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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Analytics | 30% | - Building queries from search results and events - Performing CMDB and lookup table queries - Applying group by and data aggregation |
| Topic 2: Event Correlation and Rule Management | 20% | - Managing alerts, tuning rules, reducing false positives - Creating and configuring correlation rules |
| Topic 3: Event Collection and Normalization | 20% | - Collecting logs and data from multiple sources - Normalizing, parsing, and standardizing event data |
| Topic 4: Monitoring, Reporting and Integration | 15% | - Configuring dashboards and real-time monitoring - Generating compliance and operational reports - Integrating with security tools and ZTNA |
| Topic 5: Incident Detection, Investigation and Response | 15% | - Using dashboards and tools for incident investigation - Applying incident response workflows and escalation |
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問題 #54
Refer to the exhibit. Why is this search not producing any results?
答案：D
解題說明：
The query contains a nested query attribute type mismatch because the User attribute is being compared against a Device IP group value. The attribute type and referenced group type must match for the search to return results.
問題 #55
You need a model for predicting a target field based on other fields in a dataset and then trigger an anomaly if the value does not match the prediction. Which machine learning algorithm will build this type of model?
答案：A
解題說明：
A Regression algorithm is used when predicting a continuous or numeric target field based on other features in the dataset. In FortiSIEM, regression-based machine learning models establish expected values, and an anomaly is triggered when the actual observed value significantly deviates from the regression prediction.
問題 #56
Refer to the exhibit.
Which statement about the time range settings defined in the nested query is accurate?
答案：D
解題說明：
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.
問題 #57
Rules on FortiSIEM are usually processed as events are collected (streaming). How can you create a rule to evaluate events over an 8-hour period?
答案：A
解題說明：
Setting the Evaluation Mode to Scheduled allows FortiSIEM to evaluate accumulated events over a defined historical period, such as 8 hours, instead of processing events only in real time through streaming evaluation.
問題 #58
Which two data areas can you use for user and entity behavior analytics (EBA) machine learning models?
(Choose two.)
答案：B,C
解題說明：
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.
問題 #59
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