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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Event Collection and Normalization | 20% | - Normalizing, parsing, and standardizing event data - Collecting logs and data from multiple sources |
| Topic 2: Analytics | 30% | - Applying group by and data aggregation - Performing CMDB and lookup table queries - Building queries from search results and events |
| Topic 3: Incident Detection, Investigation and Response | 15% | - Using dashboards and tools for incident investigation - Applying incident response workflows and escalation |
| Topic 4: Monitoring, Reporting and Integration | 15% | - Generating compliance and operational reports - Configuring dashboards and real-time monitoring - 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 # 32
What feature defines when an incident is created by FortiSIEM?
Answer: B
NEW QUESTION # 33
Refer to the exhibit.
FortiSIEM is receiving syslog events from a firewall.
You are trying to search raw event logs for traffic from the last two hours that contain the keyword
"UDP". However, you are getting no results from the search.
Based on the filter shown in the exhibit, why are you getting no search results?
Answer: B
Explanation:
The = operator requires an exact match of the entire Raw Event Log field. To search for logs containing the keyword UDP within the raw log text, a contains-style operator must be used instead of an exact equality comparison.
NEW QUESTION # 34
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)
Answer: C
Explanation:
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.
NEW QUESTION # 35
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 # 36
Refer to the exhibit.
What is this rule attempting to match? (Choose one answer)
Answer: B
Explanation:
The rule is matching VPN logon failure events where the Source Country is outside the configured home country . In the exhibit, the filter section shows Event Type IN EventTypes: VPN Logon Failure and Source Country NOT IN GeoCountries: My Home . That means the source must be outside the home- country geo group. The aggregate condition shows COUNT(Matched Events) > = 3 , so the rule is looking for at least three matching failed VPN logon events. The Group By section uses Source IP and User , so FortiSIEM evaluates the count per unique source IP and user combination, not by different countries.
The FortiSIEM Study Guide explains that a rule subpattern contains three components: Filter , Aggregate , and Group By . It states that the filter identifies the matching event group, the aggregate function specifies how many events must match, and Group By combines events with the same grouped attributes into one row while the count tracks those events.
Option A is wrong because the rule does not count different countries. Options C and D are wrong because the source country is explicitly NOT IN My Home, not inside the home country.
NEW QUESTION # 37
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