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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Monitoring, Reporting and Integration | 15% | - Configuring dashboards and real-time monitoring - Generating compliance and operational reports - Integrating with security tools and ZTNA |
| Topic 2: Event Correlation and Rule Management | 20% | - Managing alerts, tuning rules, reducing false positives - Creating and configuring correlation rules |
| Topic 3: Analytics | 30% | - Applying group by and data aggregation - Building queries from search results and events - Performing CMDB and lookup table queries |
| Topic 4: Event Collection and Normalization | 20% | - Normalizing, parsing, and standardizing event data - Collecting logs and data from multiple sources |
| Topic 5: Incident Detection, Investigation and Response | 15% | - Applying incident response workflows and escalation - Using dashboards and tools for incident investigation |
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NEW QUESTION # 58
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.)
Answer: A,E
Explanation:
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.
NEW QUESTION # 59
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?
Answer: C
Explanation:
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.
NEW QUESTION # 60
Several new internal servers are generating incidents and must be excluded from several FortiSIEM rules. How must you tune rules to exclude several undiscovered devices from rules?
Answer: B
Explanation:
Creating a device group containing the servers and applying that group as a filter exclusion in the relevant rules is the proper tuning method for excluding multiple undiscovered devices from generating incidents across several rules.
NEW QUESTION # 61
When using user and entity behavior analytics (UEBA) on FortiSIEM, what must you use to dynamically supply a list of IP addresses to a FortiGate device for blocking purposes?
Answer: A
NEW QUESTION # 62
When configuring machine learning (ML), in which step can you modify how the model fits the training data set?
Answer: C
NEW QUESTION # 63
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