Fortinet Fortinet NSE 6 - FortiSIEM 7.4 Analyst NSE6_FSM_AN-7.4 Exam Dumps: Updated Questions & Answers (September 2026)

When an associated rule triggers, FortiSIEM performs all selected actions in the automation policy. In this case, it will send an email/SMS/webhook, run the remediation script, invoke the integration policy (even if none is currently defined), and create a case. All checked actions are executed.

The correct answer is B because FortiSIEM automation policies are designed to execute the actions selected in the policy when the policy criteria match. The FortiSIEM Study Guide states that automation policy actions define what occurs when policy criteria match. It lists possible automation actions such as sending an alert, invoking an integration policy, sending SNMP or HTTPS XML notifications, opening a remedy ticket or creating a FortiSIEM case, sending email or SMS, and running a remediation script. The same Study Guide explains that users can configure “any combination of actions.” Therefore, there is no single-action precedence rule where remediation overrides all other selected actions or email runs only because it appears first. If multiple action checkboxes are selected, FortiSIEM executes the configured selected actions according to the automation policy. In the exhibit, multiple actions are selected, including email/SMS/webhook, remediation/script, integration policy, and case creation. Option C is incorrect because the absence of a defined integration policy does not make FortiSIEM ignore the other selected actions. The policy runs the selected configured actions.

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.

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 best matching analytics search is Username CONTAIN smit. The FortiSIEM Analytics lesson explains keyword and attribute matching by using the CONTAIN operator for searching values inside raw logs or attributes. The Study Guide gives a direct example of FortiSIEM translating keyword logic into a condition such as “Raw Event Log CONTAIN TCP.” That shows the operator is designed for substring-style matching rather than full-value equality. For a user such as JSmith, the string fragment smit is contained inside the username value, so the condition can match events where the username appears as JSmith, jsmith, or possibly in a longer identity string such as a domain-qualified username. Option A is wrong because smith is not the complete username and uses an equality-style comparison. Option B is wrong because it excludes usernames ending in jsmith. Option C uses the wrong operator form for this context. Therefore, the practical FortiSIEM analytics condition that can match the target failed-login event is Username CONTAIN smit.

To detect three failed login attempts within three minutes, you must set the aggregate count to 3 in the subpattern and the time window to 180 seconds in the rule condition. This ensures the rule triggers only if three or more failed logins occur in that timeframe.

The correct answer is A because three minutes equals 180 seconds, and the aggregate threshold must be set to three matching events. The FortiSIEM Study Guide explains that rule conditions specify event attributes and thresholds that trigger the rule and create an incident. It further states that the time window defines the period within which the subpattern must match for the rule condition to be satisfied. The Study Guide’s single-subpattern rule example shows the same principle: the condition has a configured time window, and the Aggregate section uses a function such as COUNT(Matched Events) to require a minimum number of matching events. In this question, the analyst wants the rule to trigger when three failed login attempts happen within three minutes . Therefore, the rule condition time window must be 180 seconds , and the aggregate count must be 3 . A 90-second window would detect only events inside one and a half minutes, not the required three minutes. An aggregate count of 2 would trigger too early because the requirement is three failed attempts.

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 search fails because nested analytics queries require the outer query attribute type to match the inner query display-column data type. The FortiSIEM Study Guide explicitly explains this rule in the Nested Query section: “Another important aspect to understand is that the data value types must match.” It further explains that each inner query display column has a data value type, such as IP, string, or integer, and the outer query attribute must match that type. The FortiSIEM 7.4 User Guide states the same operational requirement: for a nested query to work correctly, “the data type of the filter attribute in the outer query must match up with the data type of one certain display column in the inner query.” Therefore, if the inner query returns a string attribute but the outer query compares it against an IP-type attribute, FortiSIEM cannot produce a valid match. The issue is not the time range, not the use of User and Event Type together, and not the Boolean operator. It is an attribute type mismatch between the query and subquery.

The attribute must be selected as a Triggered Attribute so that it becomes available for incident generation and incident-title substitution. In the FortiSIEM Study Guide’s rule action configuration section, FortiSIEM separates incident attributes from triggered attributes. Triggered attributes are taken from the events that cause the rule to trigger and are then available for incident display and incident context. The guide explains that the rule action step is where an analyst defines the incident generated by a rule and chooses which attributes are carried forward. If Destination Host Name is not selected in the Triggered Attributes list, FortiSIEM cannot use it as an incident attribute in the generated incident title. Option B is wrong because aggregate items are used for calculations such as COUNT, AVG, or SUM, not for making a text attribute available in the incident title. Option C is wrong because Destination Host Name is an event attribute, not an event type. Option D is unrelated; removing Destination IP would not make Destination Host Name selectable.

The fields are highlighted in red because unique values such as Event Receive Time and Raw Event Log cannot be used in group-by operations. Grouping requires aggregatable or consistent values across events, while these fields are unique to each event, making them incompatible for grouping.

The correct answer is A because the highlighted fields are not valid for that grouped/aggregated display configuration. The FortiSIEM 7.4 User Guide notes that some event attributes, functions, and queries are not supported in specific analytics result-filter and display contexts. It lists date fields, including examples such as Event Receive Time , and also lists Raw Event Log and Binary Raw Event Log among unsupported fields for that context. The reason is practical: grouping requires stable values that can combine multiple events into meaningful grouped rows. Attributes such as Event Receive Time and Raw Event Log are highly specific to individual events. If every event has its own receive timestamp or unique raw log content, grouping by those fields defeats aggregation and can create one row per event rather than meaningful grouped output. COUNT(Matched Events) itself is a valid aggregate expression when used correctly. Event Receive Time is available in logs, but it is not appropriate as a grouped field in the configuration shown. Therefore, the red highlighting indicates invalid grouped fields caused by unique/non-groupable values.

A FortiSIEM rule requires detection logic and at least one subpattern structure to evaluate matching events. The FortiSIEM Study Guide explains that a single subpattern rule example consists of filter, aggregate, and group-by sections. It states: “The subpattern, ExcessVPNLoginFailure, consists of three components: Filter, Aggregate, Group By.” That confirms that a subpattern is a core rule component. The FortiSIEM 7.4 User Guide also identifies Detection Technology as part of rule definition and built-in rule metadata. Detection Technology describes the detection method used by the rule, such as correlation, profiling, machine learning, or correlation using lookup tables. Exception policy is not required; it is used to suppress or tune matches under specific conditions. Clear policy is also not required; it controls how an incident can be cleared or auto-cleared after triggering. Therefore, the required components among the listed options are Subpattern and Detection Technology. Without them, the rule lacks both the event-detection structure and the detection-method classification needed for rule evaluation.