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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Incident Detection, Investigation and Response | 15% | - Applying incident response workflows and escalation - Using dashboards and tools for incident investigation |
| Topic 2: Analytics | 30% | - Performing CMDB and lookup table queries - Building queries from search results and events - Applying group by and data aggregation |
| Topic 3: Event Collection and Normalization | 20% | - Collecting logs and data from multiple sources - Normalizing, parsing, and standardizing event data |
| Topic 4: Monitoring, Reporting and Integration | 15% | - Configuring dashboards and real-time monitoring - Generating compliance and operational reports - Integrating with security tools and ZTNA |
| Topic 5: Event Correlation and Rule Management | 20% | - Creating and configuring correlation rules - Managing alerts, tuning rules, reducing false positives |
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NEW QUESTION # 17
Refer to the exhibit.
An incorrect configuration is shown.
Which setting must you change to successfully apply this configuration to FortiSIEM?
Answer: C
Explanation:
For a regression machine learning job, the train factor must allocate enough data for model training. Setting it to 70% or greater provides sufficient training data so FortiSIEM can successfully apply and train the model configuration.
NEW QUESTION # 18
When using user and entity behavior analytics (UEBA) on FortiSIEM, what must you use to dynamically supply a list of IP addresses to a FortiGate device for blocking purposes?
Answer: B
NEW QUESTION # 19
Which two ways are rule tags used on FortiSIEM? (Choose two.)
Answer: B,C
Explanation:
Rule tags help classify and organize rules so analysts can search events or incidents associated with tagged rules. They can also be used to filter playbooks, making it easier to associate the appropriate playbook workflows with specific categories of rule-triggered incidents.
NEW QUESTION # 20
Refer to the exhibit.
An analyst is troubleshooting the rule shown in the exhibit. It is not generating any incidents, but the filter parameters are generating events on the Analytics tab.
What is wrong with the rule conditions?
Answer: C
Explanation:
The correct answer is C because the rule's Group By attributes determine how events are grouped before the aggregate condition is evaluated. The Study Guide explains that rule conditions are built from subpatterns consisting of event attribute filters and aggregation functions. It also explains that a subpattern combines filters, aggregate, and group by fields to form the rule logic. In this case, the filters may return matching events in Analytics, but the rule still may not trigger because the aggregate condition is calculated separately for each unique Group By combination. The exhibit groups by Destination IP and User while applying COUNT(Source IP) > = 2. This means FortiSIEM does not count all matching events together. Instead, it counts only events that share the same Destination IP and User combination. If no single grouped combination reaches the aggregate threshold, no incident is created. The issue is not the event lookup, not the Destination Host Name format, and not necessarily the aggregate expression itself. The grouping logic is what restricts the counted event set.
NEW QUESTION # 21
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?
Answer: A
Explanation:
The scatter plot shows two visually distinct groupings of data points, making two clusters an appropriate fit for a KMeans ML job.
NEW QUESTION # 22
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