NSE6_FSM_AN-7.4 Exam Dumps - Fortinet NSE 6 Network Security Specialist Questions and Answers

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.

The correct answer is C because the rule’s Group By attributes determine how events are grouped before the aggregate condition is evaluated. The Study Guide explains that rule conditions are built from subpatterns consisting of event attribute filters and aggregation functions. It also explains that a subpattern combines filters, aggregate, and group by fields to form the rule logic. In this case, the filters may return matching events in Analytics, but the rule still may not trigger because the aggregate condition is calculated separately for each unique Group By combination. The exhibit groups by Destination IP and User while applying COUNT(Source IP) > = 2. This means FortiSIEM does not count all matching events together. Instead, it counts only events that share the same Destination IP and User combination. If no single grouped combination reaches the aggregate threshold, no incident is created. The issue is not the event lookup, not the Destination Host Name format, and not necessarily the aggregate expression itself. The grouping logic is what restricts the counted event set.

The correct answer is A. A configuration management database (CMDB) device group . In the exhibit, the analytics filter uses Source IP IN Group: VPN Gateway . In FortiSIEM analytics, values shown as Group: for IP/device-related attributes commonly reference FortiSIEM CMDB groups, not firewall address groups or rule folders. The FortiSIEM 7.4 User Guide explains how CMDB groups are inserted into queries: to add a CMDB group, the user selects an attribute, selects an operator such as IN , and then selects a value from CMDB. The guide gives a direct example where a reporting IP is matched using a firewall device group, expressed as a condition equivalent to “reptDevIpAddr IN Firewall group.”

This matches the exhibit’s structure: Source IP is the event attribute, IN is the operator, and Group: VPN Gateway is the selected CMDB group value. A FortiSIEM watchlist is different; the Study Guide describes watchlists as containers of similar items that can be referenced in searches, rules, and reports, but they are managed under Resources > Watch Lists, not shown here as a CMDB-style device group value. A FortiGate address group exists on FortiGate, not as this FortiSIEM analytics CMDB group reference.

The best answer is A. Two . The exhibit shows an Analytics Search scatter plot with two visually distinct groups of data points: one isolated group around the lower-left area of the chart and another dense group near the upper-right area. For KMeans clustering, the analyst must provide the number of clusters based on the observed structure of the data. The FortiSIEM 7.4 User Guide describes KMeans as “an unsupervised clustering algorithm that groups data points into user specified K groups so that each data point belongs to one group.” It also states that KMeans “tries to iteratively minimize intra-cluster distance and maximize inter-cluster distance” and notes that the user must “specify the number of clusters based on user’s knowledge of data.”

The same FortiSIEM guide explains that during Clustering Local Mode training, the analyst chooses the algorithm and, “for KMeans choose the cluster size as a guess.” In this exhibit, the natural guess is 2, because the scatter plot separates into two obvious groups. Fifty or 100 clusters would overfit the small number of visible groups, while one cluster would merge two clearly separate behaviors into a single cluster.