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| Section | Objectives |
|---|---|
| Analytics and Search | - Query and Event Analysis
|
| Advanced Analytics and Integrations | - ML, UEBA, and ZTNA
|
| FortiEDR and Security Policy Integration | - FortiEDR Security Configuration
|
| Rules and Incident Management | - Incidents and Notifications
|
>> NSE6_FSM_AN-7.4 Online Test <<
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18. Frage
Which three types of data can you use to train FortiSIEM machine learning (ML)? (Choose three.)
Antwort: A,C,E
Begründung:
FortiSIEM machine learning models can be trained using structured data from CSV files, FortiSIEM analytical reports, and SQL database sources. These sources provide the historical datasets needed to prepare, train, and evaluate the ML model.
19. Frage
What must match when referencing an inner query from an outer query?
Antwort: D
Begründung:
When creating an inner query in FortiSIEM, the referenced attribute in the outer and inner queries must share the same data type (for example, IP address, string, or integer). This ensures the system can properly correlate and filter results between the two queries during execution.
20. Frage
Refer to the exhibit.
The configuration shown in the exhibit is incorrect.
What must you change to allow this configuration to be successfully applied to FortiSIEM?
Antwort: D
Begründung:
The correct answer is A . In FortiSIEM machine learning training, the Train factor controls how much of the prepared dataset is used for training and how much is reserved for testing. The FortiSIEM 7.4 User Guide states in the regression training procedure that the analyst must choose the Train factor and that it "should be greater than 70%." It also explains the meaning of the value: 70% of the data is used for training and 30% is used for testing. This directly supports option A. Option B is incorrect because the supported running modes include Local, Local Auto, and AWS; "ML" is not the required run mode. Option C is incorrect because regression can use multiple fields for prediction; the guide instructs the user to choose the fields to use for prediction and the field to predict. Option D is incorrect because prediction input fields and target field should not be the same. Regression builds a model to predict a target field based on other fields, so the configuration must use a valid Train factor rather than matching predictor and target fields.
21. Frage
You want to create a rule with multiple subpatterns but trigger an incident only if three different subpatterns are matched over a 24-hour period.
Where must you define the time period that the rule uses to evaluate all the subpatterns? (Choose one answer)
Antwort: D
Begründung:
The time period must be defined under the Define Condition tab of the rule. The FortiSIEM Study Guide states that a rule has three components: General , Condition , and Action . The General section defines the overall purpose and scope of the rule, the Condition section specifies which conditions trigger the rule, and the Action section configures what happens after the rule is triggered. The Study Guide further explains that on the Define Condition page, FortiSIEM specifies "the conditions-event attributes and thresholds-that will trigger the rule and create an incident," including "the time window" within which the subpatterns must match for the rule condition to be satisfied. It also states that when there is more than one subpattern, you must define the logic between subpatterns and their relationships or constraints.
For a 24-hour multiple-subpattern rule, the guide gives an example using an 86400-second time window, which equals 24 hours, and shows it under Resource > Rules > Define Condition. Therefore, the time period is not configured inside each individual subpattern, not under General, and not under Define Action.
22. Frage
Refer to the exhibit.
An analyst wants the rule shown in the exhibit to trigger when three failed login attempts occur within three minutes.
What should the values be for the condition time window and aggregate count?
Antwort: C
Begründung:
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.
23. Frage
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