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| Section | Weight | Objectives |
|---|---|---|
| Monitoring, Reporting and Integration | 15% | - Integrating with security tools and ZTNA - Configuring dashboards and real-time monitoring - Generating compliance and operational reports |
| Event Collection and Normalization | 20% | - Normalizing, parsing, and standardizing event data - Collecting logs and data from multiple sources |
| Event Correlation and Rule Management | 20% | - Creating and configuring correlation rules - Managing alerts, tuning rules, reducing false positives |
| Incident Detection, Investigation and Response | 15% | - Using dashboards and tools for incident investigation - Applying incident response workflows and escalation |
| Analytics | 30% | - Applying group by and data aggregation - Performing CMDB and lookup table queries - Building queries from search results and events |
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質問 # 40
Which information can FortiSIEM retrieve from FortiClient EMS through an API connection?
正解：D
解説：
The correct answer is D. ZTNA tags. FortiSIEM 7.4 includes FortiEMS endpoint tagging integration.
The FortiSIEM 7.4 User Guide states that FortiSIEM supports discovery of FortiEMS servers by using the FortiEMS Management Server API with basic username/password authentication. It also explains that after FortiEMS discovery, FortiSIEM can tag or untag a host by using classification tags on the FortiEMS server. In a ZTNA deployment, those tags are imported by member Fortinet devices, especially FortiGate firewalls, and are referenced in ZTNA firewall rules. The guide gives a common use case in which a host is tagged as suspicious or potentially compromised so firewall rules can isolate it or allow only minimal traffic. Host software versions, FortiSIEM license information, and host login credentials are not the FortiEMS API information relevant to this ZTNA tagging workflow. The API integration supports retrieving FortiEMS-managed host context and using classification/ZTNA tags for automated response and policy enforcement.
質問 # 41
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?
正解：D
解説：
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.
質問 # 42
You need a model that predicts a target field based on other fields in a dataset and then triggers an anomaly if the value does not match the prediction. Which machine learning (ML) algorithm will you use to build this type of model?
正解：D
解説：
Regression models predict the value of a target field using other fields as predictors. FortiSIEM can then generate anomalies when the observed value significantly deviates from the predicted value.
質問 # 43
A critical server is sending traffic that is triggering a high severity outbound intrusion prevention system (IPS) permitted IPS exploit rule. This traffic must be allowed. Which two items must you configure to prevent this sever from triggering the incident? (Choose two.)
正解：A、E
解説：
To stop a known allowed server from generating this incident, you can tune the rule logic by excluding the server IP address in the subpattern filter. You can also create a rule exception for that IP address, which suppresses incident generation for matching traffic from the approved server while leaving the rule active for other sources.
質問 # 44
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?
正解：D
解説：
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.
質問 # 45
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