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MuleSoft MCPA-Level-1-Maintenance Exam Syllabus Topics:

TopicDetails
Topic 1
  • Describe the metrics collected by Anypoint Platform for API invocations
  • Specify alerts to define for key metrics of API invocations for all layers of API-led connectivity
Topic 2
  • Identify changes to an API that would require or not require a major version increment
  • Explain how to register an API client for access to an API version
Topic 3
  • Define and describe the benefits of API-led connectivity and application networks
  • Explain MuleSoft’s proposal for closing the IT delivery gap
Topic 4
  • Identify KPIs to measure the success of a C4E
  • Select Anypoint Platform identity management vs client management for the correct purpose
Topic 5
  • Describe the purpose and roles of a Center for Enablement (C4E)
  • Describe the capabilities and high-level components of Anypoint Platform
Topic 6
  • Identify the factors involved in scaling API performance
  • Select strategies that help API clients guard against failures in API invocations
Topic 7
  • Correctly use the terms API, API implementation, API client, API consumer, and API invocation
  • Describe the fundamentals of deployments, networking, and routing on CloudHub

MuleSoft Certified Platform Architect - Level 1 MAINTENANCE Sample Questions (Q63-Q68):

NEW QUESTION # 63
True or False. We should always make sure that the APIs being designed and developed are self-servable even if it needs more man-day effort and resources.

  • A. FALSE
  • B. TRUE

Answer: B

Explanation:
TRUE
*****************************************
>> As per MuleSoft proposed IT Operating Model, designing APIs and making sure that they are discoverable and self-servable is VERY VERY IMPORTANT and decides the success of an API and its application network.


NEW QUESTION # 64
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. Permanently increase the number of CloudHub workers by four times (4x) to eight (8) CloudHub workers
  • B. Use a horizontal CloudHub autoscaling policy that triggers on CPU utilization greater than 70%
  • C. Use a vertical CloudHub autoscaling policy that triggers on CPU utilization greater than 70%
  • D. Permanently increase the size of each of the two (2) CloudHub workers by at least four times (4x) to one (1) vCore

Answer: B

Explanation:
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 # 65
What are the major benefits of MuleSoft proposed IT Operating Model?

  • A. 1. Decrease the IT delivery gap
    2. Meet various business demands without increasing the IT capacity
    3. Focus on creation of reusable assets first. Upon finishing creation of all the possible assets then inform the LOBs in the organization to start using them
  • B. 1. Decrease the IT delivery gap
    2. Meet various business demands by increasing the IT capacity and forming various IT departments
    3. Make consumption of assets at the rate of production
  • C. 1. Decrease the IT delivery gap
    2. Meet various business demands without increasing the IT capacity
    3. Make consumption of assets at the rate of production

Answer: C

Explanation:
1. Decrease the IT delivery gap
2. Meet various business demands without increasing the IT capacity
3. Make consumption of assets at the rate of production.
*****************************************


NEW QUESTION # 66
An API implementation is updated. When must the RAML definition of the API also be updated?

  • A. When the API implementation is migrated from an older to a newer version of the Mule runtime
  • B. When the API implementation changes from interacting with a legacy backend system deployed on-premises to a modern, cloud-based (SaaS) system
  • C. When the API implementation is optimized to improve its average response time
  • D. When the API implementation changes the structure of the request or response messages

Answer: D

Explanation:
When the API implementation changes the structure of the request or response messages
*****************************************
>> RAML definition usually needs to be touched only when there are changes in the request/response schemas or in any traits on API.
>> It need not be modified for any internal changes in API implementation like performance tuning, backend system migrations etc..


NEW QUESTION # 67
A system API has a guaranteed SLA of 100 ms per request. The system API is deployed to a primary environment as well as to a disaster recovery (DR) environment, with different DNS names in each environment. An upstream process API invokes the system API and the main goal of this process API is to respond to client requests in the least possible time. In what order should the system APIs be invoked, and what changes should be made in order to speed up the response time for requests from the process API?

  • A. In parallel, invoke the system API deployed to the primary environment and the system API deployed to the DR environment using a scatter-gather configured with a timeout, and then merge the responses
  • B. In parallel, invoke the system API deployed to the primary environment and the system API deployed to the DR environment, and ONLY use the first response
  • C. Invoke ONLY the system API deployed to the primary environment, and add timeout and retry logic to avoid intermittent failures
  • D. Invoke the system API deployed to the primary environment, and if it fails, invoke the system API deployed to the DR environment

Answer: B

Explanation:
In parallel, invoke the system API deployed to the primary environment and the system API deployed to the DR environment, and ONLY use the first response.
*****************************************
>> The API requirement in the given scenario is to respond in least possible time.
>> The option that is suggesting to first try the API in primary environment and then fallback to API in DR environment would result in successful response but NOT in least possible time. So, this is NOT a right choice of implementation for given requirement.
>> Another option that is suggesting to ONLY invoke API in primary environment and to add timeout and retries may also result in successful response upon retries but NOT in least possible time. So, this is also NOT a right choice of implementation for given requirement.
>> One more option that is suggesting to invoke API in primary environment and API in DR environment in parallel using Scatter-Gather would result in wrong API response as it would return merged results and moreover, Scatter-Gather does things in parallel which is true but still completes its scope only on finishing all routes inside it. So again, NOT a right choice of implementation for given requirement The Correct choice is to invoke the API in primary environment and the API in DR environment parallelly, and using ONLY the first response received from one of them.


NEW QUESTION # 68
......

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