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| Section | Objectives |
|---|---|
| Topic 1: FortiEDR and Security Policy Integration | - FortiEDR Security Configuration
|
| Topic 2: Analytics and Search | - Query and Event Analysis
|
| Topic 3: Rules and Incident Management | - Incidents and Notifications
|
| Topic 4: Advanced Analytics and Integrations | - ML, UEBA, and ZTNA
|
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NEW QUESTION # 38
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: A
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 # 39
Refer to the exhibit. If you group these events by the Reporting IP, Event Type, and User attributes, how many results will FortiSIEM display?
Answer: A
Explanation:
All events share the same Reporting IP and Event Type. Grouping by User produces three distinct groups: Mike, Bob, and Alice. Therefore, FortiSIEM displays three results.
NEW QUESTION # 40
Refer to the exhibit. Which two things that happen when this automation policy triggers? (Choose two.)
Answer: C,D
Explanation:
The automation policy has Send Email/SMS/Webhook to the target users enabled, so an email notification is sent. It also has Run Remediation/Script enabled, so the configured remediation script is executed when the policy triggers.
NEW QUESTION # 41
When configuring machine learning (ML), in which step can you modify how the model fits the training data set?
Answer: A
Explanation:
The correct answer is B. Train. In FortiSIEM machine learning, the Train step is where the model is built from the prepared dataset and where model-fitting behavior can be adjusted. The FortiSIEM 7.4 User Guide explains that after preparing data, the analyst goes to Analytics > Machine Learning > Train, selects the machine learning task, chooses the algorithm, selects the prediction/target fields when required, and chooses the Train factor, which determines how much data is used for training versus testing. The guide states that the Train factor should be greater than 70%, meaning 70% of the data is used for training and 30% for testing. It also explains that model quality metrics show how accurately the algorithm predicts the field. For regression, lower MAE means a better fit, and R2 shows how well predictions approximate real data points. Most importantly, the guide states: "If you want to change the algorithm parameters and re-train, then click Tune & Train, change the parameters and click Save
& Train." This confirms that modifying how the model fits the training dataset is done in the Train step, not Prepare Data, Statistics, or Design.
NEW QUESTION # 42
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: C
NEW QUESTION # 43
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