Valid MCPA-Level-1-Maintenance Test Sims & MCPA-Level-1-Maintenance Reliable Test Duration by pekabab554 pekabab554

A minor mistake may result you to lose chance even losing out on your MCPA-Level-1-Maintenance exam. So we hold responsible tents when compiling the MCPA-Level-1-Maintenance learning guide. The principles of our MCPA-Level-1-Maintenancepractice materials can be expressed in words like clarity, correction and completeness. Experts expressed their meaning with clarity by knowledgeable and understandable words which cannot be misunderstood.

To be eligible to take the MCPA-Level-1-Maintenance exam, candidates must have already passed the MCPA-Level-1 exam and hold a current MCPA-Level-1 certification. This exam is intended for architects, developers, and technical professionals who are responsible for designing, building, and maintaining MuleSoft integrations. The MCPA-Level-1-Maintenance exam is designed to test the candidate's knowledge and skills related to MuleSoft's Anypoint Platform, including its architecture, design, deployment, and management.

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You can also set the number of MuleSoft MCPA-Level-1-Maintenance dumps questions to attempt in the practice test and time as well. The web-based MuleSoft MCPA-Level-1-Maintenance practice test software needs an active internet connection and can be accessed through all major browsers like Chrome, Edge, Firefox, Opera, and Safari. Our Desktop-based MuleSoft MCPA-Level-1-Maintenance Practice Exam Software is very suitable for those who don't have an internet connection. You can download and install it within a few minutes on Windows-based PCs only and start preparing for the MuleSoft Certified Platform Architect - Level 1 MAINTENANCE exam.

The MCPA-Level-1-Maintenance exam is a multiple-choice exam that contains 40 questions. The duration of the exam is 60 minutes, and the passing score is 70%. The exam is delivered online and can be taken from anywhere in the world. The exam fee is $250, and the exam is available in English, Spanish, and Portuguese.

MuleSoft Certified Platform Architect - Level 1 MAINTENANCE Sample Questions (Q23-Q28):

NEW QUESTION # 23
An API implementation is updated. When must the RAML definition of the API also be updated?

A. When the API implementation is optimized to improve its average response timeB. When the API implementation changes from interacting with a legacy backend system deployed on-premises to a modern, cloud-based (SaaS) systemC. When the API implementation changes the structure of the request or response messagesD. When the API implementation is migrated from an older to a newer version of the Mule runtime

Answer: C

Explanation:
When the API implementation changes the structure of the request or response messages
*****************************************
>> 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..

NEW QUESTION # 24
An API experiences a high rate of client requests (TPS) vwth small message paytoads. How can usage limits be imposed on the API based on the type of client application?

A. Use a cross-origin resource sharing (CORS) policy to limit resource sharing between client applications, configured by the client application typeB. Use a rate limiting policy and a client ID enforcement policy, each configured by the client application typeC. Use a spike control policy that limits the number of requests for each client application typeD. Use an SLA-based rate limiting policy and assign a client application to a matching SLA tier based on its type

Answer: D

Explanation:
Use an SLA-based rate limiting policy and assign a client application to a matching SLA tier
based on its type.
*****************************************
>> SLA tiers will come into play whenever any limits to be imposed on APIs based on client type

NEW QUESTION # 25
A company has started to create an application network and is now planning to implement a Center for Enablement (C4E) organizational model. What key factor would lead the company to decide upon a federated rather than a centralized C4E?

A. When there are a large number of existing common assets shared by development teamsB. When the majority of the applications in the application network are cloud basedC. When various teams responsible for creating APIs are new to integration and hence need extensive trainingD. When development is already organized into several independent initiatives or groups

Answer: D

Explanation:
When development is already organized into several independent initiatives or groups
*****************************************
>> 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.

NEW QUESTION # 26
An organization has implemented a Customer Address API to retrieve customer address information. This API has been deployed to multiple environments and has been configured to enforce client IDs everywhere.
A developer is writing a client application to allow a user to update their address. The developer has found the Customer Address API in Anypoint Exchange and wants to use it in their client application.
What step of gaining access to the API can be performed automatically by Anypoint Platform?

A. Create a new application in Anypoint Exchange for requesting access to the APIB. Approve the client application request for the chosen SLA tierC. Request access to the appropriate API Instances deployed to multiple environments using the client application's credentialsD. Modify the client application to call the API using the client application's credentials

Answer: B

Explanation:
Approve the client application request for the chosen SLA tier
*****************************************
>> Only approving the client application request for the chosen SLA tier can be automated
>> Rest of the provided options are not valid

NEW QUESTION # 27
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. Increase the number of CloudHub workersB. Increase the size of the CloudHub worker(s)C. se a CloudHub autoscaling policy to add CloudHub workersD. Use a CloudHub autoscaling policy to increase the size of the CloudHub worker

Answer: A

Explanation:
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.

NEW QUESTION # 28
......

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