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| Section | Weight | Objectives |
|---|---|---|
| Event Collection and Normalization | 20% | - Collecting logs and data from multiple sources - Normalizing, parsing, and standardizing event data |
| Event Correlation and Rule Management | 20% | - Managing alerts, tuning rules, reducing false positives - Creating and configuring correlation rules |
| Incident Detection, Investigation and Response | 15% | - Using dashboards and tools for incident investigation - Applying incident response workflows and escalation |
| Monitoring, Reporting and Integration | 15% | - Generating compliance and operational reports - Integrating with security tools and ZTNA - Configuring dashboards and real-time monitoring |
| Analytics | 30% | - Applying group by and data aggregation - Performing CMDB and lookup table queries - Building queries from search results and events |
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NEW QUESTION # 77
From which two sources can you import data to train FortiSIEM machine learning? (Choose two.)
Answer: B,D
NEW QUESTION # 78
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: D
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 # 79
Refer to the exhibit.
Which two lookup types can you reference as the subquery in a nested analytics query? (Choose two.)
Answer: B,D
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 # 80
Refer to the exhibit.
As shown in the exhibit, why are some of the fields highlighted in red?
Answer: B
Explanation:
The fields are highlighted in red because unique values such as Event Receive Time and Raw Event Log cannot be used in group-by operations. Grouping requires aggregatable or consistent values across events, while these fields are unique to each event, making them incompatible for grouping.
The correct answer is A because the highlighted fields are not valid for that grouped/aggregated display configuration. The FortiSIEM 7.4 User Guide notes that some event attributes, functions, and queries are not supported in specific analytics result-filter and display contexts. It lists date fields, including examples such as Event Receive Time , and also lists Raw Event Log and Binary Raw Event Log among unsupported fields for that context. The reason is practical: grouping requires stable values that can combine multiple events into meaningful grouped rows. Attributes such as Event Receive Time and Raw Event Log are highly specific to individual events. If every event has its own receive timestamp or unique raw log content, grouping by those fields defeats aggregation and can create one row per event rather than meaningful grouped output. COUNT (Matched Events) itself is a valid aggregate expression when used correctly. Event Receive Time is available in logs, but it is not appropriate as a grouped field in the configuration shown. Therefore, the red highlighting indicates invalid grouped fields caused by unique/non-groupable values.
NEW QUESTION # 81
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 # 82
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