[Oct-2023] The Best MuleSoft Certified Platform Architect Study Guide for the MCPA-Level-1-Maintenance Exam
MCPA-Level-1-Maintenance certification guide Q&A from Training Expert TestValid
NEW QUESTION # 28
A system API is deployed to a primary environment as well as to a disaster recovery (DR) environment, with different DNS names in each environment. A process API is a client to the system API and is being rate limited by the system API, with different limits in each of the environments. The system API's DR environment provides only 20% of the rate limiting offered by the primary environment. What is the best API fault-tolerant invocation strategy to reduce overall errors in the process API, given these conditions and constraints?
- A. Invoke the system API deployed to the primary environment; add timeout and retry logic to the process API to avoid intermittent failures; if it still fails, invoke the system API deployed to the DR environment
- B. Invoke the system API deployed to the primary environment; add retry logic to the process API to handle intermittent failures by invoking the system API deployed to the DR environment
- C. Invoke the system API deployed to the primary environment; add timeout and retry logic to the process API to avoid intermittent failures; if it still fails, invoke a copy of the process API deployed to the DR environment
- D. In parallel, invoke the system API deployed to the primary environment and the system API deployed to the DR environment; add timeout and retry logic to the process API to avoid intermittent failures; add logic to the process API to combine the results
Answer: A
Explanation:
Invoke the system API deployed to the primary environment; add timeout and retry logic to the process API to avoid intermittent failures; if it still fails, invoke the system API deployed to the DR environment
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There is one important consideration to be noted in the question which is - System API in DR environment provides only 20% of the rate limiting offered by the primary environment. So, comparitively, very less calls will be allowed into the DR environment API opposed to its primary environment. With this in mind, lets analyse what is the right and best fault-tolerant invocation strategy.
1. Invoking both the system APIs in parallel is definitely NOT a feasible approach because of the 20% limitation we have on DR environment. Calling in parallel every time would easily and quickly exhaust the rate limits on DR environment and may not give chance to genuine intermittent error scenarios to let in during the time of need.
2. Another option given is suggesting to add timeout and retry logic to process API while invoking primary environment's system API. This is good so far. However, when all retries failed, the option is suggesting to invoke the copy of process API on DR environment which is not right or recommended. Only system API is the one to be considered for fallback and not the whole process API. Process APIs usually have lot of heavy orchestration calling many other APIs which we do not want to repeat again by calling DR's process API. So this option is NOT right.
3. One more option given is suggesting to add the retry (no timeout) logic to process API to directly retry on DR environment's system API instead of retrying the primary environment system API first. This is not at all a proper fallback. A proper fallback should occur only after all retries are performed and exhausted on Primary environment first. But here, the option is suggesting to directly retry fallback API on first failure itself without trying main API. So, this option is NOT right too.
This leaves us one option which is right and best fit.
- Invoke the system API deployed to the primary environment
- Add Timeout and Retry logic on it in process API
- If it fails even after all retries, then invoke the system API deployed to the DR environment.
NEW QUESTION # 29
What Mule application deployment scenario requires using Anypoint Platform Private Cloud Edition or Anypoint Platform for Pivotal Cloud Foundry?
- A. When ALL backend systems in the application network are deployed in the organization's intranet
- 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 it Is required to make ALL applications highly available across multiple data centers
Answer: C
Explanation:
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.
NEW QUESTION # 30
A new upstream API Is being designed to offer an SLA of 500 ms median and 800 ms maximum (99th percentile) response time. The corresponding API implementation needs to sequentially invoke 3 downstream APIs of very similar complexity.
The first of these downstream APIs offers the following SLA for its response time: median: 100 ms, 80th percentile: 500 ms, 95th percentile: 1000 ms.
If possible, how can a timeout be set in the upstream API for the invocation of the first downstream API to meet the new upstream API's desired SLA?
- A. Do not set a timeout; the Invocation of this API Is mandatory and so we must wait until it responds
- B. Set a timeout of 50 ms; this times out more invocations of that API but gives additional room for retries
- C. Set a timeout of 100 ms; that leaves 400 ms for the other two downstream APIs to complete
- D. No timeout is possible to meet the upstream API's desired SLA; a different SLA must be negotiated with the first downstream API or invoke an alternative API
Answer: C
Explanation:
Set a timeout of 100ms; that leaves 400ms for other two downstream APIs to complete
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Key details to take from the given scenario:
>> Upstream API's designed SLA is 500ms (median). Lets ignore maximum SLA response times.
>> This API calls 3 downstream APIs sequentially and all these are of similar complexity.
