Get Fortinet FCP_FWB_AD-7.4 Exam Practice Questions - Real and Updated

Answer : A, D

It does not affect denial-of-service (DoS) protection profile actions to rate limit traffic: Monitor mode allows FortiWeb to monitor traffic without impacting the protection profile actions, including rate limiting in the DoS protection profiles. Traffic will still be subjected to DoS protection actions like rate limiting, but FortiWeb will not block traffic unless a violation occurs.

It overrides all usual profile actions. FortiWeb accepts all requests and generates alert email or log messages only for violations: In monitor mode, FortiWeb will allow all traffic through and generate logs or alerts for any violations, but it will not take active actions like blocking requests or redirecting traffic. This allows you to observe the traffic patterns and potential threats without disrupting normal operations.

Answer : B, C

The first layer of the FortiWeb anomaly machine learning (ML) analysis mechanism focuses on analyzing traffic and creating a probability model for parameters and HTTP methods to detect potential anomalies. It also assesses traffic patterns over time to determine whether certain behavior is anomalous. These functions are key to understanding and classifying traffic before further analysis is done.

Answer : C

A self-signed certificate is useful when all the devices in your network can be configured to trust it. In this case, if your enterprise's computers trust the internal Active Directory or Certificate Authority (CA) server that signed the certificate, the self-signed certificate can be used internally for HTTPS connections without raising trust issues.

Answer : A

In SAML (Security Assertion Markup Language) deployments, the Identity Provider (IdP) is responsible for storing and managing user authentication credentials, such as usernames and passwords. The IdP authenticates the user and then issues a SAML assertion to the Service Provider (SP), which allows the user to access services without needing to re-enter credentials.

Answer : A, C, D

Model building: In this stage, you design and develop the ML model, which involves selecting appropriate algorithms and features to detect bot activity.

Model verification: This is where you test and evaluate the model's performance to ensure it can accurately detect bots without false positives or negatives.

Sample collecting: Gathering relevant data samples (e.g., bot and non-bot traffic) to train the machine learning model is crucial to ensure it can learn from various scenarios.