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-
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Description automatically generated
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Mule applications that implement a number of REST APIs are deployed to their own subnet
that is inaccessible from outside the organization.
External business-partners need to access these APIs, which are only allowed to be
invoked from a separate subnet dedicated to partners - called Partner-subnet. This subnet
is accessible from the public internet, which allows these external partners to reach it.
Anypoint Platform and Mule runtimes are already deployed in Partner-subnet. These Mule
runtimes can already access the APIs.
What is the most resource-efficient solution to comply with these requirements, while
having the least impact on other applications that are currently using the APIs?
A.
Implement (or generate) an API proxy Mule application for each of the APIs, then deploy the API proxies to the Mule runtimes
B.
Redeploy the API implementations to the same servers running the Mule runtimes
C.
Add an additional endpoint to each API for partner-enablement consumption
D.
Duplicate the APIs as Mule applications, then deploy them to the Mule runtimes
Implement (or generate) an API proxy Mule application for each of the APIs, then deploy the API proxies to the Mule runtimes
An API implementation is deployed to CloudHub.
What conditions can be alerted on using the default Anypoint Platform functionality, where
the alert conditions depend on the end-to-end request processing of the API
implementation?
A.
When the API is invoked by an unrecognized API client
B.
When a particular API client invokes the API too often within a given time period
C.
When the response time of API invocations exceeds a threshold
D.
When the API receives a very high number of API invocations
When the response time of API invocations exceeds a threshold
Explanation: Explanation
Correct Answer: When the response time of API invocations exceeds a threshold
*****************************************
>> Alerts can be setup for all the given options using the default Anypoint Platform
functionality
>> However, the question insists on an alert whose conditions depend on the end-to-end
request processing of the API implementation.
>> Alert w.r.t "Response Times" is the only one which requires end-to-end request
processing of API implementation in order to determine if the threshold is exceeded or not.
Reference: https://docs.mulesoft.com/api-manager/2.x/using-api-alerts
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, and ONLY use the first response
B. 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
C. Invoke the system API deployed to the primary environment, and if it fails, invoke the system API deployed to the DR environment
D. Invoke ONLY the system API deployed to the primary environment, and add timeout and retry logic to avoid intermittent failures
Explanation: Explanation
Correct Answer: 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
A company wants to move its Mule API implementations into production as quickly as
possible. To protect access to all Mule application data and metadata, the company
requires that all Mule applications be deployed to the company's customer-hosted
infrastructure within the corporate firewall. What combination of runtime plane and control
plane options meets these project lifecycle goals?
A.
Manually provisioned customer-hosted runtime plane and customer-hosted control plane
B.
MuleSoft-hosted runtime plane and customer-hosted control plane
C.
Manually provisioned customer-hosted runtime plane and MuleSoft-hosted control plane
D.
iPaaS provisioned customer-hosted runtime plane and MuleSoft-hosted control plane
Manually provisioned customer-hosted runtime plane and customer-hosted control plane
Explanation:
Explanation
Correct Answer: Manually provisioned customer-hosted runtime plane and customerhosted
control plane
*****************************************
There are two key factors that are to be taken into consideration from the scenario given in
the question.
>> Company requires both data and metadata to be resided within the corporate firewall
>> Company would like to go with customer-hosted infrastructure.
Any deployment model that is to deal with the cloud directly or indirectly (Mulesoft-hosted
or Customer's own cloud like Azure, AWS) will have to share atleast the metadata.
Application data can be controlled inside firewall by having Mule Runtimes on customer
hosted runtime plane. But if we go with Mulsoft-hosted/ Cloud-based control plane, the
control plane required atleast some minimum level of metadata to be sent outside the
corporate firewall.
As the customer requirement is pretty clear about the data and metadata both to be within
the corporate firewall, even though customer wants to move to production as quickly as
possible, unfortunately due to the nature of their security requirements, they have no other
option but to go with manually provisioned customer-hosted runtime plane and customerhosted
control plane.
An API experiences a high rate of client requests (TPS) vwth small message paytoads.
How can usage limits be imposed on the API based on the type of client application?
A.
Use an SLA-based rate limiting policy and assign a client application to a matching SLA
tier based on its type
B.
Use a spike control policy that limits the number of requests for each client application
type
C.
Use a cross-origin resource sharing (CORS) policy to limit resource sharing between
client applications, configured by the client application type
D.
Use a rate limiting policy and a client ID enforcement policy, each configured by the
client application type
Use an SLA-based rate limiting policy and assign a client application to a matching SLA
tier based on its type
Explanation: Correct Answer: Use an SLA-based rate limiting policy and assign a client
application to a matching SLA tier based on its type.
*****************************************
>> SLA tiers will come into play whenever any limits to be imposed on APIs based on client
type
Reference: https://docs.mulesoft.com/api-manager/2.x/rate-limiting-and-throttling-slabased-
policies
In an organization, the InfoSec team is investigating Anypoint Platform related data traffic. From where does most of the data available to Anypoint Platform for monitoring and alerting originate?
A.
From the Mule runtime or the API implementation, depending on the deployment model
B.
From various components of Anypoint Platform, such as the Shared Load Balancer, VPC, and Mule runtimes
C.
From the Mule runtime or the API Manager, depending on the type of data
D.
From the Mule runtime irrespective of the deployment model
From the Mule runtime irrespective of the deployment model
Explanation: Explanation
Correct Answer: From the Mule runtime irrespective of the deployment model
*****************************************
>> Monitoring and Alerting metrics are always originated from Mule Runtimes irrespective
of the deployment model.
>> It may seems that some metrics (Runtime Manager) are originated from Mule Runtime
and some are (API Invocations/ API Analytics) from API Manager. However, this is
realistically NOT TRUE. The reason is, API manager is just a management tool for API
instances but all policies upon applying on APIs eventually gets executed on Mule
Runtimes only (Either Embedded or API Proxy).
>> Similarly all API Implementations also run on Mule Runtimes.
So, most of the day required for monitoring and alerts are originated fron Mule Runtimes
only irrespective of whether the deployment model is MuleSoft-hosted or Customer-hosted
or Hybrid.
What is a typical result of using a fine-grained rather than a coarse-grained API deployment model to implement a given business process?
A.
A decrease in the number of connections within the application network supporting the business process
B.
A higher number of discoverable API-related assets in the application network
C.
A better response time for the end user as a result of the APIs being smaller in scope and complexity
D.
An overall tower usage of resources because each fine-grained API consumes less resources
A higher number of discoverable API-related assets in the application network
Explanation: Explanation
Correct Answer: A higher number of discoverable API-related assets in the application
network.
*****************************************
>> We do NOT get faster response times in fine-grained approach when compared to
coarse-grained approach.
>> In fact, we get faster response times from a network having coarse-grained APIs
compared to a network having fine-grained APIs model. The reasons are below.
Fine-grained approach:
1. will have more APIs compared to coarse-grained
2. So, more orchestration needs to be done to achieve a functionality in business process.
3. Which means, lots of API calls to be made. So, more connections will needs to be
established. So, obviously more hops, more network i/o, more number of integration points
compared to coarse-grained approach where fewer APIs with bulk functionality embedded
in them.
4. That is why, because of all these extra hops and added latencies, fine-grained approach
will have bit more response times compared to coarse-grained.
5. Not only added latencies and connections, there will be more resources used up in finegrained
approach due to more number of APIs.
That's why, fine-grained APIs are good in a way to expose more number of resuable assets
in your network and make them discoverable. However, needs more maintenance, taking
care of integration points, connections, resources with a little compromise w.r.t network
hops and response times.
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