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Fortinet NSE6_FSM_AN-7.4 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Event Collection and Normalization	20%	- Normalizing, parsing, and standardizing event data - Collecting logs and data from multiple sources
Topic 2: Monitoring, Reporting and Integration	15%	- Generating compliance and operational reports - Configuring dashboards and real-time monitoring - Integrating with security tools and ZTNA
Topic 3: Event Correlation and Rule Management	20%	- Managing alerts, tuning rules, reducing false positives - Creating and configuring correlation rules
Topic 4: Incident Detection, Investigation and Response	15%	- Applying incident response workflows and escalation - Using dashboards and tools for incident investigation
Topic 5: Analytics	30%	- Building queries from search results and events - Applying group by and data aggregation - Performing CMDB and lookup table queries

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Fortinet NSE 6 - FortiSIEM 7.4 Analyst Sample Questions (Q81-Q86):

NEW QUESTION # 81
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:
With Source IP and Destination IP as grouping attributes, and COUNT(Matched Events) included, FortiSIEM will display a list of unique source-destination IP pairs along with the number of allowed connections between each pair. This configuration summarizes connection activity by unique communication paths.

NEW QUESTION # 82
You need a model for predicting a target field based on other fields in a dataset and then trigger an anomaly if the value does not match the prediction. Which machine learning algorithm will build this type of model?

A. Clustering
B. Regression
C. Regression
D. Forecasting

Answer: C

Explanation:
A Regression algorithm is used when predicting a continuous or numeric target field based on other features in the dataset. In FortiSIEM, regression-based machine learning models establish expected values, and an anomaly is triggered when the actual observed value significantly deviates from the regression prediction.

NEW QUESTION # 83
Refer to the exhibit. Why is this search not producing any results?

A. You must set the Time Range to Real-time to identify login failures.
B. You did not use the configuration management database (CMDB) group search properly.
C. There is a nested query attribute type mismatch.
D. You cannot reference both the User and Event Type attributes in the same analytics search.
E. You must set the Operator to = for both queries.

Answer: C

Explanation:
The query contains a nested query attribute type mismatch because the User attribute is being compared against a Device IP group value. The attribute type and referenced group type must match for the search to return results.

NEW QUESTION # 84
Refer to the exhibit.

Which statement about the time range settings defined in the nested query is accurate? (Choose one answer)

A. FortiSIEM will search the last 10 minutes of events for a source IP address that is not in the Approved Devices report from the last 30 days.
B. FortiSIEM will search the last 30 days of events for a source IP address that is not in the Approved Devices report.
C. FortiSIEM will list source IP addresses found in the last 10 minutes of events from each day in the Approved Devices report from the last 30 days.
D. FortiSIEM will search in real time using 10-minute blocks for a source IP address that is not in the Approved Devices report from the last 30 days.

Answer: A

Explanation:
The correct answer is D. The exhibit shows an outer event query using the Event Attribute filter Source IP NOT IN Device IP: Approved Devices. The outer query time range is set to Relative - Last 10 Minutes, so FortiSIEM searches only the event data from the last 10 minutes. The exhibit also shows a separate Nested Time Range set to Relative - Last 30 Days. In FortiSIEM nested searches, the nested time range applies to the inner report/subquery, not to the outer event search. The FortiSIEM 7.4 User Guide states that nested query functionality lets one query refer to results from another query, and for outer event / inner event nested searches, it instructs the user to "choose the time range for outer query" and separately "choose Nested Time Range for the inner query." It also states that when an existing query is used as an inner query, "time range would be set separately" in the outer query configuration. Therefore, FortiSIEM searches the last 10 minutes of outer events and compares their Source IP values against the Device IP values returned by the Approved Devices report using the last
30 days nested time range.

NEW QUESTION # 85
Refer to the exhibit.

The configuration shown in the exhibit is incorrect.
What must you change to allow this configuration to be successfully applied to FortiSIEM?

A. Run Mode must be set to ML.
B. The selection in Fields to use for Prediction and Field to Predict must match.
C. The Train factor must be 70% or greater.
D. Only one AVG type field must be selected under Fields to use for Prediction.

Answer: A

Explanation:
The Run Mode is set to Local, which is not valid for training machine learning models in FortiSIEM. To apply this configuration correctly, the Run Mode must be set to ML, which enables proper model training and prediction using selected fields.

NEW QUESTION # 86
......

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