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Fortinet NSE5_FWB_AD-8.0 Exam Syllabus Topics:

SectionObjectives
Topic 1: Deployment and Configuration- Server objects, virtual servers, and policies
- Initial setup and system administration
- SSL inspection, SSL offloading, and high availability (HA)
- FortiWeb deployment modes and architecture
Topic 2: Application Delivery and Optimization- DoS protection configuration
- Load balancing and server pools
- Caching and compression
- Logging, monitoring, and FortiAI integration
Topic 3: Web Application and API Security with Bot Mitigation- OWASP Top 10 mitigation
- Bot detection and mitigation strategies
- Web application firewall policies and protection profiles
- API discovery and API protection
Topic 4: Compliance and Troubleshooting- Web vulnerability scanning and remediation
- Troubleshooting deployment and traffic flow issues
- Log analysis and reporting

>> NSE5_FWB_AD-8.0模擬試験問題集 <<

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Fortinet NSE 5 - FortiWeb 8.0 Administrator 認定 NSE5_FWB_AD-8.0 試験問題 (Q46-Q51):

質問 # 46
A third-party penetration test reveals that users can bypass login controls through a mobile API. Your current FortiWeb configuration includes zero trust network access (ZTNA) profiles and cookie security, but API protection and client management are not enabled. The security team asks you to recommend the most effective way to close this gap.
Which FortiWeb adjustment would best prevent future unauthorized API access?

正解:C

解説:
The issue is unauthorized access through a mobile API, so the control must enforce API-specific identity and access rules. FortiWeb API protection can validate API structure, methods, paths, and authorization requirements, while client management can help associate requests with legitimate clients or authenticated users. ZTNA profiles and cookie security can help with access and session protection, but they do not replace API-specific authorization controls. Switching reverse-proxy mode to bypass cookie controls makes no sense and could weaken protection. Replacing ZTNA with bot protection addresses a different problem: automation, not API authorization. Logging only records activity after the fact and does not prevent bypass. The correct action is to enable API protection and client management for mobile API traffic.


質問 # 47
Which URL should you rewrite to reduce security risk?

正解:B

解説:
The URL https://www.example.com/25.3.6/Browse/MediaData exposes internal application structure and what appears to be a version number. Revealing version information, framework paths, or internal directory names gives attackers useful reconnaissance data. Attackers can correlate exposed versions with known vulnerabilities and target the application more precisely. FortiWeb URL rewriting can reduce this risk by hiding or transforming sensitive internal URLs before users or scanners see them. The other URLs are normal- looking public paths or common application resources. A WordPress RSS feed may or may not be appropriate depending on business requirements, but it does not clearly expose internal versioned routing in the same way. The risky URL is the one containing 25.3.6/Browse/MediaData.


質問 # 48
Which URL should you rewrite to reduce security risk?

正解:B

解説:
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.


質問 # 49
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?

正解:D

解説:
FortiWeb machine learning uses layered detection rather than treating every unusual value as malicious. The HMM layer models normal parameter behavior and can flag a value as abnormal when it falls outside the learned distribution. However, abnormal does not automatically mean hostile. FortiWeb then uses additional ML classification logic, including SVM-based evaluation, to determine whether the anomaly resembles an actual attack or simply a legitimate unusual input. In this case, HMM noticed that the value was uncommon, but SVM classified it as normal, so FortiWeb allowed the request. That is expected behavior. Raising thresholds, disabling models, or assuming FortiWeb failed would misunderstand the two-stage ML decision process.


質問 # 50
Which statement correctly differentiates FortiWeb's "signature-based detection" from "machine learning-based detection"?

正解:C

解説:
Signature-based detection relies on a database of known attack patterns (updated via FortiGuard) to identify malicious requests, while machine learning-based detection builds a statistical model of normal application behavior and flags deviations, allowing it to catch novel or previously unseen attacks that lack a matching signature.


質問 # 51
......

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