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 size of each of the two (2) CloudHub workers by at least four
times (4x) to one (1) vCore
B.
Use a vertical CloudHub autoscaling policy that triggers on CPU utilization greater than
70%
C.
Permanently increase the number of CloudHub workers by four times (4x) to eight (8)
CloudHub workers
D.
Use a horizontal CloudHub autoscaling policy that triggers on CPU utilization greater
than 70%
Use a horizontal CloudHub autoscaling policy that triggers on CPU utilization greater
than 70%
Explanation: 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.
A customer wants to monitor and gain insights about the number of requests coming in a
given time period as well as to measure key performance indicators
(response times, CPU utilization, number of active APIs).
Which tool provides these data insights?
A. Anypoint Monitoring
B. APT Manager
C. Runtime Alerts
D. Functional Monitoring
An API has been updated in Anypoint Exchange by its API producer from version 3.1.1 to
3.2.0 following accepted semantic versioning practices and the changes have been
communicated via the API's public portal.
The API endpoint does NOT change in the new version.
How should the developer of an API client respond to this change?
A.
The update should be identified as a project risk and full regression testing of the functionality that uses this API should be run
B.
The API producer should be contacted to understand the change to existing functionality
C.
The API producer should be requested to run the old version in parallel with the new one
D.
The API client code ONLY needs to be changed if it needs to take advantage of new
features
The API client code ONLY needs to be changed if it needs to take advantage of new
features
Reference: https://docs.mulesoft.com/exchange/to-change-raml-version
Several times a week, an API implementation shows several thousand requests per minute
in an Anypoint Monitoring dashboard, Between these bursts, the
dashboard shows between two and five requests per minute. The API implementation is
running on Anypoint Runtime Fabric with two non-clustered replicas, reserved vCPU 1.0
and vCPU Limit 2.0.
An API consumer has complained about slow response time, and the dashboard shows the
99 percentile is greater than 120 seconds at the time of the complaint. It also shows greater than 90% CPU usage during these time periods.
In manual tests in the QA environment, the API consumer has consistently reproduced the
slow response time and high CPU usage, and there were no other API requests at
this time. In a brainstorming session, the engineering team has created several proposals
to reduce the response time for requests.
Which proposal should be pursued first?
A. Increase the vCPU resources of the API implementation
B. Modify the API client to split the problematic request into smaller, less-demanding requests
C. Increase the number of replicas of the API implementation
D. Throttle the APT client to reduce the number of requests per minute
A REST API is being designed to implement a Mule application.
What standard interface definition language can be used to define REST APIs?
A.
Web Service Definition Language(WSDL)
B.
OpenAPI Specification (OAS)
C.
YAML
D.
AsyncAPI Specification
OpenAPI Specification (OAS)
A System API is designed to retrieve data from a backend system that has scalability challenges. What API policy can best safeguard the backend system?
A.
IPwhitelist
B.
SLA-based rate limiting
C.
Auth 2 token enforcement
D.
Client ID enforcement
SLA-based rate limiting
Explanation: Explanation
Correct Answer: SLA-based rate limiting
*****************************************
>> Client Id enforement policy is a "Compliance" related NFR and does not help in
maintaining the "Quality of Service (QoS)". It CANNOT and NOT meant for protecting the
backend systems from scalability challenges.
>> IP Whitelisting and OAuth 2.0 token enforcement are "Security" related NFRs and again
does not help in maintaining the "Quality of Service (QoS)". They CANNOT and are NOT
meant for protecting the backend systems from scalability challenges.
Rate Limiting, Rate Limiting-SLA, Throttling, Spike Control are the policies that are "Quality
of Service (QOS)" related NFRs and are meant to help in protecting the backend systems
from getting overloaded.
https://dzone.com/articles/how-to-secure-apis
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
TRUE
Explanation: Explanation
Correct Answer: 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.
Which scenario is suited for MUnit tests instead of integration tests?
A. For read-only interactions to any dependencies (such as other web APIs)
B. When testing does not require knowledge of implementation details
C. When no mocking is permissible
D. For tests that are implemented using SoapUI
Explanation:
MUnit is MuleSoft’s testing framework for creating and running automated
tests within Anypoint Studio. It is specifically designed for unit testing Mule applications and
is best suited when testing doesn’t require understanding the inner workings or
implementation details of the components being tested.
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