Mulesoft MCPA-LEVEL1 Online Practice Questions and Exam Preparation

Mulesoft MCPA-LEVEL1 Online Questions & Answers

• Question 1:

  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. Use a vertical CloudHub autoscaling policy that triggers on CPU utilization greater than 70%
  C. Permanently increase the number of CloudHub workers by four times (4x) to eight (8) CloudHub workers
  D. Use a horizontal CloudHub autoscaling policy that triggers on CPU utilization greater than 70%
  D. Use a horizontal CloudHub autoscaling policy that triggers on CPU utilization greater than 70%

  ---

  explanation:

  Explanation/Reference:

  Use a horizontal CloudHub autoscaling policy that triggers on CPU utilization greater than 70%

  *****************************************

  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.

• Question 2:

  What is typically NOT a function of the APIs created within the framework called API-led connectivity?

  A. They provide an additional layer of resilience on top of the underlying backend system, thereby insulating clients from extended failure of these systems.
  B. They allow for innovation at the user Interface level by consuming the underlying assets without being aware of how data Is being extracted from backend systems.
  C. They reduce the dependency on the underlying backend systems by helping unlock data from backend systems In a reusable and consumable way.
  D. They can compose data from various sources and combine them with orchestration logic to create higher level value.
  A. They provide an additional layer of resilience on top of the underlying backend system, thereby insulating clients from extended failure of these systems.

  ---

  explanation:

  Explanation/Reference:

  They provide an additional layer of resilience on top of the underlying backend system, thereby insulating clients from extended failure of these systems.

  *****************************************

  In API-led connectivity,

  >> Experience APIs - allow for innovation at the user interface level by consuming the underlying assets without being aware of how data is being extracted from backend systems.

  >> Process APIs - compose data from various sources and combine them with orchestration logic to create higher level value

  >> System APIs - reduce the dependency on the underlying backend systems by helping unlock data from backend systems in a reusable and consumable way. However, they NEVER promise that they provide an additional layer of

  resilience on top of the underlying backend system, thereby insulating clients from extended failure of these systems.

  https://dzone.com/articles/api-led-connectivity-with-mule

• Question 3:

  Refer to the exhibit.

  

  What is true when using customer-hosted Mule runtimes with the MuleSoft-hosted Anypoint Platform control plane (hybrid deployment)?

  A. Anypoint Runtime Manager initiates a network connection to a Mule runtime in order to deploy Mule applications
  B. The MuleSoft-hosted Shared Load Balancer can be used to load balance API invocations to the Mule runtimes
  C. API implementations can run successfully in customer-hosted Mule runtimes, even when they are unable to communicate with the control plane
  D. Anypoint Runtime Manager automatically ensures HA in the control plane by creating a new Mule runtime instance in case of a node failure
  C. API implementations can run successfully in customer-hosted Mule runtimes, even when they are unable to communicate with the control plane

  ---

  explanation:

  Explanation/Reference:

  API implementations can run successfully in customer-hosted Mule runtimes, even when they are unable to communicate with the control plane.

  *****************************************

  >> We CANNOT use Shared Load balancer to load balance APIs on customer hosted runtimes

  

  >> For Hybrid deployment models, the on-premises are first connected to Runtime Manager using Runtime Manager agent. So, the connection is initiated first from On- premises to Runtime Manager. Then all control can be done from Runtime Manager. >> Anypoint Runtime Manager CANNOT ensure automatic HA. Clusters/Server Groups etc should be configured before hand.

  Only TRUE statement in the given choices is, API implementations can run successfully in customer-hosted Mule runtimes, even when they are unable to communicate with the control plane. There are several references below to justify this statement.

  References: https://docs.mulesoft.com/runtime-manager/deployment-strategies#hybrid-deployments https://help.mulesoft.com/s/article/On-Premise-Runtimes-Disconnected-From-US-Control- Plane-June-18th-2018 https://help.mulesoft.com/s/article/Runtime-Manager-cannot-manage-On-Prem-Applications-and-Servers-from-US-Control-Plane-June-25th-2019 https://help.mulesoft.com/s/article/On-premise-Runtimes-Appear-Disconnected-in-RuntimeManager-May-29th-2018

  

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• Question 4:

  When designing an upstream API and its implementation, the development team has been advised to NOT set timeouts when invoking a downstream API, because that downstream API has no SLA that can be relied upon. This is the only downstream API dependency of that upstream API.

  Assume the downstream API runs uninterrupted without crashing. What is the impact of this advice?

  A. An SLA for the upstream API CANNOT be provided
  B. The invocation of the downstream API will run to completion without timing out
  C. A default timeout of 500 ms will automatically be applied by the Mule runtime in which the upstream API implementation executes
  D. A toad-dependent timeout of less than 1000 ms will be applied by the Mule runtime in which the downstream API implementation executes
  A. An SLA for the upstream API CANNOT be provided

  ---

  explanation:

  Explanation/Reference:

  An SLA for the upstream API CANNOT be provided.

  *****************************************

  >> First thing first, the default HTTP response timeout for HTTP connector is 10000 ms (10 seconds). NOT 500 ms.

  >> Mule runtime does NOT apply any such "load-dependent" timeouts. There is no such behavior currently in Mule.

  >> As there is default 10000 ms time out for HTTP connector, we CANNOT always guarantee that the invocation of the downstream API will run to completion without timing out due to its unreliable SLA times. If the response time crosses 10

  seconds then the request may time out.

  The main impact due to this is that a proper SLA for the upstream API CANNOT be provided.