>> The first downstream API is offering median SLA of 100ms, 80th percentile: 500ms; 95th percentile:
1000ms.
Based on the above details:
>> We can rule out the option which is suggesting to set 50ms timeout. Because, if the median SLA itself being offered is 100ms then most of the calls are going to timeout and time gets wasted in retried them and eventually gets exhausted with all retries. Even if some retries gets successful, the remaining time wont leave enough room for 2nd and 3rd downstream APIs to respond within time.
>> The option suggesting to NOT set a timeout as the invocation of this API is mandatory and so we must wait until it responds is silly. As not setting time out would go against the good implementation pattern and moreover if the first API is not responding within its offered median SLA 100ms then most probably it would either respond in 500ms (80th percentile) or 1000ms (95th percentile). In BOTH cases, getting a successful response from 1st downstream API does NO GOOD because already by this time the Upstream API SLA of
500 ms is breached. There is no time left to call 2nd and 3rd downstream APIs.
>> It is NOT true that no timeout is possible to meet the upstream APIs desired SLA.
As 1st downstream API is offering its median SLA of 100ms, it means MOST of the time we would get the responses within that time. So, setting a timeout of 100ms would be ideal for MOST calls as it leaves enough room of 400ms for remaining 2 downstream API calls.
NEW QUESTION # 31
An organization makes a strategic decision to move towards an IT operating model that emphasizes consumption of reusable IT assets using modern APIs (as defined by MuleSoft).
What best describes each modern API in relation to this new IT operating model?
- A. Each modern API must be easy to consume, so should avoid complex authentication mechanisms such as SAML or JWT D
- B. Each modern API must be REST and HTTP based
- C. Each modem API must be treated like a product and designed for a particular target audience (for instance, mobile app developers)
- D. Each modern API has its own software development lifecycle, which reduces the need for documentation and automation
Answer: C
Explanation:
Explanation
Correct Answers:
1. Each modern API must be treated like a product and designed for a particular target audience (for instance mobile app developers)
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NEW QUESTION # 32
What Anypoint Platform Capabilities listed below fall under APIs and API Invocations/Consumers category?
Select TWO.
- A. API Operations and Management
- B. API Runtime Execution and Hosting
- C. API Consumer Engagement
- D. API Design and Development
Answer: D
Explanation:
Explanation
Correct Answers: API Design and Development and API Runtime Execution and Hosting
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>> API Design and Development - Anypoint Studio, Anypoint Design Center, Anypoint Connectors
>> API Runtime Execution and Hosting - Mule Runtimes, CloudHub, Runtime Services
>> API Operations and Management - Anypoint API Manager, Anypoint Exchange
>> API Consumer Management - API Contracts, Public Portals, Anypoint Exchange, API Notebooks
Explanation
Correct Answers: API Operations and Management and API Consumer Engagement
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>> API Design and Development - Anypoint Studio, Anypoint Design Center, Anypoint Connectors
>> API Runtime Execution and Hosting - Mule Runtimes, CloudHub, Runtime Services
>> API Operations and Management - Anypoint API Manager, Anypoint Exchange
>> API Consumer Management - API Contracts, Public Portals, Anypoint Exchange, API Notebooks
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NEW QUESTION # 33
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. An overall tower usage of resources because each fine-grained API consumes less resources
- B. A higher number of discoverable API-related assets in the application network
- C. A decrease in the number of connections within the application network supporting the business process
- D. A better response time for the end user as a result of the APIs being smaller in scope and complexity
Answer: B
Explanation:
A higher number of discoverable API-related assets in the application network.
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>> 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 fine-grained 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.
NEW QUESTION # 34
An organization has created an API-led architecture that uses various API layers to integrate mobile clients with a backend system. The backend system consists of a number of specialized components and can be accessed via a REST API. The process and experience APIs share the same bounded-context model that is different from the backend data model. What additional canonical models, bounded-context models, or anti-corruption layers are best added to this architecture to help process data consumed from the backend system?
- A. Create an anti-corruption layer for every API to perform transformation for every data model to match each other, and let data simply travel between APIs to avoid the complexity and overhead of building canonical models
- B. Create a bounded-context model for the system layer to closely match the backend data model, and add an anti-corruption layer to let the different bounded contexts cooperate across the system and process layers
- C. Create a bounded-context model for every layer and overlap them when the boundary contexts overlap, letting API developers know about the differences between upstream and downstream data models
- D. Create a canonical model that combines the backend and API-led models to simplify and unify data models, and minimize data transformations.
