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| Section | Weight | Objectives |
|---|---|---|
| Analytics | 30% | - Performing CMDB and lookup table queries - Applying group by and data aggregation - Building queries from search results and events |
| Monitoring, Reporting and Integration | 15% | - Generating compliance and operational reports - Configuring dashboards and real-time monitoring - Integrating with security tools and ZTNA |
| Event Collection and Normalization | 20% | - Collecting logs and data from multiple sources - Normalizing, parsing, and standardizing event data |
| Incident Detection, Investigation and Response | 15% | - Applying incident response workflows and escalation - Using dashboards and tools for incident investigation |
| Event Correlation and Rule Management | 20% | - Managing alerts, tuning rules, reducing false positives - Creating and configuring correlation rules |
>> NSE6_FSM_AN-7.4 Praxisprüfung <<
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91. Frage
Refer to the exhibit.
An analyst wants to perform a KMeans machine learning (ML) job on this data. How many N clusters would be a good fit for the data? (Choose one answer)
Antwort: D
Begründung:
The best answer is A. Two . The exhibit shows an Analytics Search scatter plot with two visually distinct groups of data points: one isolated group around the lower-left area of the chart and another dense group near the upper-right area. For KMeans clustering, the analyst must provide the number of clusters based on the observed structure of the data. The FortiSIEM 7.4 User Guide describes KMeans as "an unsupervised clustering algorithm that groups data points into user specified K groups so that each data point belongs to one group." It also states that KMeans "tries to iteratively minimize intra-cluster distance and maximize inter- cluster distance" and notes that the user must "specify the number of clusters based on user's knowledge of data." The same FortiSIEM guide explains that during Clustering Local Mode training, the analyst chooses the algorithm and, "for KMeans choose the cluster size as a guess." In this exhibit, the natural guess is
2, because the scatter plot separates into two obvious groups. Fifty or 100 clusters would overfit the small number of visible groups, while one cluster would merge two clearly separate behaviors into a single cluster.
92. Frage
Refer to the exhibit.
The configuration for a machine learning (ML) dataset using anomaly detection is shown.
If data for this model is generated every hour, how long must the FortiSIEM device be up before it can produce a valid training set?
Antwort: C
Begründung:
The Windows parameter is set to 10, meaning FortiSIEM requires 10 data windows to build a valid training baseline. Since data is generated every hour, the device must collect 10 hours of data before producing a valid training set.
93. Frage
Refer to the exhibit.
A FortiSIEM device is receiving syslog events from a FortiGate firewall. The FortiSIEM analyst is trying to search the raw event logs for the last two hours that contain the keyword " udp " . However, they are getting no results from the search, which they know should be available. Based on the filter shown in the exhibit, why are there no search results?
Antwort: B
Begründung:
The operator is set to " = " , which performs an exact match on the entire raw event log, not a substring search. To find logs that contain the keyword " udp " , the analyst should use the CONTAIN operator instead.
This will return all logs where " udp " appears anywhere in the raw log message.
The correct answer is D because the analyst is trying to search for raw logs that contain the keyword udp, but the filter uses the equality operator. The FortiSIEM Study Guide explains keyword searches in terms of Raw Event Log CONTAIN logic. In the keyword phrase search section, the guide states that without quotes, FortiSIEM searches raw event logs by using conditions such as Raw Event Log CONTAIN TCP OR Raw Event Log CONTAIN connection . Another analytics example explains that searching for TCP or UDP events uses the raw event log containing the keyword tcp or udp and that the search returns case-insensitive results for TCP and UDP. This directly eliminates option C. The time range is already set to the last two hours, which is correct for historical raw log searching. The problem is the operator. To find a keyword anywhere inside a raw log message, the analyst should use CONTAIN , not =.
94. Frage
Refer to the exhibit.
A FortiSIEM analyst is investigating an issue by examining events to two destination IP addresses. However, the analyst is not getting any results from the search.
Based on the selected filter shown in the exhibit, why is the search returning no results?
Antwort: A
Begründung:
The correct answer is B because the analyst is searching for events to either of two destination IP addresses, but the filter uses the wrong Boolean relationship. The FortiSIEM Study Guide explains structured searches with multiple conditions and states that "when you use multiple conditions, you must specify the next logical operator between conditions." It gives a direct example: when searching for events from two specific devices, the first condition is one IP address, the second condition is another IP address, and "the next logical operator between the two conditions is an OR operator, because the search is for events from condition 1 OR condition
2." The same logic applies here. A single event cannot usually have Destination IP equal to 10.10.1.1 and Destination IP equal to 192.168.1.1 at the same time. Using AND requires both conditions to be true simultaneously, so no results are returned. The correct operator between the two Destination IP conditions is OR.
95. Frage
Refer to the exhibit. Why would the two entries shown in the exhibit be included in an incident action history?
Antwort: D
Begründung:
The action history shows that the system cleared the incident and then sent an email notification.
This occurs when the automation policy is configured to send an Email/SMS/Webhook notification to the target users when the incident is cleared by the system.
96. Frage
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Die Konkurrenz in unserer Gesellschaft wird immer heftiger. Unsere Zertpruefung ist noch bei vielen Prüfungskandidaten sehr beliebt, weil wir immer vom Standpunkt der Teilnehmer die Softwaren entwickeln. Z.B. die gut gekaufte Fortinet NSE6_FSM_AN-7.4 Prüfungssofteware wird von unserem professionellem Team entwickelt mit großer Menge Forschung der Fortinet NSE6_FSM_AN-7.4 Prüfung. Obwohl wir eine volle Rückerstattung für die Verlust des Tests versprechen, bestehen fast alle Kunde Fortinet NSE6_FSM_AN-7.4, die unsere Produkte benutzen. Was beweist die Vertrauenswürdigkeit und die Effizienz unserer Fortinet NSE6_FSM_AN-7.4 Prüfungsunterlagen.
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