An organization is implementing a Quote of the Day API that caches today's quote.
What scenario can use the GoudHub Object Store via the Object Store connector to persist
the cache's state?
A.
When there are three CloudHub deployments of the API implementation to three
separate CloudHub regions that must share the cache state
B.
When there are two CloudHub deployments of the API implementation by two Anypoint
Platform business groups to the same CloudHub region that must share the cache state
C.
When there is one deployment of the API implementation to CloudHub and anottV
deployment to a customer-hosted Mule runtime that must share the cache state
D.
When there is one CloudHub deployment of the API implementation to three CloudHub
workers that must share the cache state
When there is one CloudHub deployment of the API implementation to three CloudHub
workers that must share the cache state
Explanation: Explanation
Correct Answer: When there is one CloudHub deployment of the API implementation to
three CloudHub workers that must share the cache state.
*****************************************
Key details in the scenario:
>> Use the CloudHub Object Store via the Object Store connector
Considering above details:
>> CloudHub Object Stores have one-to-one relationship with CloudHub Mule Applications.
>> We CANNOT use an application's CloudHub Object Store to be shared among multiple
Mule applications running in different Regions or Business Groups or Customer-hosted
Mule Runtimes by using Object Store connector.
>> If it is really necessary and very badly needed, then Anypoint Platform supports a way
by allowing access to CloudHub Object Store of another application using Object Store
REST API. But NOT using Object Store connector.
So, the only scenario where we can use the CloudHub Object Store via the Object Store
connector to persist the cache’s state is when there is one CloudHub deployment of the
API implementation to multiple CloudHub workers that must share the cache state
True or False. We should always make sure that the APIs being designed and developed are self-servable even if it needs more man-day effort and resources.
A.
FALSE
B.
TRUE
TRUE
Explanation: Explanation
Correct Answer: TRUE
*****************************************
>> As per MuleSoft proposed IT Operating Model, designing APIs and making sure that
they are discoverable and self-servable is VERY VERY IMPORTANT and decides the
success of an API and its application network.
What best explains the use of auto-discovery in API implementations?
A. It makes API Manager aware of API implementations and hence enables it to enforce policies
B. It enables Anypoint Studio to discover API definitions configured in Anypoint Platform
C. It enables Anypoint Exchange to discover assets and makes them available for reuse
D. It enables Anypoint Analytics to gain insight into the usage of APIs
Explanation: Explanation
Correct Answer: It makes API Manager aware of API implementations and hence enables it
to enforce policies.
*****************************************
>> API Autodiscovery is a mechanism that manages an API from API Manager by pairing
the deployed application to an API created on the platform.
>> API Management includes tracking, enforcing policies if you apply any, and reporting
API analytics.
>> Critical to the Autodiscovery process is identifying the API by providing the API name
and version.
References:
https://docs.mulesoft.com/api-manager/2.x/api-auto-discovery-new-concept
https://docs.mulesoft.com/api-manager/1.x/api-auto-discovery
https://docs.mulesoft.com/api-manager/2.x/api-auto-discovery-new-concept
A company has created a successful enterprise data model (EDM). The company is
committed to building an application network by adopting modern APIs as a core enabler of
the company's IT operating model. At what API tiers (experience, process, system) should
the company require reusing the EDM when designing modern API data models?
A.
At the experience and process tiers
B.
At the experience and system tiers
C.
At the process and system tiers
D.
At the experience, process, and system tiers
At the process and system tiers
Explanation: Explanation Correct Answer: At the process and system tiers
*****************************************
>> 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.
A retail company with thousands of stores has an API to receive data about purchases and
insert it into a single database. Each individual store sends a batch of purchase data to the
API about every 30 minutes. The API implementation uses a database bulk insert
command to submit all the purchase data to a database using a custom JDBC driver
provided by a data analytics solution provider. The API implementation is deployed to a
single CloudHub worker. The JDBC driver processes the data into a set of several
temporary disk files on the CloudHub worker, and then the data is sent to an analytics
engine using a proprietary protocol. This process usually takes less than a few minutes.
Sometimes a request fails. In this case, the logs show a message from the JDBC driver
indicating an out-of-file-space message. When the request is resubmitted, it is successful.
What is the best way to try to resolve this throughput issue?
A.
se a CloudHub autoscaling policy to add CloudHub workers
B.
Use a CloudHub autoscaling policy to increase the size of the CloudHub worker
C.
Increase the size of the CloudHub worker(s)
D.
Increase the number of CloudHub workers
Increase the number of CloudHub workers
Explanation: Explanation
Correct Answer: Increase the size of the CloudHub worker(s)
*****************************************
The key details that we can take out from the given scenario are:
>> API implementation uses a database bulk insert command to submit all the purchase
data to a database
>> JDBC driver processes the data into a set of several temporary disk files on the
CloudHub worker
>> Sometimes a request fails and the logs show a message indicating an out-of-file-space
message
Based on above details:
>> Both auto-scaling options does NOT help because we cannot set auto-scaling rules
based on error messages. Auto-scaling rules are kicked-off based on CPU/Memory usages
and not due to some given error or disk space issues.
>> Increasing the number of CloudHub workers also does NOT help here because the
reason for the failure is not due to performance aspects w.r.t CPU or Memory. It is due to
disk-space.
>> Moreover, the API is doing bulk insert to submit the received batch data. Which means,
all data is handled by ONE worker only at a time. So, the disk space issue should be
tackled on "per worker" basis. Having multiple workers does not help as the batch may still
fail on any worker when disk is out of space on that particular worker.
Therefore, the right way to deal this issue and resolve this is to increase the vCore size of
the worker so that a new worker with more disk space will be provisioned.
An API has been updated in Anypoint exchange by its API producer from version 3.1.1 to
3.2.0 following accepted semantic versioning practices and the changes have been
communicated via the APIs public portal. The API endpoint does NOT change in the new
version. How should the developer of an API client respond to this change?
A.
The API producer should be requested to run the old version in parallel with the new one
B.
The API producer should be contacted to understand the change to existing functionality
C.
The API client code only needs to be changed if it needs to take advantage of the new features
D.
The API clients need to update the code on their side and need to do full regression
The API client code only needs to be changed if it needs to take advantage of the new features
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
What are the major benefits of MuleSoft proposed IT Operating Model?
A.
1. Decrease the IT delivery gap
2. Meet various business demands without increasing the IT capacity
3. Focus on creation of reusable assets first. Upon finishing creation of all the possible
assets then inform the LOBs in the organization to start using them
B.
1. Decrease the IT delivery gap
2. Meet various business demands by increasing the IT capacity and forming various IT
departments
3. Make consumption of assets at the rate of production
C.
1. Decrease the IT delivery gap
2. Meet various business demands without increasing the IT capacity
3. Make consumption of assets at the rate of production
1. Decrease the IT delivery gap
2. Meet various business demands without increasing the IT capacity
3. Make consumption of assets at the rate of production
Explanation: Explanation
Correct Answer:
1. Decrease the IT delivery gap
2. Meet various business demands without increasing the IT capacity
3. Make consumption of assets at the rate of production.
*****************************************
Reference: https://www.youtube.com/watch?v=U0FpYMnMjmM
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