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Fortinet NSE5_FWB_AD-8.0 Exam Syllabus Topics:

Section	Weight	Objectives
Deployment and Configuration	30%	- Deployment modes and basic administration 1. Initial setup and system management 2. Reverse Proxy, Transparent Bridge, Offline Protection - SSL configuration and High Availability 1. SSL inspection, offloading, and certificate management 2. Active-Passive and Active-Active HA deployment - Server objects and security policies 1. Server pool, virtual server configuration 2. Policy creation and rule management
Compliance and Troubleshooting	15%	- System and traffic troubleshooting 1. Configuration and connectivity issues 2. Log analysis and error diagnosis - Compliance and audit 1. Vulnerability scanning and reporting 2. Regulatory compliance standards
Application Delivery and Additional Configuration	20%	- Performance and delivery optimization 1. Content routing, URL rewriting, and caching 2. Compression and acceleration - Protection and integration 1. Logging, monitoring, and FortiAI integration 2. DoS and DDoS protection
Web Application and API Security with Bot Mitigation	35%	- API security 1. API discovery and endpoint mapping 2. API protection and access control - Web application security measures 1. Attack signature and threat protection 2. Security profiles and protection rules - Bot mitigation 1. CAPTCHA, reputation, and behavioral analysis 2. Bot detection and classification

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Fortinet NSE 5 - FortiWeb 8.0 Administrator Sample Questions (Q56-Q61):

NEW QUESTION # 56
Refer to the exhibit.

You are a FortiWeb administrator reviewing the biometrics-based detection rule shown in the exhibit. Your goal is to configure a rule that detects bots that avoid typical human interactions like using a mouse or clicking. You also want to log the detection event and apply a high-severity alert.
Based on the current configuration, which settings should you change to meet this goal?

A. Select Mouse Movement and Click , change the action to Alert , and set the severity to High .
B. Do not select any client events to monitor, enable Bot Trait Checking , keep the current severity, and keep the action as Deny (no log) .
C. Select Screen Touch and Page Focus , set the severity to Low , and keep action as Deny (no log) .
D. Select Keyboard and Scroll , change the action to Alert , and set the severity to High .

Answer: A

Explanation:
The goal is to detect bots that avoid normal human interaction, specifically mouse use and clicking, while logging the event and treating it as high severity. In FortiWeb biometrics-based bot detection, monitored client events such as mouse movement, click, keyboard, screen touch, page focus, and scroll help FortiWeb distinguish human behavior from automated behavior. Since the question specifically mentions mouse and clicking, the correct monitored events are Mouse Movement and Click . The current action is Deny (no log) , which would not meet the logging requirement. Changing the action to Alert logs the event instead of silently denying it, and setting severity to High aligns with the requirement for a high-severity alert.

NEW QUESTION # 57
An administrator is troubleshooting why FortiWeb is not decrypting HTTPS traffic for inspection in reverse proxy mode. What is the most likely missing configuration?

A. A static route to the internet
B. An HA heartbeat interface
C. A local certificate and private key bound to the virtual server for SSL offloading
D. A DNS resolver setting

Answer: C

NEW QUESTION # 58
You are reviewing a report from your FortiWeb logs and notice a JavaScript payload like < script > document.
cookie < /script > is submitted through a product review form. The page doesn't filter the script, and when users view the review, their session cookies are exposed.
Why is this attack dangerous?

A. It leaks back-end database information.
B. It executes code in the victim's browser.
C. It bypasses login pages.
D. It forces a victim to click malicious links.

Answer: B

Explanation:
This is a stored cross-site scripting attack. The attacker submits JavaScript into a product review form, and the application stores and displays it later to other users. When victims view the review, their browsers execute the attacker's script as if it came from the trusted site. That is dangerous because the script can access browser- side data available to the page, such as cookies, tokens, page content, or session-related information depending on browser and cookie security settings. It does not directly leak the back-end database; that would be more aligned with SQL injection. It also does not inherently bypass login pages or require the victim to click a link. The core risk is malicious code execution in the victim's browser.

NEW QUESTION # 59
Which action must a FortiWeb administrator take to enable FortiAppSec Cloud Advanced Bot Protection (ABP) service in a cloud deployment?

A. Set rate-limiting rules for every user session.
B. Deploy an internal intrusion detection system (IDS) to monitor incoming requests.
C. Use browser fingerprinting to reject headless browsers.
D. Activate Advanced Bot Protection in FortiWeb and bind it to a FortiAppSec Cloud Application ID.

Answer: D

Explanation:
FortiAppSec Cloud Advanced Bot Protection is enabled by activating the ABP feature in FortiWeb and associating it with the FortiAppSec Cloud Application ID. This links the FortiWeb deployment to the cloud-based bot protection service so it can analyze and enforce advanced bot detection for the protected application.

NEW QUESTION # 60
A FortiWeb administrator wants to create a machine learning (ML)-based bot detection system.
Which three actions must the administrator take to build and activate this ML model? (Choose three.)

A. Run the model in the live environment.
B. Verify the model manually on test data only.
C. Apply Bayesian analysis to the model output.
D. Collect traffic samples for training.
E. Build the detection model using collected data.

Answer: A,D,E

Explanation:
FortiWeb machine learning protection depends on observed application traffic. The administrator must first collect traffic samples so FortiWeb can learn normal behavior and create a useful baseline. After sample collection, FortiWeb uses the collected data to build the detection model. Once the model is built, it must be enabled or run in the live environment so FortiWeb can evaluate production requests and detect abnormal or bot-like behavior. Manual verification on test data only is not enough to activate the model for real traffic.
Bayesian analysis is not the FortiWeb configuration step shown for this process; the platform handles model logic internally. The practical workflow is collection, model building, and live enforcement or detection.

NEW QUESTION # 61
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