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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Event Correlation and Rule Management | 20% | - Managing alerts, tuning rules, reducing false positives - Creating and configuring correlation rules |
| Topic 2: Monitoring, Reporting and Integration | 15% | - Generating compliance and operational reports - Configuring dashboards and real-time monitoring - Integrating with security tools and ZTNA |
| Topic 3: Event Collection and Normalization | 20% | - Collecting logs and data from multiple sources - Normalizing, parsing, and standardizing event data |
| Topic 4: Incident Detection, Investigation and Response | 15% | - Using dashboards and tools for incident investigation - Applying incident response workflows and escalation |
| Topic 5: Analytics | 30% | - Building queries from search results and events - Applying group by and data aggregation - Performing CMDB and lookup table queries |
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NEW QUESTION # 80
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: B
Explanation:
The correct answer is A . In FortiSIEM machine learning training, the Train factor controls how much of the prepared dataset is used for training and how much is reserved for testing. The FortiSIEM 7.4 User Guide states in the regression training procedure that the analyst must choose the Train factor and that it "should be greater than 70%." It also explains the meaning of the value: 70% of the data is used for training and 30% is used for testing. This directly supports option A. Option B is incorrect because the supported running modes include Local, Local Auto, and AWS; "ML" is not the required run mode. Option C is incorrect because regression can use multiple fields for prediction; the guide instructs the user to choose the fields to use for prediction and the field to predict. Option D is incorrect because prediction input fields and target field should not be the same. Regression builds a model to predict a target field based on other fields, so the configuration must use a valid Train factor rather than matching predictor and target fields.
NEW QUESTION # 81
Which two elements can you use to define how an automation policy activates? (Choose two.)
Answer: B,D
NEW QUESTION # 82
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: D
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 # 83
Several new internal servers are generating incidents and must be excluded from several FortiSIEM rules. How must you tune rules to exclude several undiscovered devices from rules?
Answer: C
Explanation:
Creating a device group containing the servers and applying that group as a filter exclusion in the relevant rules is the proper tuning method for excluding multiple undiscovered devices from generating incidents across several rules.
NEW QUESTION # 84
Which rule logic should you use to apply a user entity and behavior analytics (UEBA) tag to an event for a failed login incident by the account JSmith?
Answer: B
Explanation:
To apply a UEBA tag to events for a specific failed-login account, the rule logic should match the username field exactly. Using an exact username match ensures the tag is applied only to events associated with the JSmith account.
NEW QUESTION # 85
......
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