MuleSoft MCPA-Level-1-Maintenance - MuleSoft Certified Platform Architect - Level 1 MAINTENANCE

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.

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: By refining the response definitions by adding the x-ratelimit-* response headers with description, type, and example

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References:

https://docs.mulesoft.com/api-manager/2.x/rate-limiting-and-throttling#response-headers

https://docs.mulesoft.com/api-manage r/2.x/rate-limiting-and-throttling-sla-based-policies#response-headers

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.

Explanation

Correct Answer: Anypoint Runtime Fabric

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>> When a customer is already having an Azure environment, It is not at all an ideal approach to go with hybrid model having some Mule Runtimes hosted on Azure and some on MuleSoft. This is unnecessary and useless.

>> CloudHub is a Mulesoft-hosted Runtime plane and is on AWS. We cannot customize to point CloudHub to customer's Azure environment.

>> Anypoint Platform for Pivotal Cloud Foundry is specifically for infrastructure provided by Pivotal Cloud Foundry

>> Anypoint Runtime Fabric is right answer as it is a container service that automates the deployment and orchestration of Mule applications and API gateways. Runtime Fabric runs within a customer-managed infrastructure on AWS, Azure, virtual machines (VMs), and bare-metal servers.

-Some of the capabilities of Anypoint Runtime Fabric include:

-Isolation between applications by running a separate Mule runtime per application.

-Ability to run multiple versions of Mule runtime on the same set of resources.

-Scaling applications across multiple replicas.

-Automated application fail-over.

-Application management with Anypoint Runtime Manager.

Explanation

Correct Answer: 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.

Explanation

Correct Answer: Increase the size of the CloudHub worker(s)

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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.

Explanation

Correct Answer: When a new required field is added to the method called by the API client

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>> Generally, the logic on API clients need to be updated when the API contract breaks.

>> When a new method or a child method is added to an API , the API client does not break as it can still continue to use its existing method. So these two options are out.

>> We are left for two more where "datatype of the response if changed" and "a new required field is added".

>> Changing the datatype of the response does break the API contract. However, the question is insisting on the "invocation" logic and not about the response handling logic. The API client can still invoke the API successfully and receive the response but the response will have a different datatype for some field.

>> Adding a new required field will break the API's invocation contract. When adding a new required field, the API contract breaks the RAML or API spec agreement that the API client/API consumer and API provider has between them. So this requires the API client invocation logic to also be updated.

Explanation

Correct Answer: The FQDNs are determined by the application name chosen, IRRESPECTIVE of the region

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>> When deploying applications to Shared Worker Cloud, the FQDN are always determined by application name chosen.

>> It does NOT matter what region the app is being deployed to.

>> Although it is fact and true that the generated FQDN will have the region included in it (Ex: exp-salesorder-api.au-s1.cloudhub.io), it does NOT mean that the same name can be used when deploying to another CloudHub region.

>> Application name should be universally unique irrespective of Region and Organization and solely determines the FQDN for Shared Load Balancers.