The implementation of a Process API must change.What is a valid approach that minimizes the impact of this change on API clients?
A.
Update the RAML definition of the current Process API and notify API client developers
by sending them links to the updated RAML definition
B.
Postpone changes until API consumers acknowledge they are ready to migrate to a new
Process API or API version
C.
Implement required changes to the Process API implementation so that whenever
possible, the Process API's RAML definition remains unchanged
D.
Implement the Process API changes in a new API implementation, and have the old API
implementation return an HTTP status code 301 - Moved Permanently to inform API clients
they should be calling the new API implementation
Implement required changes to the Process API implementation so that whenever
possible, the Process API's RAML definition remains unchanged
Explanation: Explanation
Correct Answer: Implement required changes to the Process API implementation so that,
whenever possible, the Process API’s RAML definition remains unchanged.
*****************************************
Key requirement in the question is:
>> Approach that minimizes the impact of this change on API clients
Based on above:
>> Updating the RAML definition would possibly impact the API clients if the changes
require any thing mandatory from client side. So, one should try to avoid doing that until
really necessary.
>> Implementing the changes as a completely different API and then redirectly the clients
with 3xx status code is really upsetting design and heavily impacts the API clients.
>> Organisations and IT cannot simply postpone the changes required until all API
consumers acknowledge they are ready to migrate to a new Process API or API version.
This is unrealistic and not possible.
The best way to handle the changes always is to implement required changes to the API
implementations so that, whenever possible, the API’s RAML definition remains
unchanged.
Which APIs can be used with DataGraph to create a unified schema?
A. APIs 1, 3, 5
B. APIs 2, 4 ,6
C. APIs 1, 2, s5, 6
D. APIs 1, 2, 3, 4
Explanation:
To create a unified schema in MuleSoft's DataGraph, APIs must be exposed
in a way that allows DataGraph to pull and consolidate data from these APIs into a single
schema accessible to consumers. DataGraph provides a federated approach, combining
multiple APIs to form a single, unified API endpoint.
In this setup:
APIs 1, 2, 3, and 4 are suitable candidates for DataGraph because they are hosted
within the Customer VPC on CloudHub and are accessible either through a
Shared Load Balancer (LB) or a Dedicated Load Balancer (DLB). Both of these
load balancers provide public access, which is a necessary condition for
DataGraph as it must access the APIs to aggregate data.
APIs 5 and 6 are hosted on Customer Hosted Server 2, which is explicitly marked
as "Not public". Since DataGraph requires API access through a publicly
reachable endpoint to aggregate them into a unified schema, APIs 5 and 6 cannot
be used with DataGraph in this configuration.
APIs 3 and 4 on Customer Hosted Server 1 appear accessible through a Shared
LB, implying public accessibility that meets DataGraph’s requirements.
By combining APIs 1, 2, 3, and 4 within DataGraph, you can create a unified schema that
enables clients to query data seamlessly from all these APIs as if it were from a single
source.
This setup allows for efficient data retrieval and can simplify API consumption by reducing
the need to call multiple APIs individually, thus optimizing performance and developer
experience.
An established communications company is beginning its API-led connectivity journey, The
company has been using a successful Enterprise Data Model for many years. The company has identified a self-service account management app as the first effort for APIled,
and it has identified the following APIs.
A. Customer SAPI
B. Customer Lookup PAPI
C. Mobile Account Management EAPI
D. Service SAPI
Explanation: In the API-led connectivity approach, APIs are categorized into Experience,
Process, and System layers:
Enterprise Data Model Scope:
Why Option C is Correct:
Explanation of Incorrect Options:
References:
For additional guidance, review MuleSoft's best practices on API-led
connectivity and data modeling.
An organization wants to create a Center for Enablement (C4E). The IT director schedules a series of meetings with IT senior managers. What should be on the agenda of the first meeting?
A. Define C4E objectives, mission statement, guiding principles, a
B. Explore API monetization options based on identified use cases through MuleSoft
C. A walk through of common-services best practices for logging, auditing, exception handling, caching, security via policy, and rate limiting/throttling via policy
D. Specify operating model for the MuleSoft Integrations division
Explanation:
In the initial meeting for establishing a Center for Enablement (C4E), it’s
essential to lay the foundational vision, objectives, and guiding principles for the team.
Here’s why this is crucial:
A business process is being implemented within an organization's application network. The architecture group proposes using a more coarse-grained application network design with relatively fewer APIs deployed to the application network compared to a more fine-grained design. Overall, which factor typically increases with a more coarse-grained design for this business process implementation and deployment compared with using a more finegrained design?
A. The complexity of each API implementation
B. The number of discoverable assets related to APIs deployed in the application network
C. The number of possible connections between API implementations in the application network
D. The usage of network infrastructure resources by the application network
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 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 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
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