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| Section | Objectives |
|---|---|
| Topic 1: Rules and Subpatterns | - Analytics rules configuration
|
| Topic 2: Analytics | - Query and event analysis
|
| Topic 3: FortiEDR Security Settings and Policies | - Security configuration
|
| Topic 4: Machine Learning, UEBA, and ZTNA | - Advanced analytics integration
|
| Topic 5: Incidents, Notifications, and Remediation | - Incident management
|
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NEW QUESTION # 28
Refer to the exhibit.
The configuration for a machine learning (ML) dataset using anomaly detection is shown.
If data for this model is generated every hour, how long must the FortiSIEM device be up before it can produce a valid training set?
Answer: A
Explanation:
The Windows parameter is set to 10, meaning FortiSIEM requires 10 data windows to build a valid training baseline. Since data is generated every hour, the device must collect 10 hours of data before producing a valid training set.
NEW QUESTION # 29
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 # 30
Refer to the exhibit.
If you group the events by User and Count attributes, how many results will FortiSIEM display?
Answer: A
Explanation:
The verified answer is D. Five . FortiSIEM grouping is based on unique combinations of the selected Group By fields. The Study Guide explains this behavior clearly: if multiple events have the same selected Group By values, "they are grouped together in one row," and the count column tracks the number of events for each row. In this question, the selected fields are User and Count . The six raw rows contain these combinations:
Mike/4, Bob/3, Alice/2, Alice/2, Bob/6, and Mike/5. Because Alice/2 appears twice, those two rows are grouped into a single result. The remaining combinations are unique. So FortiSIEM displays five grouped results, not six. Six would be correct only if every row had a unique User-and-Count combination, or if grouping included another differentiating attribute such as Source IP. Since the question specifically groups only by User and Count, duplicate User/Count pairs collapse into one row. Therefore, the correct result count is five .
NEW QUESTION # 31
Refer to the exhibit.
What is the Group: FortiSIEM Analysts value referring to?
Answer: C
Explanation:
The correct answer is C. CMDB user group . In FortiSIEM, users and user groups are maintained as CMDB objects and can be referenced in analytics filters and rule logic. The FortiSIEM 7.4 User Guide table of contents explicitly includes CMDB management for users, viewing user information, adding users, editing or deleting users, performing operations on users, and working with user groups. This confirms that user groups are part of the FortiSIEM CMDB data model. The query shown in the exhibit uses the Analytics filter with the User attribute and the value Group: FortiSIEM Analysts . That syntax indicates that FortiSIEM is referencing a FortiSIEM-defined user group from CMDB, not an LDAP group directly and not an Active Directory group directly. LDAP and Active Directory can be used to discover or authenticate users, but once referenced as a FortiSIEM analytics group value, the object is a CMDB user group. FortiSIEM organization groups are tenant/organization constructs and are not the same as CMDB user groups.
NEW QUESTION # 32
When configuring machine learning (ML), in which step can you modify how the model fits the training data set?
Answer: C
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 # 33
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