Answer: B
Explanation:
Create a bounded-context model for the system layer to closely match the backend data model, and add an anti-corruption layer to let the different bounded contexts cooperate across the system and process layers
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>> Canonical models are not an option here as the organization has already put in efforts and created bounded-context models for Experience and Process APIs.
>> Anti-corruption layers for ALL APIs is unnecessary and invalid because it is mentioned that experience and process APIs share same bounded-context model. It is just the System layer APIs that need to choose their approach now.
>> So, having an anti-corruption layer just between the process and system layers will work well. Also to speed up the approach, system APIs can mimic the backend system data model.
NEW QUESTION # 35
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. iPaaS provisioned customer-hosted runtime plane and MuleSoft-hosted control plane
- C. MuleSoft-hosted runtime plane and customer-hosted control plane
- D. Manually provisioned customer-hosted runtime plane and MuleSoft-hosted control plane
Answer: A
Explanation:
Manually provisioned customer-hosted runtime plane and customer-hosted control plane
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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 customer-hosted control plane.
NEW QUESTION # 36
Which of the following sequence is correct?
- A. API Consumer requests access to API >> API Client implementes logic to call an API >> API routes the request to >> API Implementation
- B. API Client implementes logic to call an API >> API Consumer requests access to API >> API routes the request to >> API Implementation
- C. API Client implementes logic to call an API >> API Consumer requests access to API >> API Implementation routes the request to >> API
- D. API Consumer implementes logic to call an API >> API Client requests access to API >> API Implementation routes the request to >> API
Answer: A
Explanation:
API Consumer requests access to API >> API Client implementes logic to call an API >> API routes the request to >> API Implementation
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>> API consumer does not implement any logic to invoke APIs. It is just a role. So, the option stating "API Consumer implementes logic to call an API" is INVALID.
>> API Implementation does not route any requests. It is a final piece of logic where functionality of target systems is exposed. So, the requests should be routed to the API implementation by some other entity. So, the options stating "API Implementation routes the request to >> API" is INVALID
>> The statements in one of the options are correct but sequence is wrong. The sequence is given as "API Client implementes logic to call an API >> API Consumer requests access to API >> API routes the request to
>> API Implementation". Here, the statements in the options are VALID but sequence is WRONG.
>> Right option and sequence is the one where API consumer first requests access to API on Anypoint Exchange and obtains client credentials. API client then writes logic to call an API by using the access client credentials requested by API consumer and the requests will be routed to API implementation via the API which is managed by API Manager.
NEW QUESTION # 37
Which layer in the API-led connectivity focuses on unlocking key systems, legacy systems, data sources etc and exposes the functionality?
- A. System Layer
- B. Process Layer
- C. Experience Layer
Answer: A
Explanation:
System Layer
The APIs used in an API-led approach to connectivity fall into three categories:
System APIs - these usually access the core systems of record and provide a means of insulating the user from the complexity or any changes to the underlying systems. Once built, many users, can access data without any need to learn the underlying systems and can reuse these APIs in multiple projects.
Process APIs - These APIs interact with and shape data within a single system or across systems (breaking down data silos) and are created here without a dependence on the source systems from which that data originates, as well as the target channels through which that data is delivered.
Experience APIs - Experience APIs are the means by which data can be reconfigured so that it is most easily consumed by its intended audience, all from a common data source, rather than setting up separate point-to-point integrations for each channel. An Experience API is usually created with API-first design principles where the API is designed for the specific user experience in mind.
NEW QUESTION # 38
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. Permanently increase the number of CloudHub workers by four times (4x) to eight (8) CloudHub workers
- C. Use a horizontal CloudHub autoscaling policy that triggers on CPU utilization greater than 70%
- D. Use a vertical CloudHub autoscaling policy that triggers on CPU utilization greater than 70%
Answer: C
Explanation:
Use a horizontal CloudHub autoscaling policy that triggers on CPU utilization greater than
70%
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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.
NEW QUESTION # 39
What is most likely NOT a characteristic of an integration test for a REST API implementation?
- A. The test runs immediately after the Mule application has been compiled and packaged
- B. The test prepares a known request payload and validates the response payload
- C. The test is triggered by an external HTTP request
- D. The test needs all source and/or target systems configured and accessible
Answer: A
Explanation:
The test runs immediately after the Mule application has been compiled and packaged
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>> Integration tests are the last layer of tests we need to add to be fully covered.
>> These tests actually run against Mule running with your full configuration in place and are tested from external source as they work in PROD.
>> These tests exercise the application as a whole with actual transports enabled. So, external systems are affected when these tests run.
