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Question 1

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What API policy would LEAST likely be applied to a Process API?

Question 2

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What is a key performance indicator (KPI) that measures the success of a typical C4E that is immediately apparent in responses from the Anypoint Platform APIs?

Question 3

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An organization is implementing a Quote of the Day API that caches today's quote. What scenario can use the GoudHub Object Store via the Object Store connector to persist the cache's state?

Question 4

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What condition requires using a CloudHub Dedicated Load Balancer?

Question 5

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What do the API invocation metrics provided by Anypoint Platform provide?

Question 6

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What is true about the technology architecture of Anypoint VPCs?

Question 7

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An API implementation is deployed on a single worker on CloudHub and invoked by external API clients (outside of CloudHub). How can an alert be set up that is guaranteed to trigger AS SOON AS that A

Question 8

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The implementation of a Process API must change. What is a valid approach that minimizes the impact of this change on API clients?

Question 9

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Refer to the exhibit. An organization needs to enable access to their customer data from both a mobile app and a web application, which each need access to common fields as well as certain unique fi

Question 10

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Refer to the exhibit. A developer is building a client application to invoke an API deployed to the STAGING environment that is governed by a client ID enforcement policy. What is required to su

Question 61 - MCPA - Level 1 discussion

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?

Permanently increase the size of each of the two (2) CloudHub workers by at least four times (4x) to one (1) vCore

Use a vertical CloudHub autoscaling policy that triggers on CPU utilization greater than 70%

Permanently increase the number of CloudHub workers by four times (4x) to eight (8) CloudHub workers

Use a horizontal CloudHub autoscaling policy that triggers on CPU utilization greater than 70%

Suggested answer: A, B, C, D

Explanation:

Answer: 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.

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asked 18/09/2024

Hendrik van Bemmel

32 questions

Your answer: A B C D

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