NSE5_FWB_AD-8.0 PDF Question - Authentic NSE5_FWB_AD-8.0 Exam Questions

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Fortinet NSE5_FWB_AD-8.0 Exam Syllabus Topics:

Section	Objectives
Application Delivery and Optimization	- Caching and compression - Load balancing and server pools - Logging, monitoring, and FortiAI integration - DoS protection configuration
Compliance and Troubleshooting	- Web vulnerability scanning and remediation - Troubleshooting deployment and traffic flow issues - Log analysis and reporting
Deployment and Configuration	- Server objects, virtual servers, and policies - Initial setup and system administration - FortiWeb deployment modes and architecture - SSL inspection, SSL offloading, and high availability (HA)
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

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NSE5_FWB_AD-8.0 PDF Question and Fortinet Authentic NSE5_FWB_AD-8.0 Exam Questions: Fortinet NSE 5 - FortiWeb 8.0 Administrator Pass for Sure

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Fortinet NSE 5 - FortiWeb 8.0 Administrator Sample Questions (Q31-Q36):

NEW QUESTION # 31
Your security team is reviewing tools for artificial intelligence (AI) integration and wants to ensure no real user or IP data is exposed to third-party services. How does FortiAI meet this requirement better than traditional external AI tools?

A. FortiAI blocks all network access during queries.
B. The large language model (LLM) receives IP addresses from only the internal DNS.
C. It encrypts data using a cloud-based key.
D. Only masked prompts go to the LLM; unmasking happens locally.

Answer: D

Explanation:
FortiAI protects sensitive information by masking data before sending prompts to the language model. The original values are restored locally, so real user details, IP addresses, and other sensitive data are not exposed to the external LLM service.

NEW QUESTION # 32
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?

A. FortiWeb failed to detect an attack and should have blocked the request.
B. FortiWeb is correctly allowing an unusual but non-malicious input based on combined HMM and SVM evaluation.
C. One of the ML models should be disabled to avoid inconsistent results.
D. The anomaly detection thresholds are too low and must be increased.

Answer: B

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 # 33
Which certificate deployment method allows FortiWeb to present different SSL certificates depending on the hostname requested by the client during the TLS handshake?

A. Certificate intermediate group
B. Server Name Indication (SNI)
C. Client certificate verification
D. Personal certificate revocation

Answer: B

Explanation:
SNI allows the TLS client to indicate the hostname it is trying to connect to during the handshake, enabling FortiWeb to select and present the correct certificate for that hostname when multiple websites share a single virtual server IP.

NEW QUESTION # 34
Which statement correctly differentiates FortiWeb's "signature-based detection" from "machine learning-based detection"?

A. Signature-based detection requires no updates, while machine learning must be updated daily via FortiGuard.
B. Machine learning only works in offline protection mode, while signatures only work in reverse proxy mode.
C. Signature-based detection is exclusively used for SSL, while machine learning is exclusively used for HTTP.
D. Signature-based detection matches traffic against known attack patterns, while machine learning detects anomalies by comparing traffic to a learned baseline of normal behavior.

Answer: D

Explanation:
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.

NEW QUESTION # 35
A FortiWeb administrator sees the following request:
GET /api/v1/data HTTP/1.1
Host: example.com
Authorization: ApiKey abc123def456
The API key belongs to a user in group B who is authorized to access only /api/v1/reports.
What should the administrator do to prevent this unauthorized access?

A. Block /api/v1/data for all user groups to avoid policy confusion.
B. Move the user to group A so they can access both endpoints.
C. Allow all valid API keys to access any API endpoint.
D. Restrict access to /api/v1/data using user group-based access control.

Answer: D

Explanation:
The problem is not that the API key is invalid; the key belongs to a real user. The issue is authorization scope:
group B is allowed only to access /api/v1/reports, but the request targets /api/v1/data. The correct FortiWeb control is API gateway rule enforcement using API key verification and API user grouping. FortiWeb can restrict API access by user group, sub-URL, API key verification, and configured violation actions. Blocking the endpoint for every group is too broad, moving the user to another group grants unnecessary privilege, and allowing all API keys to access all endpoints destroys endpoint-level authorization. The correct fix is group- based access control on /api/v1/data.

NEW QUESTION # 36
......

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