So, these tests do NOT run immediately after the Mule application has been compiled and packaged.
FYI... Unit Tests are the one that run immediately after the Mule application has been compiled and packaged.
NEW QUESTION # 40
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 a pragmatic approach with only limited isolation from the backend system is deemed appropriate
- B. When the corresponding backend system is expected to be replaced in the near future
- C. When the System API can be assigned to a bounded context with a corresponding data model
- D. When there is an existing Enterprise Data Model widely used across the organization
Answer: A
Explanation:
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 implementations of the Process API tier
NEW QUESTION # 41
In which layer of API-led connectivity, does the business logic orchestration reside?
- A. System Layer
- B. Experience Layer
- C. Process Layer
Answer: C
Explanation:
Process Layer
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>> Experience layer is dedicated for enrichment of end user experience. This layer is to meet the needs of different API clients/ consumers.
>> System layer is dedicated to APIs which are modular in nature and implement/ expose various individual functionalities of backend systems
>> Process layer is the place where simple or complex business orchestration logic is written by invoking one or many System layer modular APIs So, Process Layer is the right answer.
NEW QUESTION # 42
A code-centric API documentation environment should allow API consumers to investigate and execute API client source code that demonstrates invoking one or more APIs as part of representative scenarios.
What is the most effective way to provide this type of code-centric API documentation environment using Anypoint Platform?
- A. Ensure the APIs are well documented through their Anypoint Exchange entries and API Consoles and share these pages with all API consumers
- B. Make relevant APIs discoverable via an Anypoint Exchange entry
- C. Create API Notebooks and include them in the relevant Anypoint Exchange entries
- D. Enable mocking services for each of the relevant APIs and expose them via their Anypoint Exchange entry
Answer: C
Explanation:
Create API Notebooks and Include them in the relevant Anypoint exchange entries
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>> API Notebooks are the one on Anypoint Platform that enable us to provide code-centric API documentation
NEW QUESTION # 43
A Mule application exposes an HTTPS endpoint and is deployed to the CloudHub Shared Worker Cloud. All traffic to that Mule application must stay inside the AWS VPC.
To what TCP port do API invocations to that Mule application need to be sent?
- A. 0
- B. 1
- C. 2
- D. 3
Answer: D
Explanation:
8082
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>> 8091 and 8092 ports are to be used when keeping your HTTP and HTTPS app private to the LOCAL VPC respectively.
>> Above TWO ports are not for Shared AWS VPC/ Shared Worker Cloud.
>> 8081 is to be used when exposing your HTTP endpoint app to the internet through Shared LB
>> 8082 is to be used when exposing your HTTPS endpoint app to the internet through Shared LB So, API invocations should be sent to port 8082 when calling this HTTPS based app.
References:
https://docs.mulesoft.com/runtime-manager/cloudhub-networking-guide
https://help.mulesoft.com/s/article/Configure-Cloudhub-Application-to-Send-a-HTTPS-Request-Directly-to-Ano
https://help.mulesoft.com/s/question/0D52T00004mXXULSA4/multiple-http-listerners-on-cloudhub-one-with-p
NEW QUESTION # 44
An organization is deploying their new implementation of the OrderStatus System API to multiple workers in CloudHub. This API fronts the organization's on-premises Order Management System, which is accessed by the API implementation over an IPsec tunnel.
What type of error typically does NOT result in a service outage of the OrderStatus System API?
- A. The Order Management System is Inaccessible due to a network outage in the organization's on-premises data center
- B. API Manager has an extended outage during the initial deployment of the API implementation
- C. A CloudHub worker fails with an out-of-memory exception
- D. The AWS region goes offline with a major network failure to the relevant AWS data centers
Answer: C
Explanation:
A CloudHub worker fails with an out-of-memory exception.
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>> An AWS Region itself going down will definitely result in an outage as it does not matter how many workers are assigned to the Mule App as all of those in that region will go down. This is a complete downtime and outage.
>> Extended outage of API manager during initial deployment of API implementation will of course cause issues in proper application startup itself as the API Autodiscovery might fail or API policy templates and polices may not be downloaded to embed at the time of applicaiton startup etc... there are many reasons that could cause issues.
>> A network outage onpremises would of course cause the Order Management System not accessible and it does not matter how many workers are assigned to the app they all will fail and cause outage for sure.
The only option that does NOT result in a service outage is if a cloudhub worker fails with an out-of-memory exception. Even if a worker fails and goes down, there are still other workers to handle the requests and keep the API UP and Running. So, this is the right answer.
NEW QUESTION # 45
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