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| Section | Objectives |
|---|---|
| Topic 1: Rules and Incident Management | - Incidents and Notifications
|
| Topic 2: FortiEDR and Security Policy Integration | - FortiEDR Security Configuration
|
| Topic 3: Advanced Analytics and Integrations | - ML, UEBA, and ZTNA
|
| Topic 4: Analytics and Search | - Query and Event Analysis
|
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NEW QUESTION # 92
Refer to the exhibit. Which two items can be referenced in the incident details when this rule is triggered and creates an incident? (Choose two.)
Answer: A,C
Explanation:
Incident details can reference attributes defined in the Group By section because those values are preserved and available when the incident is generated. In this rule, User and Reporting Device are grouped attributes and can therefore be referenced in the incident details.
NEW QUESTION # 93
Refer to the exhibit.
If you group the events by User , Source IP , and Count attributes, how many results will FortiSIEM display?
Answer: C
Explanation:
Grouping by User, Source IP, and Count means that each unique combination of those three attributes will be treated as a separate result. In the table, all six rows have distinct combinations of User, Source IP, and Count
- so FortiSIEM will display 6 results.
Six because grouping by User , Source IP , and Count creates a separate result for every unique combination of those three selected attributes. The FortiSIEM Study Guide explains this grouping behavior in the single- subpattern rule example: "If multiple VPN login failure events have the same source IP address, reporting device, reporting IP address, and user, they are grouped together in one row, and the count column tracks the number of events for each of those rows." Applying that rule here, FortiSIEM compares all selected Group By fields together. In the exhibit, every row has a unique Source IP address, even where the same user appears more than once. For example, Mike appears twice, but the Source IP and Count values are different. Alice appears twice with Count 2, but the Source IP values are different. Bob appears twice, but both Source IP and Count are different. Since no row has the same User, Source IP, and Count combination as another row, FortiSIEM displays all six rows.
NEW QUESTION # 94
Refer to the exhibit.
Which two lookup types can you reference as the subquery in a nested analytics query? (Choose two.)
Answer: A,C
Explanation:
The FortiSIEM Study Guide defines a nested query as a query that has another query embedded within it, where the embedded query is known as the subquery. The FortiSIEM 7.4 User Guide lists the supported nested search scenarios: Outer CMDB Query, Inner Event Query ; Outer Event Query, Inner Event Query ; and Outer Event Query, Inner CMDB Query . These supported combinations prove that the inner or subquery can be either an Event Query or a CMDB Query. LDAP Query and SNMP Query are not listed as supported nested analytics subquery types. LDAP may be used for user discovery or authentication, and SNMP may be used for monitoring or discovery, but neither is referenced as a nested analytics query type.
The User Guide also explains that the outer query uses Select from Report to reference the saved inner query and that the matching attribute data types must align between the outer query and the selected display column in the inner query.
NEW QUESTION # 95
Refer to the exhibit.
Which value would you expect the FortiSIEM parser to use to populate the Application Name field?
Answer: A
Explanation:
The Application Name field in FortiSIEM is typically populated using the value of the app field in the raw log. In this event, app="SSL", so "SSL" is the expected application name parsed by FortiSIEM.
NEW QUESTION # 96
Which two data areas can you use for user and entity behavior analytics (UEBA) machine learning models? (Choose two.)
Answer: C,D
Explanation:
FortiSIEM's UEBA models analyze user and entity behavior by correlating data such as location (for detecting unusual logins or access patterns) and network activity (for identifying abnormal communication or traffic behaviors). These data areas enable the system to build baseline profiles and detect anomalies indicating potential insider threats or compromised accounts.
NEW QUESTION # 97
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