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| Section | Objectives |
|---|---|
| Machine Learning, UEBA, and ZTNA | - Advanced analytics integration
|
| Rules and Subpatterns | - Analytics rules configuration
|
| Incidents, Notifications, and Remediation | - Incident management
|
| FortiEDR Security Settings and Policies | - Security configuration
|
| Analytics | - Query and event analysis
|
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NEW QUESTION # 41
Refer to the exhibit.
You are attempting to tune an anomaly detection machine learning (ML) job. The chart shows there are no anomalies detected, but you are not satisfied with the fit of the ML model.
Which adjustment must you make to train the model and ensure a better fit?
Answer: A
Explanation:
Increasing the number of windows gives the ML job more historical data windows to use during training. This improves the model's baseline calculation and helps produce a better statistical fit for anomaly detection.
NEW QUESTION # 42
What can you use to send data to FortiSIEM for user and entity behavior analytics (UEBA)?
Answer: B
NEW QUESTION # 43
You need a model that predicts a target field based on other fields in a dataset and then triggers an anomaly if the value does not match the prediction. Which machine learning (ML) algorithm will you use to build this type of model?
Answer: D
Explanation:
Regression models predict the value of a target field using other fields as predictors. FortiSIEM can then generate anomalies when the observed value significantly deviates from the predicted value.
NEW QUESTION # 44
Refer to the exhibit.
An analyst is trying to generate an incident with a title that includes the Source IP, Destination IP, User, and Destination Host Name. They are unable to add Destination Host Name as an incident attribute.
What must be changed to allow the analyst to select Destination Host Name as an attribute?
Answer: A
Explanation:
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.
NEW QUESTION # 45
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?
Answer: C
Explanation:
The Run Mode is set to Local, which is not valid for training machine learning models in FortiSIEM. To apply this configuration correctly, the Run Mode must be set to ML, which enables proper model training and prediction using selected fields.
NEW QUESTION # 46
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