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Fortinet NSE6_FSM_AN-7.4 Exam Syllabus Topics:

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

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                                    Fortinet NSE 6 - FortiSIEM 7.4 Analyst Sample Questions (Q47-Q52):

                                    NEW QUESTION # 47
                                    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: B

                                    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 # 48
                                    Refer to the exhibit. Which event type attribute value will the FortiSIEM parser save for this event?

                                    Answer: A

                                    Explanation:
                                    FortiSIEM parsers map raw event attributes to normalized event type attributes. In this event, the parser identifies the sysUpTime value and stores it under the normalized FortiSIEM attribute name PH_DEV_MON_SYS_UPTIME.


                                    NEW QUESTION # 49
                                    In an automation policy, which two methods can you use for notifications when an incident is triggered? (Choose two.)

                                    Answer: A,D

                                    Explanation:
                                    Automation policies can notify users or external systems when an incident is triggered by sending email notifications or SNMP traps. These notification actions are configured in the automation policy action settings.


                                    NEW QUESTION # 50
                                    When configuring machine learning (ML), in which step can you modify how the model fits the training data set?

                                    Answer: B

                                    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 # 51
                                    You need to model for predicting a target based on other fields in the dataset and then trigger an anomaly if the value does not match the prediction.
                                    Which machine learning algorithm will build this type of model?

                                    Answer: B

                                    Explanation:
                                    The correct answer is C. Regression. Regression is the machine learning task used when a model predicts a target value based on other fields in a dataset. In FortiSIEM's machine learning workflow, regression configuration includes selecting Fields to use for Prediction and a Field to Predict. The User Guide explains that during regression training, the analyst chooses the fields used for prediction and the field being predicted, then trains the model with a Train factor. This matches the question exactly:
                                    the model predicts a target based on other fields, and the inference phase can identify anomalies when observed values differ significantly from predicted values. Classification is used to assign records to discrete classes or categories, not to predict a continuous target metric from other variables. Clustering groups similar observations without a predefined target field. Forecasting predicts future values over time, normally based on time-series behavior. Because the question specifically describes predicting a target field from other dataset fields and detecting mismatch from the prediction, the correct machine learning method is Regression.


                                    NEW QUESTION # 52
                                    ......

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