Salesforce MuleSoft-Integration-Architect-I Exam With Confidence Using Practice Dumps

Explanation

Before we answer this question , we need to understand what median (50th percentile) and 80th percentile means. If the 50th percentile (median) of a response time is 500ms that means that 50% of my transactions are either as fast or faster than 500ms.

If the 90th percentile of the same transaction is at 1000ms it means that 90% are as fast or faster and only 10% are slower. Now as per upstream SLA , 99th percentile is 800 ms which means 99% of the incoming requests should have response time less than or equal to 800 ms. But as per one of the backend API , their 95th percentile is 1000 ms which means that backend API will take 1000 ms or less than that for 95% of. requests. As there are three API invocation from upstream API , we can not conclude a timeout that can be set to meet the desired SLA as backend SLA's do not support it.

Let see why other answers are not correct.

1) Do not set a timeout --> This can potentially violate SLA's of upstream API

2) Set a timeout of 100 ms; ---> This will not work as backend API has 100 ms as median meaning only 50% requests will be answered in this time and we will get timeout for 50% of the requests. Important thing to note here is, All APIs need to be executed sequentially, so if you get timeout in first API, there is no use of going to second and third API. As a service provider you wouldn't want to keep 50% of your consumers dissatisfied. So not the best option to go with.

*To quote an example: Let's assume you have built an API to update customer contact details.

- First API is fetching customer number based on login credentials

- Second API is fetching Info in 1 table and returning unique key

- Third API, using unique key provided in second API as primary key, updating remaining details

* Now consider, if API times out in first API and can't fetch customer number, in this case, it's useless to call API 2 and 3 and that is why question mentions specifically that all APIs need to be executed sequentially.

3) Set a timeout of 50 ms --> Again not possible due to the same reason as above Hence correct answer is No timeout is possible to meet the upstream API's desired SLA; a different SLA must be negotiated with the first downstream API or invoke an alternative API

In a hybrid, load-balanced, single cluster production environment running Mule applications in an active-active multi-node configuration, several considerations are critical for ensuring performance and reliability. The key consideration is the use of an external load balancer:

Active-Active Multi-Node Cluster Configuration:

An active-active cluster means that all nodes are actively handling traffic, providing high availability and better resource utilization.

External Load Balancer Requirement:

Distribution of Requests: An external load balancer is essential to evenly distribute incoming requests across all active nodes in the cluster. This prevents any single node from becoming a bottleneck and ensures balanced load distribution.

Scalability and Failover: The load balancer provides scalability by allowing more nodes to be added seamlessly. It also handles failover, rerouting traffic to healthy nodes if one node goes down.

Load Balancer Configuration:

Setup: Configure the load balancer to include all the nodes of the Mule cluster.

Health Checks: Implement health checks to monitor the status of each node and ensure traffic is only directed to healthy nodes.

Session Persistence: If required, enable session persistence (sticky sessions) to ensure that user sessions remain consistent across requests.

Mule Application Isolation:

Each Mule application instance runs in isolation but shares the same configuration and state. This isolation ensures that the failure of one node does not impact others.

Handling Requests:

In an active-active configuration, all nodes handle incoming requests simultaneously. The load balancer's role is to manage the distribution of these requests efficiently.

Benefits:

High Availability: Ensures that the system remains available even if some nodes fail.

Improved Performance: Balances the load, preventing any single node from being overwhelmed.

Scalability: Makes it easy to scale horizontally by adding more nodes.

MuleSoft Documentation on Mule Clustering

Best Practices for Load Balancing