NSE6_FSM_AN-7.4 Testking Learning Materials, Exam NSE6_FSM_AN-7.4 Question

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Fortinet NSE6_FSM_AN-7.4 Exam Syllabus Topics:

Section	Objectives
Incidents, Notifications, and Remediation	- Incident management 1. Manage and tune incidents 2. Configure notification policies 3. Configure remediation options
FortiEDR Security Settings and Policies	- Security configuration 1. Configure playbooks 2. Explain Fortinet Cloud Service (FCS) 3. Configure security policies 4. Configure communication control policy
Analytics	- Query and event analysis 1. Apply group by and data aggregation on search results 2. Build queries from search results and events 3. Perform nested query lookups 4. Perform CMDB and lookup table queries
Machine Learning, UEBA, and ZTNA	- Advanced analytics integration 1. Describe ZTNA integration in FortiSIEM operations 2. Configure ML configuration tasks 3. Integrate UEBA data into rules and dashboards
Rules and Subpatterns	- Analytics rules configuration 1. Use rule subpatterns, aggregation, and group by 2. Configure FortiSIEM analytics rules 3. Identify rule components

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Fortinet NSE 6 - FortiSIEM 7.4 Analyst Sample Questions (Q58-Q63):

NEW QUESTION # 58
An analyst wants to create a rule from a newly created analytics search. What is the quickest method?

A. Create a new rule under Resources > Rules and fill in the search details.
B. On the upper menu bar on any tab, click the pencil icon.
C. On the Analytics tab, click Actions > Create Rule.
D. On the Analytics tab, click the New button next to the Filter By box.

Answer: C

Explanation:
The quickest way to create a rule from an existing analytics search in FortiSIEM is to go to the Analytics tab and select Actions > Create Rule. This automatically converts the current search filters and parameters into a correlation rule template, saving time compared to manually re- entering all the search criteria.

NEW QUESTION # 59
Refer to the exhibit.

What will FortiSIEM display if you apply the Group By and Display Fields configuration to a list of allowed firewall connections?

A. A list of connections ordered by the number of unique connections started by each source IP address
B. A list of connections ordered by destination IP address hit count
C. A list of connections between unique source and destination IP addresses
D. A running count of connections, regardless of source or destination

Answer: C

Explanation:
The correct answer is B because the configuration groups results by Source IP and Destination IP , while using COUNT(Matched Events) as a display/aggregate value. FortiSIEM's grouping logic combines events only when the selected Group By attributes match. The Study Guide explains that Group By attributes determine how matching events are placed into rows, and that when multiple events share the same grouped values, "they are grouped together in one row." The count column then tracks the number of events represented by that row. In the exhibit, Source IP and Destination IP are the grouping fields, so FortiSIEM displays each unique connection pair once. The count shows how many matching allowed firewall connection events were seen for each pair. Option A is not correct because the exhibit does not show sorting by destination IP hit count. Option C ignores the source and destination grouping. Option D would require grouping by source IP alone or by distinct destination counts per source, which is not the shown configuration.

NEW QUESTION # 60
Refer to the exhibit. Why are some of the fields highlighted in red?

A. No Raw Event Log attribute information is available.
B. Multiple unique values cannot be grouped.
C. The attribute COUNT(Matched Events) is an invalid expression.
D. The Event Receive Time attribute is a valid choice to sort by.

Answer: B

Explanation:
The highlighted fields represent attributes that contain multiple unique values and therefore cannot be used together in a grouped aggregation display in the current configuration.

NEW QUESTION # 61
Refer to the exhibit.

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

A. FortiSIEM will update the model with a higher memory utilization average value.
B. FortiSIEM will trigger an incident for high memory utilization.
C. FortiSIEM will lower the CPU utilization trigger requirement for CPU utilization.
D. FortiSIEM will update the regression tables for memory utilization, and average sent and received bytes.

Answer: A

Explanation:
The exhibit shows a machine learning Regression configuration. In FortiSIEM regression, selected numerical fields are used as predictors, and another field is selected as the value to predict. The FortiSIEM 7.4 User Guide's Machine Learning section lists Regression separately and describes the training workflow, including selecting fields used by the algorithm. The exhibit shows Memory Utilization, Sent Bytes, and Received Bytes selected under Fields to use for Prediction, while CPU Utilization is selected under Field to Predict. This means Memory Utilization is an input variable used by the model. If memory utilization is consistently high during training or retraining, that higher value becomes part of the learned model pattern. FortiSIEM does not automatically trigger a high-memory incident merely because a regression input value is high; an incident would require inference behavior and an anomaly action or rule configuration. Option C is also wrong because FortiSIEM does not dynamically lower a CPU trigger threshold just because memory is high. The correct behavior is model update based on the higher observed input value.

NEW QUESTION # 62
Refer to the exhibit. What will happen when a device being analyzed by the machine learning (ML) configuration shown in the exhibit has a consistently high memory use?

A. FortiSIEM will lower the CPU use trigger requirement for CPU use.
B. FortiSIEM will trigger an incident for high memory use.
C. FortiSIEM will update the model with a higher memory use average value.
D. FortiSIEM will update the regression tables for memory use, and average sent and received bytes.

Answer: C

Explanation:
The ML regression model uses memory utilization, sent bytes, and received bytes as prediction inputs for CPU utilization. If memory usage remains consistently high, FortiSIEM adapts the learned baseline and updates the model with a higher average memory utilization value over time.

NEW QUESTION # 63
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