Traffic is routed through an API proxy to an API implementation. The API proxy is managed
by API Manager and the API implementation is deployed to a CloudHub VPC using
Runtime Manager. API policies have been applied to this API. In this deployment scenario,
at what point are the API policies enforced on incoming API client requests?
A.
At the API proxy
B.
At the API implementation
C.
At both the API proxy and the API implementation
D.
At a MuleSoft-hosted load balancer
At the API proxy
Explanation: Explanation
Correct Answer: At the API proxy
*****************************************
>> API Policies can be enforced at two places in Mule platform.
>> One - As an Embedded Policy enforcement in the same Mule Runtime where API
implementation is running.
>> Two - On an API Proxy sitting in front of the Mule Runtime where API implementation is
running.
>> As the deployment scenario in the question has API Proxy involved, the policies will be
enforced at the API Proxy.
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
When could the API data model of a System API reasonably mimic the data model
exposed by the corresponding backend system, with minimal improvements over the
backend system's data model?
A.
When there is an existing Enterprise Data Model widely used across the organization
B.
When the System API can be assigned to a bounded context with a corresponding data
model
C.
When a pragmatic approach with only limited isolation from the backend system is deemed appropriate
D.
When the corresponding backend system is expected to be replaced in the near future
When a pragmatic approach with only limited isolation from the backend system is deemed appropriate
Explanation: Explanation
Correct Answer: When a pragmatic approach with only limited isolation from the backend
system is deemed appropriate.
*****************************************
General guidance w.r.t choosing Data Models:
>> If an Enterprise Data Model is in use then the API data model of System APIs should
make use of data types from that Enterprise Data Model and the corresponding API
implementation should translate between these data types from the Enterprise Data Model
and the native data model of the backend system.
>> If no Enterprise Data Model is in use then each System API should be assigned to a
Bounded Context, the API data model of System APIs should make use of data types from
the corresponding Bounded Context Data Model and the corresponding API
implementation should translate between these data types from the Bounded Context Data
Model and the native data model of the backend system. In this scenario, the data types in
the Bounded Context Data Model are defined purely in terms of their business
characteristics and are typically not related to the native data model of the backend system.
In other words, the translation effort may be significant.
>> If no Enterprise Data Model is in use, and the definition of a clean Bounded Context
Data Model is considered too much effort, then the API data model of System APIs should
make use of data types that approximately mirror those from the backend system, same
semantics and naming as backend system, lightly sanitized, expose all fields needed for
the given System API’s functionality, but not significantly more and making good use of
REST conventions.
The latter approach, i.e., exposing in System APIs an API data model that basically mirrors
that of the backend system, does not provide satisfactory isolation from backend systems
through the System API tier on its own. In particular, it will typically not be possible to
"swap out" a backend system without significantly changing all System APIs in front of that
backend system and therefore the API implementations of all Process APIs that depend on
those System APIs! This is so because it is not desirable to prolong the life of a previous
backend system’s data model in the form of the API data model of System APIs that now
front a new backend system. The API data models of System APIs following this approach
must therefore change when the backend system is replaced.
On the other hand:
>> It is a very pragmatic approach that adds comparatively little overhead over accessing
the backend system directly
>> Isolates API clients from intricacies of the backend system outside the data model
(protocol, authentication, connection pooling, network address, …)
>> Allows the usual API policies to be applied to System APIs
>> Makes the API data model for interacting with the backend system explicit and visible,
by exposing it in the RAML definitions of the System APIs
>> Further isolation from the backend system data model does occur in the API
An Order API must be designed that contains significant amounts of integration logic and
involves the invocation of the Product API.
The power relationship between Order API and Product API is one of "Customer/Supplier",
because the Product API is used heavily throughout the organization and is developed by a
dedicated development team located in the office of the CTO.
What strategy should be used to deal with the API data model of the Product API within the
Order API?
A.
Convince the development team of the Product API to adopt the API data model of the Order API such that the integration logic of the Order API can work with one consistent internal data model
B.
Work with the API data types of the Product API directly when implementing the integration logic of the Order API such that the Order API uses the same (unchanged) data types as the Product API
C.
Implement an anti-corruption layer in the Order API that transforms the Product API data
model into internal data types of the Order API
D.
Start an organization-wide data modeling initiative that will result in an Enterprise Data
Model that will then be used in both the Product API and the Order API
Implement an anti-corruption layer in the Order API that transforms the Product API data
model into internal data types of the Order API
Explanation: Explanation
Correct Answer: Convince the development team of the product API to adopt the API data
model of the Order API such that integration logic of the Order API can work with one
consistent internal data model
*****************************************
Key details to note from the given scenario:
>> Power relationship between Order API and Product API is customer/supplier
So, as per below rules of "Power Relationships", the caller (in this case Order API) would
request for features to the called (Product API team) and the Product API team would need
to accomodate those requests.
What Mule application deployment scenario requires using Anypoint Platform Private Cloud Edition or Anypoint Platform for Pivotal Cloud Foundry?
A.
When it Is required to make ALL applications highly available across multiple data centers
B.
When it is required that ALL APIs are private and NOT exposed to the public cloud
C.
When regulatory requirements mandate on-premises processing of EVERY data item, including meta-data
D.
When ALL backend systems in the application network are deployed in the
organization's intranet
When regulatory requirements mandate on-premises processing of EVERY data item, including meta-data
Explanation: Explanation
Correct Answer: When regulatory requirements mandate on-premises processing of EVERY data item, including meta-data.
*****************************************
We need NOT require to use Anypoint Platform PCE or PCF for the below. So these
options are OUT.
>> We can make ALL applications highly available across multiple data centers using
CloudHub too.
>> We can use Anypoint VPN and tunneling from CloudHub to connect to ALL backend
systems in the application network that are deployed in the organization's intranet.
>> We can use Anypoint VPC and Firewall Rules to make ALL APIs private and NOT
exposed to the public cloud.
Only valid reason in the given options that requires to use Anypoint Platform PCE/ PCF is -
When regulatory requirements mandate on-premises processing of EVERY data item,
including meta-data
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.
Which out-of-the-box key performance indicator measures the success of a typical Center for Enablement and is immediately available in responses from Anypoint Platform APIs?
A. Per business group, the ratio of the number of production APT implementations deployed using a C1/CD pipeline to the number of production API implementations deployed manually
B. Per deployed API implementation, the amount of bandwidth consumed each day
C. Per published API, the number of developers that downloaded s version of the API specification
D. Per published API, the number of consumers that requested access to the API and have been approved in the Production environment
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
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