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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Event Correlation and Rule Management | 20% | - Creating and configuring correlation rules - Managing alerts, tuning rules, reducing false positives |
| Topic 2: Monitoring, Reporting and Integration | 15% | - Configuring dashboards and real-time monitoring - Generating compliance and operational reports - Integrating with security tools and ZTNA |
| Topic 3: Incident Detection, Investigation and Response | 15% | - Using dashboards and tools for incident investigation - Applying incident response workflows and escalation |
| Topic 4: Event Collection and Normalization | 20% | - Collecting logs and data from multiple sources - Normalizing, parsing, and standardizing event data |
| Topic 5: Analytics | 30% | - Building queries from search results and events - Performing CMDB and lookup table queries - Applying group by and data aggregation |
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12. Frage
How can you use the configuration management database (CMDB) in an analytics search?
Antwort: B
Begründung:
In an analytics search, FortiSIEM can use devices stored in the CMDB as searchable entities, such as source IP addresses. This allows searches and rule logic to reference known assets from the CMDB instead of relying only on manually entered IP values.
13. Frage
In an automation policy, which two methods can you use to notify analysts when an incident is triggered? (Choose two.)
Antwort: C,D
Begründung:
In FortiSIEM automation policies, analysts can be notified of triggered incidents through FortiSIEM Case (which creates and assigns a case for follow-up) and Email notifications (which send alerts directly to recipients). These methods ensure prompt awareness and response to security events.
14. Frage
You are creating a rule to fill a gap in your organization's MITRE ATT&CK rule coverage matrix.
How can you associate the rule with an appropriate tactics, techniques and procedures (TTP) category?
Antwort: C
Begründung:
A rule is associated with MITRE ATT&CK coverage by configuring the relevant tactics, techniques, and procedures category in the rule properties. This mapping is applied as part of the rule configuration so incidents generated by the rule contribute to the appropriate coverage category.
15. Frage
Refer to the exhibit.
An analyst wants to perform a KMeans machine learning (ML) job on this data. How many N clusters would be a good fit for the data? (Choose one answer)
Antwort: D
Begründung:
The best answer is A. Two . The exhibit shows an Analytics Search scatter plot with two visually distinct groups of data points: one isolated group around the lower-left area of the chart and another dense group near the upper-right area. For KMeans clustering, the analyst must provide the number of clusters based on the observed structure of the data. The FortiSIEM 7.4 User Guide describes KMeans as "an unsupervised clustering algorithm that groups data points into user specified K groups so that each data point belongs to one group." It also states that KMeans "tries to iteratively minimize intra-cluster distance and maximize inter- cluster distance" and notes that the user must "specify the number of clusters based on user's knowledge of data." The same FortiSIEM guide explains that during Clustering Local Mode training, the analyst chooses the algorithm and, "for KMeans choose the cluster size as a guess." In this exhibit, the natural guess is
2, because the scatter plot separates into two obvious groups. Fifty or 100 clusters would overfit the small number of visible groups, while one cluster would merge two clearly separate behaviors into a single cluster.
16. Frage
Refer to the exhibit.
Which two lookup types can you reference as the subquery in a nested analytics query? (Choose two.)
Antwort: A,B
Begründung:
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.
17. Frage
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