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| Section | Objectives |
|---|---|
| FortiEDR Security Settings and Policies | - Security configuration
|
| Incidents, Notifications, and Remediation | - Incident management
|
| Machine Learning, UEBA, and ZTNA | - Advanced analytics integration
|
| Rules and Subpatterns | - Analytics rules configuration
|
| Analytics | - Query and event analysis
|
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NEW QUESTION # 30
Refer to the exhibit.
You want to create a dashboard like the one shown in the exhibit on your FortiSIEM device.
Which item defines the data that these widgets display?
Answer: C
Explanation:
FortiSIEM dashboard widgets display data based on reports. Each widget uses an underlying report or analytics query to define the dataset, aggregation, and visualization shown on the dashboard.
NEW QUESTION # 31
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: B
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 # 32
Where must you define and assign a custom python script as a remediation action?
Answer: C
Explanation:
A custom Python script used as a remediation action must be defined and assigned within an Automation Policy in FortiSIEM. The automation policy framework allows you to configure triggers, select incidents or rules that activate the script, and define how the Python script executes automatically to remediate detected issues.
NEW QUESTION # 33
How can you query the configuration management database (CMDB) in an analytics search?
Answer: A
Explanation:
The correct answer is A because CMDB objects are referenced from the Value field after selecting the appropriate event attribute and operator. The FortiSIEM Study Guide gives a structured search example that references the CMDB. In that example, the attribute is Reporting IP, the operator is IN, and the value is selected from CMDB groups such as Devices: Windows and Networks: Inside Net. The guide explains that to show events reported by Windows servers within a specific network, you set the attribute and operator first, then browse the CMDB and select the relevant CMDB group value. This confirms the workflow: the CMDB reference is chosen as the value of the condition, not as the attribute itself. Option B is incorrect because the CMDB tab is not used to launch the analytics search this way.
Option C is not a valid workflow. Option D is wrong because the attribute is selected from event or CMDB attribute lists, while the CMDB object or group is selected in the value field.
NEW QUESTION # 34
Which data collection method generates the most comprehensive information for FortiSIEM user entity and behavior analytics (UEBA) models?
Answer: D
Explanation:
The Windows UEBA agent collects detailed user activity and endpoint behavior data specifically designed for FortiSIEM UEBA analytics. It provides richer telemetry for behavioral modeling, anomaly detection, and user activity correlation than standard logs or general-purpose agents.
NEW QUESTION # 35
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