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Fortinet NSE6_FSM_AN-7.4 Exam Syllabus Topics:

SectionObjectives
Topic 1: Machine Learning, UEBA, and ZTNA- Advanced analytics integration
  • 1. Integrate UEBA data into rules and dashboards
    • 2. Configure ML configuration tasks
      • 3. Describe ZTNA integration in FortiSIEM operations
        Topic 2: FortiEDR Security Settings and Policies- Security configuration
        • 1. Configure security policies
          • 2. Explain Fortinet Cloud Service (FCS)
            • 3. Configure communication control policy
              • 4. Configure playbooks
                Topic 3: Analytics- Query and event analysis
                • 1. Apply group by and data aggregation on search results
                  • 2. Perform CMDB and lookup table queries
                    • 3. Perform nested query lookups
                      • 4. Build queries from search results and events
                        Topic 4: Incidents, Notifications, and Remediation- Incident management
                        • 1. Manage and tune incidents
                          • 2. Configure remediation options
                            • 3. Configure notification policies
                              Topic 5: Rules and Subpatterns- Analytics rules configuration
                              • 1. Configure FortiSIEM analytics rules
                                • 2. Use rule subpatterns, aggregation, and group by
                                  • 3. Identify rule components

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                                    Fortinet NSE 6 - FortiSIEM 7.4 Analyst Sample Questions (Q92-Q97):

                                    NEW QUESTION # 92
                                    Refer to the exhibit. If you group the events by Reporting IP, Event Type, and User attributes, how many results will FortiSIEM display?

                                    Answer: C

                                    Explanation:
                                    When grouped by Reporting IP, Event Type, and User, FortiSIEM consolidates rows sharing the same values for these attributes.
                                    Reporting IP: all are 10.1.1.1
                                    Event Type: all are Logon
                                    Users: Mike, Bob, and Alice
                                    Thus, FortiSIEM will display three results, one for each user.


                                    NEW QUESTION # 93
                                    Refer to the exhibit.

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

                                    Answer: B

                                    Explanation:
                                    Grouping by User and Count combines only rows that have the same values for both attributes.
                                    The two Alice rows with a count of 2 are grouped into one result, while the other user-and-count combinations remain unique, so FortiSIEM displays five unique results.


                                    NEW QUESTION # 94
                                    Refer to the exhibits.



                                    Three events are collected over 10 minutes from two servers: Server A and Server B.
                                    Based on the settings for the rule subpattern and a 10-minute condition window, how many incidents will the servers generate?

                                    Answer: D

                                    Explanation:
                                    The rule groups events by host and evaluates them within the 10-minute window. Server A has three matching CPU utilization events, and its average CPU utilization is greater than the configured critical threshold of 90, so it generates one incident. Server B does not exceed the threshold, so it does not generate an incident.


                                    NEW QUESTION # 95
                                    Refer to the exhibit. You want to use a machine learning (ML) model to train data with the following characteristics shown in the exhibit. Which ML model is the best fit to match the data in the exhibit?

                                    Answer: A

                                    Explanation:
                                    The data points form several distinct groups, so a clustering model is the best fit for identifying natural groupings within the dataset.


                                    NEW QUESTION # 96
                                    Refer to the exhibit.

                                    If a rule containing the automation policy shown in the exhibit triggers, what will happen?

                                    Answer: B

                                    Explanation:
                                    The automation policy is configured to run a remediation script named "Fortinet FortiOS - Block Source IP FortiOS via API". It specifies enforcement on two FortiGate devices: FortiGate508 and FortiGate90D. Therefore, associated source IP addresses will be blocked on those two FortiGate firewalls only.


                                    NEW QUESTION # 97
                                    ......

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