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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Web Application and API Security with Bot Mitigation | 35% | - Web application security measures
|
| Topic 2: Compliance and Troubleshooting | 15% | - System and traffic troubleshooting
|
| Topic 3: Deployment and Configuration | 30% | - Deployment modes and basic administration
|
| Topic 4: Application Delivery and Additional Configuration | 20% | - Protection and integration
|
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NEW QUESTION # 57
Refer to the exhibit.
What does the exhibit show?
Answer: C
Explanation:
The exhibit is written in structured OpenAPI/YAML-style format. It includes fields such as info, version, title, servers, paths, HTTP method get, operationId, responses, content type application/json, and a schema definition. That is not HTML and it is not a live API response. It is also not CLI output from FortiWeb.
FortiWeb OpenAPI validation uses OpenAPI description files in YAML or JSON to define API structure, endpoints, parameters, and expected data types. FortiWeb then uses that uploaded schema as a baseline to validate API requests and block requests that do not conform. So the exhibit is best identified as an API schema file
NEW QUESTION # 58
A FortiWeb administrator wants to stop coordinated scraping traffic coming from several IP addresses, each making only a few requests so thresholds never trigger.
Which tactic should the administrator deploy to identify botnets using shared behavioral signals instead of volume?
Answer: C
Explanation:
The scenario describes distributed scraping where each individual IP stays below request-rate thresholds. A simple DoS threshold is weak here because the attacker avoids volume-based detection per source. Static blocklists are also ineffective because many botnet IPs may appear only once or rotate frequently. Blocking every non-allowlisted user agent would cause severe false positives and is easy for bots to evade by spoofing headers. FortiWeb bot mitigation is the correct control because it can evaluate behavior beyond source IP volume. Device fingerprinting, browser behavior, headers, JavaScript challenges, and client characteristics help correlate suspicious automation even when requests are spread across many addresses. Therefore, bot mitigation with behavioral/device fingerprinting is the strongest answer.
NEW QUESTION # 59
You are reviewing the FortiWeb integration with the Advanced Bot Protection (ABP) service.
Match each step in the ABP flow with its description.
Answer:
Explanation:
Explanation:
The ABP workflow begins when FortiWeb challenges the browser by injecting JavaScript used to collect behavior data. The client browser then executes that JavaScript and sends the observed behavior information to the ABP service. FortiWeb then uses the ABP service decision process to determine whether the browser behavior looks human or automated. If the ABP service identifies the activity as malicious or bot-like, FortiWeb blocks the request before it reaches the protected application. If the request is validated as coming from a real user, FortiWeb allows the traffic to continue to the back-end server. The order is important because FortiWeb cannot allow or block based on ABP until browser behavior has first been collected and evaluated.
NEW QUESTION # 60
An administrator is troubleshooting why FortiWeb is not decrypting HTTPS traffic for inspection in reverse proxy mode. What is the most likely missing configuration?
Answer: A
Explanation:
To decrypt and inspect HTTPS traffic in reverse proxy mode, FortiWeb must terminate SSL/TLS, which requires a local certificate and matching private key be bound to the virtual server. Without it, FortiWeb cannot offload and inspect encrypted traffic.
NEW QUESTION # 61
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.)
Answer: B,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 # 62
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