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| Section | Objectives |
|---|---|
| Analytics and Search | - Query and Event Analysis
|
| Advanced Analytics and Integrations | - ML, UEBA, and ZTNA
|
| FortiEDR and Security Policy Integration | - FortiEDR Security Configuration
|
| Rules and Incident Management | - Rules and Alerts
|
>> NSE6_FSM_AN-7.4 New Questions <<
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NEW QUESTION # 84
You want to create a rule with multiple subpatterns but trigger an incident only if three different subpatterns are matched over a 24-hour period. Where must you define the time period that the rule uses to evaluate all the subpatterns?
Answer: D
Explanation:
For a rule that evaluates multiple subpatterns together, the shared evaluation period is configured as the rule's time window under the General tab. This defines the overall period FortiSIEM uses to correlate the required subpattern matches before triggering the incident.
NEW QUESTION # 85
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: B
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 # 86
Refer to the exhibit.
If you group the events by User , Source IP , and Count attributes, how many results will FortiSIEM display?
Answer: C
Explanation:
Grouping by User, Source IP, and Count means that each unique combination of those three attributes will be treated as a separate result. In the table, all six rows have distinct combinations of User, Source IP, and Count
- so FortiSIEM will display 6 results.
Six because grouping by User , Source IP , and Count creates a separate result for every unique combination of those three selected attributes. The FortiSIEM Study Guide explains this grouping behavior in the single- subpattern rule example: "If multiple VPN login failure events have the same source IP address, reporting device, reporting IP address, and user, they are grouped together in one row, and the count column tracks the number of events for each of those rows." Applying that rule here, FortiSIEM compares all selected Group By fields together. In the exhibit, every row has a unique Source IP address, even where the same user appears more than once. For example, Mike appears twice, but the Source IP and Count values are different. Alice appears twice with Count 2, but the Source IP values are different. Bob appears twice, but both Source IP and Count are different. Since no row has the same User, Source IP, and Count combination as another row, FortiSIEM displays all six rows.
NEW QUESTION # 87
Refer to the exhibit.
If you group the events by User and Count attributes, how many results will FortiSIEM display?
Answer: C
Explanation:
The verified answer is D. Five . FortiSIEM grouping is based on unique combinations of the selected Group By fields. The Study Guide explains this behavior clearly: if multiple events have the same selected Group By values, "they are grouped together in one row," and the count column tracks the number of events for each row. In this question, the selected fields are User and Count . The six raw rows contain these combinations:
Mike/4, Bob/3, Alice/2, Alice/2, Bob/6, and Mike/5. Because Alice/2 appears twice, those two rows are grouped into a single result. The remaining combinations are unique. So FortiSIEM displays five grouped results, not six. Six would be correct only if every row had a unique User-and-Count combination, or if grouping included another differentiating attribute such as Source IP. Since the question specifically groups only by User and Count, duplicate User/Count pairs collapse into one row. Therefore, the correct result count is five .
NEW QUESTION # 88
When configuring machine learning (ML), in which step can you modify how the model fits the training data set?
Answer: D
Explanation:
The Statistics step is where you tune statistical model parameters that affect how closely the machine learning model fits the training data set, such as deviation-related settings used for anomaly detection.
NEW QUESTION # 89
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