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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% | - Applying incident response workflows and escalation - Using dashboards and tools for incident investigation |
| Analytics | 30% | - Applying group by and data aggregation - Performing CMDB and lookup table queries - Building queries from search results and events |
| Monitoring, Reporting and Integration | 15% | - Integrating with security tools and ZTNA - Generating compliance and operational reports - Configuring dashboards and real-time monitoring |
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NEW QUESTION # 36
Which two ways are rule tags used on FortiSIEM? (Choose two.)
Answer: A,B
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 # 37
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: A
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 # 38
Where must you define and assign a custom python script as a remediation action?
Answer: D
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 # 39
When configuring machine learning (ML), in which step can you modify how the model fits the training data set?
Answer: C
Explanation:
The correct answer is B. Train. In FortiSIEM machine learning, the Train step is where the model is built from the prepared dataset and where model-fitting behavior can be adjusted. The FortiSIEM 7.4 User Guide explains that after preparing data, the analyst goes to Analytics > Machine Learning > Train, selects the machine learning task, chooses the algorithm, selects the prediction/target fields when required, and chooses the Train factor, which determines how much data is used for training versus testing. The guide states that the Train factor should be greater than 70%, meaning 70% of the data is used for training and 30% for testing. It also explains that model quality metrics show how accurately the algorithm predicts the field. For regression, lower MAE means a better fit, and R2 shows how well predictions approximate real data points. Most importantly, the guide states: "If you want to change the algorithm parameters and re-train, then click Tune & Train, change the parameters and click Save
& Train." This confirms that modifying how the model fits the training dataset is done in the Train step, not Prepare Data, Statistics, or Design.
NEW QUESTION # 40
Refer to the exhibit. Which event type attribute value will the FortiSIEM parser save for this event?
Answer: B
Explanation:
FortiSIEM parsers map raw event attributes to normalized event type attributes. In this event, the parser identifies the sysUpTime value and stores it under the normalized FortiSIEM attribute name PH_DEV_MON_SYS_UPTIME.
NEW QUESTION # 41
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