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Fortinet NSE5_FWB_AD-8.0 Exam Syllabus Topics:

SectionObjectives
Deployment and Configuration- Server objects and policy configuration
- FortiWeb deployment and basic administration
- SSL inspection, offloading, and high availability (HA)
Compliance and Troubleshooting- Web vulnerability scanning implementation
- Troubleshoot deployment issues
- System and configuration troubleshooting
Web Application and API Security- Botnet mitigation and protection features
- API discovery and protection
- Web application security configuration
Application Delivery and Additional Configuration- FortiAI integration
- Denial of service (DoS) mitigation
- Logging and reporting configuration
- Application delivery optimization

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Fortinet NSE 5 - FortiWeb 8.0 Administrator Sample Questions (Q43-Q48):

NEW QUESTION # 43
Refer to the exhibit.


A FortiWeb administrator tests a new form input value after training the machine learning (ML) anomaly detection system.
The hidden Markov model (HMM) flags the input as abnormal, while the support vector machine (SVM) model classifies it as normal. FortiWeb allows the request.
What does this result indicate about the FortiWeb ML anomaly detection behavior?

Answer: A

Explanation:
FortiWeb ML anomaly detection uses multiple models to evaluate whether input is truly malicious or simply unusual. In this case, the input appears abnormal to the HMM pattern model, but the SVM classification considers it acceptable, so FortiWeb treats it as normal and allows the request.


NEW QUESTION # 44
How should a FortiWeb administrator configure behavior-based bot detection to identify traffic from nonhuman users?

Answer: C

Explanation:
FortiWeb bot mitigation is designed to distinguish automated clients from real human users by evaluating request behavior and browser interaction signals. Request-rate limits help detect automation patterns such as excessive requests over a short period, while mouse movement tracking is a behavioral or biometric-style control that helps confirm whether a browser session is being operated by a human. Blocking all unknown devices is too aggressive and would create major false positives. Disabling JavaScript for anonymous users would actually weaken behavior collection because FortiWeb uses JavaScript-based techniques in some bot workflows. Login-failure IP blocklists help against credential attacks, but they do not broadly identify nonhuman users. Therefore, request limits plus mouse movement tracking is the best answer.


NEW QUESTION # 45
A user from group B sends 150 requests in one minute to this endpoint:

Group B users are allowed access to only /api/v1/reports and are limited to 50 requests per minute.
What should the FortiWeb administrator configure to stop this abuse?

Answer: A

Explanation:
Group-based rate limiting enforces the allowed request threshold for group B users, limiting them to 50 requests per minute and preventing excessive API use even when the request includes a valid API key.


NEW QUESTION # 46
Which URL should you rewrite to reduce security risk?

Answer: A

Explanation:
The URL exposes a specific application version in the path. Rewriting it reduces information disclosure, making it harder for attackers to identify the application version and target known vulnerabilities.


NEW QUESTION # 47
FortiWeb is blocking groups of users behind your load balancer. In the logs, all users show the same source IP address. Which action should you take to restore proper client identification?

Answer: A

Explanation:
When traffic passes through a load balancer, FortiWeb may see only the load balancer IP as the source. Inserting the original client IP address in an HTTP header, such as X-Forwarded-For, allows FortiWeb to identify individual clients correctly and avoid applying IP-based blocking to an entire group of users.


NEW QUESTION # 48
......

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