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NEW QUESTION 38
Refer to the exhibit.
A RAML definition has been proposed for a new Promotions Process API, and has been published to Anypoint Exchange.
The Marketing Department, who will be an important consumer of the Promotions API, has important requirements and expectations that must be met.
What is the most effective way to use Anypoint Platform features to involve the Marketing Department in this early API design phase?
A) Ask the Marketing Department to interact with a mocking implementation of the API using the automatically generated API Console
B) Organize a design workshop with the DBAs of the Marketing Department in which the database schema of the Marketing IT systems is translated into RAML
C) Use Anypoint Studio to Implement the API as a Mule application, then deploy that API implementation to CloudHub and ask the Marketing Department to interact with it
D) Export an integration test suite from API designer and have the Marketing Department execute the tests In that suite to ensure they pass
- A. Option A
- B. Option D
- C. Option B
- D. Option C
Answer: B
NEW QUESTION 39
What is a best practice when building System APIs?
- A. Expose to API clients all technical details of the API implementation’s interaction with the backend system
- B. Document the API using an easily consumable asset like a RAML definition
- C. Build an Enterprise Data Model (Canonical Data Model) for each backend system and apply it to System APIs
- D. Model all API resources and methods to closely mimic the operations of the backend system
Answer: B
Explanation:
Explanation/Reference:
NEW QUESTION 40
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?
- A. Use a vertical CloudHub autoscaling policy that triggers on CPU utilization greater than 70%
- B. Permanently increase the number of CloudHub workers by four times (4x) to eight (8) CloudHub workers
- C. Permanently increase the size of each of the two (2) CloudHub workers by at least four times (4x) to one (1) vCore
- D. Use a horizontal CloudHub autoscaling policy that triggers on CPU utilization greater than 70%
Answer: D
Explanation:
Correct answer: Use a horizontal CloudHub autoscaling policy that triggers on CPU utilization greater than 70%
*****************************************
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.
NEW QUESTION 41
The responses to some HTTP requests can be cached depending on the HTTP verb used in the request.
According to the HTTP specification, for what HTTP verbs is this safe to do?
- A. GET, OPTIONS, HEAD
- B. GET, HEAD, POST
- C. GET, PUT, OPTIONS
- D. PUT, POST, DELETE
Answer: A
Explanation:
Explanation
http://restcookbook.com/HTTP%20Methods/idempotency/
NEW QUESTION 42
……
