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| Section | Objectives |
|---|---|
| Topic 1: Analytics | - Query and event analysis
|
| Topic 2: Rules and Subpatterns | - Analytics rules configuration
|
| Topic 3: FortiEDR Security Settings and Policies | - Security configuration
|
| Topic 4: Incidents, Notifications, and Remediation | - Incident management
|
| Topic 5: Machine Learning, UEBA, and ZTNA | - Advanced analytics integration
|
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NEW QUESTION # 55
Which two ways can an automation service playbook can be triggered? (Choose two.)
Answer: A,D
Explanation:
FortiSIEM playbooks can be triggered manually from the incident details menu or automatically through automation policies tied to rule-generated incidents.
NEW QUESTION # 56
Refer to the exhibits.
You are troubleshooting why the rule shown in the exhibit is generating incidents for successful Remote Desktop Protocol (RDP) connections with correct logins. It should only be triggering when a person fails to log in three or more times to the target device when connecting with RDP.
What is causing the rule to be triggered by correct login events? (Choose one answer)
Answer: D
Explanation:
The rule is triggering on successful RDP connection events because the Next operator between the two subpatterns is set to OR . The FortiSIEM Study Guide explains that multiple subpattern rules are used when patterns must occur within a specific time period or when one of several patterns proves that an incident condition exists. It lists the OR operator as: "Subpattern X OR Subpattern Y occurred within the Time Window." The same Study Guide further explains that if multiple patterns are used, FortiSIEM requires a next operator, and in the OR example, "an event that matches either" subpattern will trigger. It also states that because the next operator is OR, the constraint between the two subpatterns is not enforced.
In the exhibit, Subpattern 1 matches RDP traffic on TCP/UDP port 3389 from FortiGate traffic- forward events, while Subpattern 2 matches logon failure events with COUNT(Matched Events) > = 3.
Because the rule uses OR, FortiSIEM can trigger when only the RDP connection subpattern matches, even if the failed-logon subpattern does not match. The correct logic should require both subpatterns to match with the intended relationship constraints, not either subpattern independently.
NEW QUESTION # 57
Refer to the exhibit. If you group the events by Reporting IP, Event Type, and User attributes, how many results will FortiSIEM display?
Answer: C
Explanation:
When grouped by Reporting IP, Event Type, and User, FortiSIEM consolidates rows sharing the same values for these attributes.
Reporting IP: all are 10.1.1.1
Event Type: all are Logon
Users: Mike, Bob, and Alice
Thus, FortiSIEM will display three results, one for each user.
NEW QUESTION # 58
Refer to the exhibit. You want to use a machine learning (ML) model to train data with the following characteristics shown in the exhibit. Which ML model is the best fit to match the data in the exhibit?
Answer: A
Explanation:
The data points form several distinct groups, so a clustering model is the best fit for identifying natural groupings within the dataset.
NEW QUESTION # 59
When configuring anomaly detection machine learning, in which step must you select the fields to analyze?
Answer: A
Explanation:
The correct answer is A. Design. In the FortiSIEM 7.4 User Guide's anomaly detection workflow, the first configuration step is Step 1: Design. In that step, FortiSIEM requires the analyst to identify the fields that will be analyzed by the machine learning job. The guide states that under Design, the analyst must identify Fields to Analyze, and explains that these fields are considered for anomaly detection and must currently be numerical fields. The guide also identifies the time field requirement for statistical deviation algorithms and notes that a FortiSIEM report is used to provide the dataset. The Prepare Data step comes later and is used to load or prepare the data source for training. The Train step runs the algorithm against the prepared dataset. The Schedule step is used after training to run inference.
Therefore, the selection of fields to analyze belongs to the Design phase, not Prepare Data, Train, or Schedule. This sequence matters because FortiSIEM must know which numerical fields are relevant before it can prepare, train, or run inference on the machine learning job.
NEW QUESTION # 60
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