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.
An eCommerce company is adding a new Product Details feature to their website, A customer will launch the product catalog page, a new Product Details link will appear by product where they can click to retrieve the product detail description. Product detail data is updated with product update releases, once or twice a year, Presently the database response time has been very slow due to high volume. What action retrieves the product details with the lowest response time, fault tolerant, and consistent data?
A. Select the product details from a database in a Cache scope and return them within the API response
B. Select the product details from a database and put them in Anypoint MQ; the Anypoint MO subseriber will receive the product details and return them within the API response
C. Use an object store to store and retrieve the product details originally read from a database and return them within the API response
D. Select the product details from a database and return them within the API response
To minimize operation costs, a customer wants to use a CloudHub 1.0 solution. The
customer's requirements are:
A. One production and one non-production Virtual Private Cloud (VPC).
Use availability zones to differentiate between Business groups.
Allocate maximum CIDR per VPCs to ensure HA across availability zones
B. One production and one non-production Virtual Private Cloud (VPC) per Business
group.
Minimize CIDR aligning with projected application total.
Choose a MuleSoft CloudHub 1.0 region with multiple availability zones.
Deploy multiple workers for HA,
C. One production and one non-production Virtual Private Cloud (VPC) per Business
group.
Minimize CIDR aligning with projected application total.
Divide availability zones during deployment of APIs for HA.
D. One production and one non-production Virtual Private Claud (VPC).
Configure subnet to differentiate between business groups.
Allocate maximum CIDR per VPCs to make it easier to add Child groups.
Span VPC to cover three availability zones.
Refer to the exhibit.
An organization uses one specific CloudHub (AWS) region for all CloudHub deployments.
How are CloudHub workers assigned to availability zones (AZs) when the organization's
Mule applications are deployed to CloudHub in that region?
A.
Workers belonging to a given environment are assigned to the same AZ within that region
B.
AZs are selected as part of the Mule application's deployment configuration
C.
Workers are randomly distributed across available AZs within that region
D.
An AZ is randomly selected for a Mule application, and all the Mule application's CloudHub workers are assigned to that one AZ
An AZ is randomly selected for a Mule application, and all the Mule application's CloudHub workers are assigned to that one AZ
Explanation: Explanation
Correct Answer: Workers are randomly distributed across available AZs within that region.
*****************************************
>> Currently, we only have control to choose which AWS Region to choose but there is no
control at all using any configurations or deployment options to decide what Availability
Zone (AZ) to assign to what worker.
>> There are NO fixed or implicit rules on platform too w.r.t assignment of AZ to workers
based on environment or application.
>> They are completely assigned in random. However, cloudhub definitely ensures that
HA is achieved by assigning the workers to more than on AZ so that all workers are not
assigned to same AZ for same application.
: https://help.mulesoft.com/s/question/0D52T000051rqDj/one-cloudhub-aws-region-howcloudhub-
workers-are-assigned-to-availability-zones-azs-
Graphical user interface, application
Description automatically generated
Bottom of Form
Top of Form
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
Due to a limitation in the backend system, a system API can only handle up to 500
requests per second. What is the best type of API policy to apply to the system API to avoid overloading the backend system?
A.
Rate limiting
B.
HTTP caching
C.
Rate limiting - SLA based
D.
Spike control
Spike control
Explanation: Explanation
Correct Answer: Spike control
*****************************************
>> First things first, HTTP Caching policy is for purposes different than avoiding the
backend system from overloading. So this is OUT.
>> Rate Limiting and Throttling/ Spike Control policies are designed to limit API access, but
have different intentions.
>> Rate limiting protects an API by applying a hard limit on its access.
>> Throttling/ Spike Control shapes API access by smoothing spikes in traffic.
That is why, Spike Control is the right option
An online store's marketing team has noticed an increase in customers leaving online baskets without checking out. They suspect a technology issue is at the root cause of the baskets being left behind. They approach the Center for Enablement to ask for help identifying the issue. Multiple APIs from across all the layers of their application network are involved in the shopping application. Which feature of the Anypoint Platform can be used to view metrics from all involved APIs at the same time?
A. Custom dashboards
B. Built-in dashboards
C. Functional monitoring
D. API Manager
An API is protected with a Client ID Enforcement policy and uses the default configuration. Access is requested for the client application to the API, and an approved contract now exists between the client application and the API. How can a consumer of this API avoid a 401 error "Unauthorized or invalid client application credentials"?
A. Send the obtained token as a header in every call
B. Send the obtained: client_id and client_secret in the request body
C. Send the obtained clent_id and clent_secret as URI parameters in every call
D. Send the obtained clent_id and client_secret in the header of every API Request call
Explanation:
When using the Client ID Enforcement policy with default settings,
MuleSoft expects the client_id and client_secret to be provided in the URI parameters of
each request. This policy is typically used to control and monitor access by validating that
each request has valid credentials. Here’s how to avoid a 401 Unauthorized error:
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