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NEW QUESTION # 78
What comparison is true about a CloudHub Dedicated Load Balancer (DLB) vs. the CloudHub Shared Load Balancer (SLB)?
Answer: A
Explanation:
* Shared load balancers don't allow you to configure custom SSL certificates or proxy rules
* Dedicated Load Balancer are optional but you need to purchase them additionally if needed.
* TLS is a cryptographic protocol that provides communications security for your Mule app. TLS offers many different ways of exchanging keys for authentication, encrypting data, and guaranteeing message integrity.
* The CloudHub Shared Load Balancer terminates TLS connections and uses its own server-side certificate.
* Only a DLB allows the configuration of a custom TLS server certificate
* DLB enables you to define SSL configurations to provide custom certificates and optionally enforce two- way SSL client authentication.
* To use a DLB in your environment, you must first create an Anypoint VPC. Because you can associate multiple environments with the same Anypoint VPC, you can use the same dedicated load balancer for your different environments.
* MuleSoft Reference: https://docs.mulesoft.com/runtime-manager/dedicated-load-balancer-tutorial Additional Info on SLB Vs DLB:
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NEW QUESTION # 79
A company is using Mulesoft to develop API's and deploy them to Cloudhub and on premises targets.
Recently it has decided to enable Runtime Fabric deployment option as well and infrastructure is set up for this option.
What can be used to deploy Runtime Fabric?
Answer: D
Explanation:
When deploying to Runtime Fabric, there are several tools available within the Anypoint Platform ecosystem that facilitate the deployment process. These include:
* AnypointCLI: This command-line interface tool provides comprehensive capabilities for deploying applications to Runtime Fabric. Using AnypointCLI, you can script and automate deployments, which is useful for integrating into CI/CD pipelines.
* Anypoint Platform REST APIs: These APIs allow for programmatic deployment and management of applications on Runtime Fabric. They provide flexibility for integrating deployment processes with other systems and tools.
* Mule Maven Plugin: This plugin can be used within a Maven project to package and deploy Mule applications to Runtime Fabric as part of a build process.
However, directly uploading a JAR file from the Runtime Manager is not a supported method for deploying to Runtime Fabric.
References:
* Anypoint CLI Documentation
* Mule Maven Plugin Documentation
NEW QUESTION # 80
An organization is evaluating using the CloudHub shared Load Balancer (SLB) vs creating a CloudHub dedicated load balancer (DLB). They are evaluating how this choice affects the various types of certificates used by CloudHub deplpoyed Mule applications, including MuleSoft-provided, customer-provided, or Mule application-provided certificates.
What type of restrictions exist on the types of certificates that can be exposed by the CloudHub Shared Load Balancer (SLB) to external web clients over the public internet?
Answer: A
Explanation:
https://docs.mulesoft.com/runtime-manager/dedicated-load-balancer-tutorial
NEW QUESTION # 81
An Order microservice and a Fulfillment microservice are being designed to communicate with their dients through message-based integration (and NOT through API invocations).
The Order microservice publishes an Order message (a kind of command message) containing the details of an order to be fulfilled. The intention is that Order messages are only consumed by one Mute application, the Fulfillment microservice.
The Fulfilment microservice consumes Order messages, fulfills the order described therein, and then publishes an OrderFulfilted message (a kind of event message). Each OrderFulfilted message can be consumed by any interested Mule application, and the Order microservice is one such Mute application.
What is the most appropriate choice of message broker(s) and message destination(s) in this scenario?
Answer: A
Explanation:
* If you need to scale a JMS provider/ message broker, - add nodes to scale it horizontally or - add memory to scale it vertically * Cons of adding another JMS provider/ message broker: - adds cost. - adds complexity to use two JMS brokers - adds Operational overhead if we use two brokers, say, ActiveMQ and IBM MQ * So Two options that mention to use two brokers are not best choice. * It's mentioned that "The Fulfillment microservice consumes Order messages, fulfills the order described therein, and then publishes an OrderFulfilled message. Each OrderFulfilled message can be consumed by any interested Mule application." - When you publish a message on a topic, it goes to all the subscribers who are interested - so zero to many subscribers will receive a copy of the message. - When you send a message on a queue, it will be received by exactly one consumer. * As we need multiple consumers to consume the message below option is not valid choice: "Order messages are sent to an Anypoint MQ exchange. OrderFulfilled messages are sent to an Anypoint MQ queue. Both microservices interact with Anypoint MQ as the message broker, which must therefore scale to support the load of both microservices" * Order messages are only consumed by one Mule application, the Fulfillment microservice, so we will publish it on queue and OrderFulfilled message can be consumed by any interested Mule application so it need to be published on Topic using same broker. * Correct answer:
NEW QUESTION # 82
A leading e-commerce giant will use Mulesoft API's on runtime fabric (RTF) to process customer orders.
Some customer's sensitive information such as credit card information is also there as a part of a API payload.
What approach minimizes the risk of matching sensitive data to the original and can convert back to the original value whenever and wherever required?
Answer: C
Explanation:
To minimize the risk of exposing sensitive data such as credit card information while still allowing it to be converted back to its original value when necessary, tokenization is the most effective approach. Tokenization replaces sensitive data with a non-sensitive equivalent, called a token, which has no exploitable value. This method ensures that the original sensitive data is stored securely and only accessible through a secure tokenization system.
In this scenario, applying a tokenization policy at the API Gateway ensures that sensitive information is replaced with tokens before the data leaves the trusted boundary. This minimizes the risk of matching sensitive data to the original. When the original data is needed, the tokenization system can detokenize the token back to its original value securely.
MuleSoft Documentation on Tokenization
NEW QUESTION # 83
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