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| Section | Objectives |
|---|---|
| Topic 1: Rules and Incident Management | - Incidents and Notifications
|
| Topic 2: Analytics and Search | - Query and Event Analysis
|
| Topic 3: Advanced Analytics and Integrations | - ML, UEBA, and ZTNA
|
| Topic 4: FortiEDR and Security Policy Integration | - FortiEDR Security Configuration
|
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NEW QUESTION # 86
You need to model for predicting a target based on other fields in the dataset and then trigger an anomaly if the value does not match the prediction.
Which machine learning algorithm will build this type of model?
Answer: B
Explanation:
The correct answer is C. Regression. Regression is the machine learning task used when a model predicts a target value based on other fields in a dataset. In FortiSIEM's machine learning workflow, regression configuration includes selecting Fields to use for Prediction and a Field to Predict. The User Guide explains that during regression training, the analyst chooses the fields used for prediction and the field being predicted, then trains the model with a Train factor. This matches the question exactly:
the model predicts a target based on other fields, and the inference phase can identify anomalies when observed values differ significantly from predicted values. Classification is used to assign records to discrete classes or categories, not to predict a continuous target metric from other variables. Clustering groups similar observations without a predefined target field. Forecasting predicts future values over time, normally based on time-series behavior. Because the question specifically describes predicting a target field from other dataset fields and detecting mismatch from the prediction, the correct machine learning method is Regression.
NEW QUESTION # 87
Refer to the exhibit.
What will happen when a device being analyzed by the machine learning configuration shown in the exhibit has consistently high memory utilization?
Answer: B
Explanation:
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.
NEW QUESTION # 88
Refer to the exhibit.
What is the Group: VPN Gateway value a reference to?
Answer: D
Explanation:
In FortiSIEM analytics filters, a value shown as Group: VPN Gateway refers to a CMDB device group. The query uses that CMDB group to match events where the Source IP belongs to devices in the VPN Gateway group.
NEW QUESTION # 89
Which two processes run analytical queries and must always be running to perform searches?
(Choose two.)
Answer: A,D
Explanation:
Analytical searches depend on the query master and query worker processes. The master coordinates the query execution, while the workers run the query tasks against the event data so search results can be returned.
NEW QUESTION # 90
What can you use to send data to FortiSIEM for user and entity behavior analytics (UEBA)?
Answer: C
NEW QUESTION # 91
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