Free Fortinet FCP_FWB_AD-7.4 Practice Exam with Questions & Answers

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.

In the diagram, FortiWeb is positioned between the client and the server, handling encrypted HTTPS traffic from the client and sending unencrypted HTTP traffic to the server. This indicates that FortiWeb is performing SSL offloading, which means it is decrypting the HTTPS traffic from the client, inspecting it, and then re-encrypting the traffic before forwarding it to the server.

The web application is unable to accept any more connections because of network socket exhaustion: A Denial of Service (DoS) attack often floods the web server with an overwhelming number of requests, leading to network socket exhaustion. This can prevent the server from accepting new legitimate connections, effectively disrupting service.

The web application server is unable to accept new client sessions due to memory exhaustion: DoS attacks can consume a significant amount of server memory, causing memory exhaustion. This results in the web application being unable to accept new client sessions or handle requests properly.

The easiest way to allow a search engine indexer (such as Googlebot or Bingbot) to index your website on a FortiWeb is to add the indexer's IP address to the trusted IP list. This ensures that traffic from trusted indexers is allowed through without being blocked or interfered with by FortiWeb's security features like bot protection.

Making a secondary HTTPS connection to a server where FortiWeb acts as a client: When FortiWeb needs to connect to an external server via HTTPS (acting as a client), it may use its own certificates for that connection.

An administrator session connecting to the GUI using HTTPS: FortiWeb uses its own certificates to secure the HTTPS connection between the administrator and the FortiWeb GUI. This ensures secure access for management purposes.

When FortiWeb is deployed upstream of a device performing source network address translation (SNAT) or load balancing, the original client IP address may be lost. To preserve the original client IP address, you must enable and configure the Preserve Client IP setting on FortiWeb. This allows FortiWeb to retain and pass the client's original IP address to the backend servers for accurate logging and processing.

The best way to limit brute force login attacks on FortiWeb is to configure a brute force login custom policy. FortiWeb provides the ability to detect and mitigate brute force login attempts by automatically limiting the number of failed login attempts within a specific time period. This approach allows you to block or rate limit suspicious IP addresses while still allowing legitimate users access, based on your configuration.

In the provided configuration, the signature exception has been set for the URL http://my.blog.org/user1V. This means that any request to this specific URL will bypass the signature ID 01000001, which is designed to block cross-site scripting (XSS) attacks using the mousedown event. As the request comes from the URL http://my.blog.org/userl1/blog.php, which does not match the exception rule for http://my.blog.org/user1V, the attack will be allowed through.

Therefore, the connection will be allowed because the exception rule bypasses protection for the specified URL.

Virtual proxy: In virtual proxy mode, FortiWeb acts as an intermediary between clients and the server, and it can modify HTTP packets. It performs various security checks, such as inspecting and filtering HTTP traffic before forwarding it to the web server.

Reverse proxy: In reverse proxy mode, FortiWeb sits between the client and the server, handling incoming requests from clients, modifying or inspecting HTTP packets as needed, and forwarding them to the backend servers.

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.