Fortinet FCP_FSM_AN-7.2復習問題集 & FCP_FSM_AN-7.2赤本合格率

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Fortinet FCP_FSM_AN-7.2 認定試験の出題範囲:

トピック出題範囲
トピック 1
  • Analytics: This section of the exam measures the skills of Security Analysts and covers the foundational techniques for building and refining queries. It focuses on creating searches from events, applying grouping and aggregation methods, and performing various lookup operations, including CMDB and nested queries to effectively analyze and correlate data.
トピック 2
  • Rules and subpatterns: This section of the exam measures the skills of SOC Engineers and focuses on the construction and implementation of analytics rules. It involves identifying the different components that make up a rule, utilizing advanced features like subpatterns and aggregation, and practically configuring these rules within the FortiSIEM platform to detect security events.
トピック 3
  • Incidents, notifications, and remediation: This section of the exam measures the skills of Incident Responders and encompasses the entire incident management lifecycle. This includes the skills required to manage and prioritize security incidents, configure policies for alert notifications, and set up automated remediation actions to contain and resolve threats.
トピック 4
  • Machine learning, UEBA, and ZTNA: This section of the exam measures the skills of Advanced Security Architects and covers the integration of modern security technologies. It involves performing configuration tasks for machine learning models, incorporating UEBA (User and Entity Behavior Analytics) data into rules and dashboards for enhanced threat detection, and understanding how to integrate ZTNA (Zero Trust Network Access) principles into security operations.

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Fortinet FCP - FortiSIEM 7.2 Analyst 認定 FCP_FSM_AN-7.2 試験問題 (Q35-Q40):

質問 # 35
Refer to the exhibit.

If you group the events by User, Source IP, and Count attributes, how many results will FortiSIEM display?

正解:E

解説:
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.


質問 # 36
Refer to the exhibit.

An analyst is troubleshooting the rule shown in the exhibit. It is not generating any incidents, but the filter parameters are generating events on the Analytics tab.
What is wrong with the rule conditions?

正解:B

解説:
The Group By attributes - Destination IP and User - cause the aggregation (COUNT(Source IP) >= 2) to apply within each unique combination of those groupings. This restricts the count calculation and can prevent the rule from triggering incidents, even if matching events exist in the Analytics tab.


質問 # 37
Where must you define and assign a custom python script as a remediation action?

正解:B

解説:
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.


質問 # 38
Refer to the exhibit.

An analyst is trying to generate an incident with a title that includes the Source IP, Destination IP, User, and Destination Host Name. They are unable to add a Destination Host Name as an incident attribute.
What must be changed to allow the analyst to select Destination Host Name as an attribute?

正解:B

解説:
For an attribute like Destination Host Name to be used in the incident title, it must first be included in the Triggered Attributes list. Only attributes listed there are available for substitution in the title template (e.g., $destIpAddr, $srcIpAddr).


質問 # 39
Refer to the exhibit.

What will happen when a device being analyzed by the machine learning configuration shown in the exhibit has a consistently high memory utilization?

正解:C

解説:
In the configuration shown, FortiSIEM uses Memory Util, Sent Bytes, and Received Bytes as input features to predict CPU Utilization via a regression model. If a device shows consistently high memory utilization, the model will incorporate that into its training data and update itself with a higher average value for memory utilization, influencing future CPU utilization predictions.


質問 # 40
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