C.

At the process and system tiers

Explanation: Explanation Correct Answer: At the process and system tiers
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>> Experience Layer APIs are modeled and designed exclusively for the end user's
experience. So, the data models of experience layer vary based on the nature and type of
such API consumer. For example, Mobile consumers will need light-weight data models to
transfer with ease on the wire, where as web-based consumers will need detailed data
models to render most of the info on web pages, so on. So, enterprise data models fit for
the purpose of canonical models but not of good use for experience APIs.
>> That is why, EDMs should be used extensively in process and system tiers but NOT in
experience tier.

C.

When the response time of API invocations exceeds a threshold

Explanation: Explanation
Correct Answer: When the response time of API invocations exceeds a threshold
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>> 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

C.

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.
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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

A.

Apply a basic authentication - LDAP policy; the internal Active Directory will be
configured as the LDAP source for authenticating users

Explanation: Explanation
Correct Answer: Apply a basic authentication - LDAP policy; the internal Active Directory
will be configured as the LDAP source for authenticating users.
*****************************************
>> IP Whitelisting does NOT fit for this purpose. Moreover, the users workstations may not
necessarily have static IPs in the network.
>> OAuth 2.0 enforcement requires a client provider which isn't in the organizations system
components.
>> It is not an effective approach to let every user create separate client credentials and
configure those for their usage.
The effective way it to apply a basic authentication - LDAP policy and the internal Active
Directory will be configured as the LDAP source for authenticating users.
Reference: https://docs.mulesoft.com/api-manager/2.x/basic-authentication-ldap-concept

A.

Implement (or generate) an API proxy Mule application for each of the APIs, then deploy the API proxies to the Mule runtimes

B.

Option B

Explanation: •Explanation
Correct Answer: Use a combination of CloudHub-deployed and manually provisioned onpremises
Mule runtimes managed by the MuleSoft-hosted Platform control plane.
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Key details to be taken from the given scenario:
>> Organization uses BOTH cloud-based and on-premises systems
>> On-premises systems can only be accessed from within the organization's intranet
Let us evaluate the given choices based on above key details:
>> CloudHub-deployed Mule runtimes can ONLY be controlled using MuleSoft-hosted
control plane. We CANNOT use Private Cloud Edition's control plane to control CloudHub
Mule Runtimes. So, option suggesting this is INVALID
>> Using CloudHub-deployed Mule runtimes in the shared worker cloud managed by the
MuleSoft-hosted Anypoint Platform is completely IRRELEVANT to given scenario and silly
choice. So, option suggesting this is INVALID
>> Using an on-premises installation of Mule runtimes that are completely isolated with NO
external network access, managed by the Anypoint Platform Private Cloud Edition control
plane would work for On-premises integrations. However, with NO external access,
integrations cannot be done to SaaS-based apps. Moreover CloudHub-hosted apps are
best-fit for integrating with SaaS-based applications. So, option suggesting this is BEST
WAY.
The best way to configure and use Anypoint Platform to support these mixed/hybrid
integrations is to use a combination of CloudHub-deployed and manually provisioned onpremises
Mule runtimes managed by the MuleSoft-hosted Platform control plane.

C.

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.
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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

D.

When server-side load-balanced TLS mutual authentication is required between API
implementations and API clients

Explanation: Explanation
Correct Answer: When server-side load-balanced TLS mutual authentication is required
between API implementations and API clients
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Fact/ Memory Tip: Although there are many benefits of CloudHub Dedicated Load
balancer, TWO important things that should come to ones mind for considering it are:
>> Having URL endpoints with Custom DNS names on CloudHub deployed apps
>> Configuring custom certificates for both HTTPS and Two-way (Mutual) authentication.
Coming to the options provided for this question:
>> We CANNOT use DLB to perform cross-region load balancing between separate
deployments of the same Mule application.
>> We can have mapping rules to have more than one DLB URL pointing to same Mule
app. But vicevera (More than one Mule app having same DLB URL) is NOT POSSIBLE
>> It is true that DLB helps to setup custom DNS names for Cloudhub deployed Mule apps
but NOT true for apps deployed to Customer-hosted Mule Runtimes.
>> It is true to that we can load balance API invocations across multiple CloudHub workers
using DLB but it is NOT A MUST. We can achieve the same (load balancing) using SLB
(Shared Load Balancer) too. We DO NOT necessarily require DLB for achieve it.
So the only right option that fits the scenario and requires us to use DLB is when TLS
mutual authentication is required between API implementations and API clients.
Reference: https://docs.mulesoft.com/runtime-manager/cloudhub-dedicated-load-balancer