent an email notification.
This occurs when the automation policy is configured to send an Email/SMS/Webhook notification to the target users when the incident is cleared by the system.
NEW QUESTION # 59
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: C
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 # 60
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