  Reference: https://docs.mulesoft.com/http-connector/1.5/http-documentation#parameters-3

• Question 5:

  What do the API invocation metrics provided by Anypoint Platform provide?

  A. ROI metrics from APIs that can be directly shared with business users
  B. Measurements of the effectiveness of the application network based on the level of reuse
  C. Data on past API invocations to help identify anomalies and usage patterns across various APIs
  D. Proactive identification of likely future policy violations that exceed a given threat threshold
  C. Data on past API invocations to help identify anomalies and usage patterns across various APIs

  ---

  explanation:

  Explanation/Reference:

  Data on past API invocations to help identify anomalies and usage patterns across various APIs

  *****************************************

  API Invocation metrics provided by Anypoint Platform:

  >> Does NOT provide any Return Of Investment (ROI) related information. So the option suggesting it is OUT.

  >> Does NOT provide any information w.r.t how APIs are reused, whether there is effective usage of APIs or not etc...

  >> Does NOT prodive any prediction information as such to help us proactively identify any future policy violations.

  So, the kind of data/information we can get from such metrics is on past API invocations to help identify anomalies and usage patterns across various APIs.

  Reference:

  https://usermanual.wiki/Document/APAAppNetstudentManual02may2018.991784750.pdf

• Question 6:

  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
  D. Increase the number of CloudHub workers

  ---

  explanation:

  Explanation/Reference:

  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.

• Question 7:

  What is true about automating interactions with Anypoint Platform using tools such as Anypoint Platform REST APIs, Anypoint CU, or the Mule Maven plugin?

  A. Access to Anypoint Platform APIs and Anypoint CU can be controlled separately through the roles and permissions in Anypoint Platform, so that specific users can get access to Anypoint CLI white others get access to the platform APIs
  B. Anypoint Platform APIs can ONLY automate interactions with CloudHub, while the Mule Maven plugin is required for deployment to customer-hosted Mule runtimes
  C. By default, the Anypoint CLI and Mule Maven plugin are NOT included in the Mule runtime, so are NOT available to be used by deployed Mule applications
  D. API policies can be applied to the Anypoint Platform APIs so that ONLY certain LOBs have access to specific functions
  C. By default, the Anypoint CLI and Mule Maven plugin are NOT included in the Mule runtime, so are NOT available to be used by deployed Mule applications

  ---

  explanation:

  Explanation/Reference:

  By default, the Anypoint CLI and Mule Maven plugin are NOT included in the Mule runtime, so are NOT available to be used by deployed Mule applications ***************************************** >> We CANNOT apply API policies to the Anypoint Platform APIs like we can do on our custom written API instances. So, option suggesting this is FALSE. >> Anypoint Platform APIs can be used for automating interactions with both CloudHub and customer-hosted Mule runtimes. Not JUST the CloudHub. So, option opposing this is FALSE.

  >> Mule Maven plugin is NOT mandatory for deployment to customer-hosted Mule runtimes. It just helps your CI/CD to have smoother automation. But not a compulsory requirement to deploy. So, option opposing this is FALSE. >> We DO NOT have any such special roles and permissions on the platform to separately control access for some users to have Anypoint CLI and others to have Anypoint Platform APIs. With proper general roles/permissions (API Owner, Cloudhub Admin etc..), one can use any of the options (Anypoint CLI or Platform APIs). So, option suggesting this is FALSE. Only TRUE statement given in the choices is that - Anypoint CLI and Mule Maven plugin are NOT included in the Mule runtime, so are NOT available to be used by deployed Mule applications. Maven is part of Studio or you can use other Maven installation for development. CLI is convenience only. It is one of many ways how to install app to the runtime. These are definitely NOT part of anything except your process of deployment or automation.

• Question 8:

  Due to a limitation in the backend system, a system API can only handle up to 500 requests per second. What is the best type of API policy to apply to the system API to avoid overloading the backend system?

  A. Rate limiting
  B. HTTP caching
  C. Rate limiting - SLA based
  D. Spike control
  D. Spike control

  ---

  explanation:

  Explanation/Reference:

  Spike control

  *****************************************

  >> First things first, HTTP Caching policy is for purposes different than avoiding the backend system from overloading. So this is OUT.

  >> Rate Limiting and Throttling/ Spike Control policies are designed to limit API access, but have different intentions.

  >> Rate limiting protects an API by applying a hard limit on its access. >> Throttling/ Spike Control shapes API access by smoothing spikes in traffic.

  That is why, Spike Control is the right option.

• Question 9:

  An organization wants to make sure only known partners can invoke the organization's APIs. To achieve this security goal, the organization wants to enforce a Client ID Enforcement policy in API Manager so that only registered partner applications can invoke the organization's APIs. In what type of API implementation does MuleSoft recommend adding an API proxy to enforce the Client ID Enforcement policy, rather than embedding the policy directly in the application's JVM?

  A. A Mule 3 application using APIkit
  B. A Mule 3 or Mule 4 application modified with custom Java code
  C. A Mule 4 application with an API specification
  D. A Non-Mule application
  D. A Non-Mule application

  ---

  explanation:

  Explanation/Reference:

  A Non-Mule application

  *****************************************

  >> All type of Mule applications (Mule 3/ Mule 4/ with APIkit/ with Custom Java Code etc) running on Mule Runtimes support the Embedded Policy Enforcement on them. >> The only option that cannot have or does not support embedded

  policy enforcement and must have API Proxy is for Non-Mule Applications.

  So, Non-Mule application is the right answer.

• Question 10:

  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
  B. TRUE

  ---

  explanation:

  Explanation/Reference:

  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.