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Fortinet NSE6_FSM_AN-7.4 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Event Collection and Normalization20%- Normalizing, parsing, and standardizing event data
- Collecting logs and data from multiple sources
Topic 2: Analytics30%- Performing CMDB and lookup table queries
- Applying group by and data aggregation
- Building queries from search results and events
Topic 3: Incident Detection, Investigation and Response15%- Applying incident response workflows and escalation
- Using dashboards and tools for incident investigation
Topic 4: Monitoring, Reporting and Integration15%- Generating compliance and operational reports
- Configuring dashboards and real-time monitoring
- Integrating with security tools and ZTNA
Topic 5: Event Correlation and Rule Management20%- Managing alerts, tuning rules, reducing false positives
- Creating and configuring correlation rules

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Fortinet NSE 6 - FortiSIEM 7.4 Analyst Sample Questions (Q38-Q43):

NEW QUESTION # 38
Refer to the exhibit. If you group these events by the Reporting IP, Event Type, and User attributes, how many results will FortiSIEM display?

Answer: B

Explanation:
All events share the same Reporting IP and Event Type. Grouping by User produces three distinct groups: Mike, Bob, and Alice. Therefore, FortiSIEM displays three results.


NEW QUESTION # 39
When configuring machine learning (ML), in which step can you modify how the model fits the training data set?

Answer: D

Explanation:
The correct answer is B. Train. In FortiSIEM machine learning, the Train step is where the model is built from the prepared dataset and where model-fitting behavior can be adjusted. The FortiSIEM 7.4 User Guide explains that after preparing data, the analyst goes to Analytics > Machine Learning > Train, selects the machine learning task, chooses the algorithm, selects the prediction/target fields when required, and chooses the Train factor, which determines how much data is used for training versus testing. The guide states that the Train factor should be greater than 70%, meaning 70% of the data is used for training and 30% for testing. It also explains that model quality metrics show how accurately the algorithm predicts the field. For regression, lower MAE means a better fit, and R2 shows how well predictions approximate real data points. Most importantly, the guide states: "If you want to change the algorithm parameters and re-train, then click Tune & Train, change the parameters and click Save
& Train." This confirms that modifying how the model fits the training dataset is done in the Train step, not Prepare Data, Statistics, or Design.


NEW QUESTION # 40
When FortiSIEM is configured to apply ZTNA tags, what is the order of events when an analyst wants to automatically block a ZTNA tagged host?

Answer: B

Explanation:
The correct sequence for ZTNA tag enforcement is:
1. FortiEMS tags the host based on endpoint posture or detected condition.
2. FortiSIEM receives the tag information from FortiEMS.
3. FortiSIEM applies its own tag (for example, "blocked") to the host based on automation or incident rules.
4. FortiGate enforces the ZTNA tag policy, blocking or restricting access according to configured rules.
Thus, the event flow is FortiEMS tags host โ†’ FortiSIEM receives tag info โ†’ FortiSIEM tags host
โ†’ FortiGate enforces tags.


NEW QUESTION # 41
Refer to the exhibit.

As shown in the exhibit, why are some of the fields highlighted in red?

Answer: D

Explanation:
The fields are highlighted in red because unique values such as Event Receive Time and Raw Event Log cannot be used in group-by operations. Grouping requires aggregatable or consistent values across events, while these fields are unique to each event, making them incompatible for grouping.
The correct answer is A because the highlighted fields are not valid for that grouped/aggregated display configuration. The FortiSIEM 7.4 User Guide notes that some event attributes, functions, and queries are not supported in specific analytics result-filter and display contexts. It lists date fields, including examples such as Event Receive Time , and also lists Raw Event Log and Binary Raw Event Log among unsupported fields for that context. The reason is practical: grouping requires stable values that can combine multiple events into meaningful grouped rows. Attributes such as Event Receive Time and Raw Event Log are highly specific to individual events. If every event has its own receive timestamp or unique raw log content, grouping by those fields defeats aggregation and can create one row per event rather than meaningful grouped output. COUNT (Matched Events) itself is a valid aggregate expression when used correctly. Event Receive Time is available in logs, but it is not appropriate as a grouped field in the configuration shown. Therefore, the red highlighting indicates invalid grouped fields caused by unique/non-groupable values.


NEW QUESTION # 42
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?

Answer: D

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 # 43
......

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