MCPA-Level-1-Maintenance Sample Questions Answers

Explanation

Correct Answer: 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.

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

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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.

Explanation

Correct Answer: Approve the client application request for the chosen SLA tier

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>> Only approving the client application request for the chosen SLA tier can be automated

>> Rest of the provided options are not valid

Explanation

Correct Answer: The API policy is defined in API Manager for a specific API instance, and then ONLY applied to the specific API instance.

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>> Once our API specifications are ready and published to Exchange, we need to visit API Manager and register an API instance for each API.

>> API Manager is the place where management of API aspects takes place like addressing NFRs by enforcing policies on them.

>> We can create multiple instances for a same API and manage them differently for different purposes.

>> One instance can have a set of API policies applied and another instance of same API can have different set of policies applied for some other purpose.

>> These APIs and their instances are defined PER environment basis. So, one need to manage them seperately in each environment.

>> We can ensure that same configuration of API instances (SLAs, Policies etc..) gets promoted when promoting to higher environments using platform feature. But this is optional only. Still one can change them per environment basis if they have to.

>> Runtime Manager is the place to manage API Implementations and their Mule Runtimes but NOT APIs itself. Though API policies gets executed in Mule Runtimes, We CANNOT enforce API policies in Runtime Manager. We would need to do that via API Manager only for a cherry picked instance in an environment.

So, based on these facts, right statement in the given choices is - "The API policy is defined in API Manager for a specific API instance, and then ONLY applied to the specific API instance".

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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Explanation

Correct Answer: Create separate Anypoint VPCs for non-production and production environments, then configure connections to the backend systems in the corresponding customer-hosted environments.

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>> Creating different Business Groups does NOT make any difference w.r.t accessing the non-prod and prod customer-hosted environments. Still they will be accessing from both Business Groups unless process network restrictions are put in place.

>> We need to modify or couple the Mule Application Implementations with the environment. In fact, we should never implements application coupled with environments by binding them in the properties. Only basic things like endpoint URL etc should be bundled in properties but not environment level access restrictions.

>> IP addresses on CloudHub are dynamic until unless a special static addresses are assigned. So it is not possible to setup firewall rules in customer-hosted infrastrcture. More over, even if static IP addresses are assigned, there could be 100s of applications running on cloudhub and setting up rules for all of them would be a hectic task, non-maintainable and definitely got a good practice.

>> The best practice recommended by Mulesoft (In fact any cloud provider), is to have your Anypoint VPCs seperated for Prod and Non-Prod and perform the VPC peering or VPN tunneling for these Anypoint VPCs to respective Prod and Non-Prod customer-hosted environment networks.

Explanation

Correct Answer: The API consumer creates an API client, which sends API invocations to an API such that they are processed by an API implementation

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Terminology:

>> API Client - It is a piece of code or program the is written to invoke an API

>> API Consumer - An owner/entity who owns the API Client. API Consumers write API clients.

>> API - The provider of the API functionality. Typically an API Instance on API Manager where they are managed and operated.

>> API Implementation - The actual piece of code written by API provider where the functionality of the API is implemented. Typically, these are Mule Applications running on Runtime Manager.

Explanation

Correct Answer: The CloudHub shared load balancer

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The scenario in this question can be split as below:

>> There are 3 CloudHub workers (So, there are already good number of workers to handle high volume of requests)

>> The workers are not using static IP addresses (So, one CANNOT use customer load-balancing solutions without static IPs)

>> Looking for most cost-effective component to load balance the client requests among the workers.

Based on the above details given in the scenario:

>> Runtime autoscaling is NOT at all cost-effective as it incurs extra cost. Most over, there are already 3 workers running which is a good number.

>> We cannot go for a customer-hosted load balancer as it is also NOT most cost-effective (needs custom load balancer to maintain and licensing) and same time the Mule App is not having Static IP Addresses which limits from going with custom load balancing.

>> An API Proxy is irrelevant there as it has no role to play w.r.t handling high volumes or load balancing.

So, the only right option to go with and fits the purpose of scenario being most cost-effective is - using a CloudHub Shared Load Balancer.

Explanation

Correct Answer: 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.

Explanation

Correct Answer: Apply a JSON threat protection policy to all APIs to detect potential threat vectors

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>> Usually, if the APIs are designed and developed for specific consumers (known consumers/customers) then we would IP Whitelist the same to ensure that traffic only comes from them.

>> However, as this scenario states that the APIs are publicly available and being used by so many mobile and web applications, it is NOT possible to identify and blacklist all possible bad actors.

>> So, JSON threat protection policy is the best chance to prevent any bad JSON payloads from such bad actors.

Explanation

Correct Answer: When the API implementation changes the structure of the request or response messages

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>> RAML definition usually needs to be touched only when there are changes in the request/response schemas or in any traits on API.

>> It need not be modified for any internal changes in API implementation like performance tuning, backend system migrations etc..

Explanation

Correct Answer: When development is already organized into several independent initiatives or groups

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>> It would require lot of process effort in an organization to have a single C4E team coordinating with multiple already organized development teams which are into several independent initiatives. A single C4E works well with different teams having at least a common initiative. So, in this scenario, federated C4E works well instead of centralized C4E.

Explanation

Correct Answer: Create reusable assets, Make them discoverable so that LOB teams can self-serve and browse the APIs, Get active feedback and usage metrics.

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Explanation

Correct Answer: Allow System APIs to return data that is NOT currently required by the identified Process or Experience APIs.

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>> All customizations for the end-user application should be handled in "Experience API" only. Not in Process API

>> We should use tiered approach but NOT always by creating exactly one API for each of the 3 layers. Experience APIs might be one but Process APIs and System APIs are often more than one. System APIs for sure will be more than one all the time as they are the smallest modular APIs built in front of end systems.

>> Process APIs can call System APIs as well as other Process APIs. There is no such anti-design pattern in API-Led connectivity saying Process APIs should not call other Process APIs.

So, the right answer in the given set of options that makes sense as per API-Led connectivity principles is to allow System APIs to return data that is NOT currently required by the identified Process or Experience APIs. This way, some future Process APIs can make use of that data from System APIs and we need NOT touch the System layer APIs again and again.

Explanation

Correct Answer: 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.

Explanation

Correct Answer: 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-Another-Cloudhub-Application

https://help.mulesoft.com/s/question/0D52T00004mXXULSA4/multiple-http-listerners-on-cloudhub-one-with-port-9090

Explanation

Correct Answer: SLA-based rate limiting

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>> Client Id enforement policy is a "Compliance" related NFR and does not help in maintaining the "Quality of Service (QoS)". It CANNOT and NOT meant for protecting the backend systems from scalability challenges.

>> IP Whitelisting and OAuth 2.0 token enforcement are "Security" related NFRs and again does not help in maintaining the "Quality of Service (QoS)". They CANNOT and are NOT meant for protecting the backend systems from scalability challenges.

Rate Limiting, Rate Limiting-SLA, Throttling, Spike Control are the policies that are "Quality of Service (QOS)" related NFRs and are meant to help in protecting the backend systems from getting overloaded.

https://dzone.com/articles/how-to-secure-apis

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.

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Explanation

Correct Answer: 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.

Explanation

Correct Answer: The credentials provided by the IdP for identity management

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