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Salesforce Certified MuleSoft Platform Architect (Mule-Arch-201) Exam - Topic 2 Question 34 Discussion

Actual exam question for Salesforce's Salesforce Certified MuleSoft Platform Architect (Mule-Arch-201) exam
Question #: 34
Topic #: 2
[All Salesforce Certified MuleSoft Platform Architect (Mule-Arch-201) Questions]

A retail company is using an Order API to accept new orders. The Order API uses a JMS queue to submit orders to a backend order management service. The normal load for orders is being handled using two (2) CloudHub workers, each configured with 0.2 vCore. The CPU load of each CloudHub worker normally runs well below 70%. However, several times during the year the Order API gets four times (4x) the average number of orders. This causes the CloudHub worker CPU load to exceed 90% and the order submission time to exceed 30 seconds. The cause, however, is NOT the backend order management service, which still responds fast enough to meet the response SLA for the Order API. What is the MOST resource-efficient way to configure the Mule application's CloudHub deployment to help the company cope with this performance challenge?

Show Suggested Answer Hide Answer
Suggested Answer: D

Correct Answe r: Use a horizontal CloudHub autoscaling policy that triggers on CPU utilization greater than 70%

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The scenario in the question is very clearly stating that the usual traffic in the year is pretty well handled by the existing worker configuration with CPU running well below 70%. The problem occurs only 'sometimes' occasionally when there is spike in the number of orders coming in.

So, based on above, We neither need to permanently increase the size of each worker nor need to permanently increase the number of workers. This is unnecessary as other than those 'occasional' times the resources are idle and wasted.

We have two options left now. Either to use horizontal Cloudhub autoscaling policy to automatically increase the number of workers or to use vertical Cloudhub autoscaling policy to automatically increase the vCore size of each worker.

Here, we need to take two things into consideration:

1. CPU

2. Order Submission Rate to JMS Queue

>> From CPU perspective, both the options (horizontal and vertical scaling) solves the issue. Both helps to bring down the usage below 90%.

>> However, If we go with Vertical Scaling, then from Order Submission Rate perspective, as the application is still being load balanced with two workers only, there may not be much improvement in the incoming request processing rate and order submission rate to JMS queue. The throughput would be same as before. Only CPU utilization comes down.

>> But, if we go with Horizontal Scaling, it will spawn new workers and adds extra hand to increase the throughput as more workers are being load balanced now. This way we can address both CPU and Order Submission rate.

Hence, Horizontal CloudHub Autoscaling policy is the right and best answer.


Contribute your Thoughts:

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Tatum
2 months ago
Definitely not A, that seems like overkill for occasional spikes.
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Oliva
2 months ago
Surprised that they don't just increase the workers permanently.
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Leonora
3 months ago
I think D is better, horizontal scaling is more efficient!
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Christene
3 months ago
Option B sounds smart, autoscaling is the way to go!
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Novella
3 months ago
I agree with D, autoscaling is definitely the best approach here!
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Nada
3 months ago
I vaguely recall a practice question where vertical scaling was mentioned, but it seems like it could lead to wasted resources during normal load times.
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Alayna
3 months ago
I feel like the autoscaling option could be more efficient, especially since the load is temporary.
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German
4 months ago
I think increasing the number of workers might help, but isn't that more resource-intensive?
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Coral
4 months ago
I remember studying about autoscaling policies, but I'm not sure if vertical or horizontal is better for this scenario.
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Maile
4 months ago
This is a good opportunity to demonstrate my understanding of CloudHub scaling strategies. I'll carefully evaluate each option and explain my reasoning in the exam.
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Chandra
4 months ago
The key here is finding the most resource-efficient solution. I'm leaning towards the horizontal autoscaling approach, but I'll double-check the details to make sure it's the best fit.
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Dulce
4 months ago
Okay, I think I've got a handle on this. Increasing the worker size permanently seems like overkill, so I'll focus on the autoscaling options.
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Lauran
5 months ago
Hmm, I'm a bit confused by the details here. I'll need to re-read the question a few times to make sure I understand the requirements and constraints.
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Elliott
5 months ago
This seems like a tricky one. I'll need to carefully consider the resource efficiency aspect and think through the different scaling options.
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Floyd
5 months ago
But wouldn't it be more cost-effective to just increase the size of the existing workers instead of adding more workers?
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Johnson
5 months ago
Hmm, increasing the number of workers permanently seems a bit overkill. I'd go with the autoscaling policy, that way the resources can scale up and down as needed.
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Laurel
1 month ago
Definitely better than just increasing workers permanently!
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Fausto
2 months ago
Autoscaling can handle spikes efficiently.
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Ashanti
2 months ago
I agree, no need to waste resources all year.
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Dalene
2 months ago
Autoscaling sounds smart! Flexibility is key.
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Kristine
5 months ago
I disagree, I believe option D is more efficient. Adding more workers when needed will distribute the load better and prevent high CPU utilization.
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Floyd
7 months ago
I think option A is the best choice. Increasing the size of the CloudHub workers will provide more resources to handle the peak load.
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