MULESOFT-PLATFORM-ARCHITECT-I HIGH QUALITY: SALESFORCE CERTIFIED MULESOFT PLATFORM ARCHITECT I - HIGH PASS-RATE SALESFORCE POSITIVE MULESOFT-PLATFORM-ARCHITECT-I FEEDBACK

MuleSoft-Platform-Architect-I High Quality: Salesforce Certified MuleSoft Platform Architect I - High Pass-Rate Salesforce Positive MuleSoft-Platform-Architect-I Feedback

MuleSoft-Platform-Architect-I High Quality: Salesforce Certified MuleSoft Platform Architect I - High Pass-Rate Salesforce Positive MuleSoft-Platform-Architect-I Feedback

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Salesforce MuleSoft-Platform-Architect-I Exam Syllabus Topics:

TopicDetails
Topic 1
  • Deploying API Implementations to CloudHub: Understanding Object Store usage, selecting worker sizes, predicting app reliability and performance, and comparing load balancers. Avoiding single points of failure in deployments is also its sub-topic.
Topic 2
  • Establishing Organizational and Platform Foundations: Advising on a Center for Enablement (C4E) and identifying KPIs, describing MuleSoft Catalyst's structure, comparing Identity and Client Management options, and identifying data residency types are essential sub-topics.
Topic 3
  • Monitoring and Analyzing Application Networks: It discusses Anypoint Platform components for data generation, collected metrics, and key alerts. This topic also includes specifying alerts to define Mule applications.
Topic 4
  • Meeting API Quality Goals: This topic focuses on designing resilience strategies, selecting appropriate caching and OS usage scenarios, and describing horizontal scaling benefits.
Topic 5
  • Governing Web APIs on Anypoint Platform: This topic includes sub-topics related to managing API instances and environments, selecting API policies, enforcing API policies, securing APIs, and understanding OAuth 2.0 relationships.
Topic 6
  • Designing APIs Using System, Process, and Experience Layers: Identifying suitable APIs for business processes, assigning them according to functional focus, and recommending data model approaches are its sub-topics.

Salesforce Certified MuleSoft Platform Architect I Sample Questions (Q132-Q137):

NEW QUESTION # 132
A set of tests must be performed prior to deploying API implementations to a staging environment. Due to data security and access restrictions, untested APIs cannot be granted access to the backend systems, so instead mocked data must be used for these tests. The amount of available mocked data and its contents is sufficient to entirely test the API implementations with no active connections to the backend systems. What type of tests should be used to incorporate this mocked data?

  • A. Integration tests
  • B. Functional tests (Blackbox)
  • C. Performance tests
  • D. Unit tests (Whitebox)

Answer: D

Explanation:
Correct Answer : Unit tests (Whitebox)
*****************************************
Reference:
As per general IT testing practice and MuleSoft recommended practice, Integration and Performance tests should be done on full end to end setup for right evaluation. Which means all end systems should be connected while doing the tests. So, these options are OUT and we are left with Unit Tests and Functional Tests.
As per attached reference documentation from MuleSoft:
Unit Tests - are limited to the code that can be realistically exercised without the need to run it inside Mule itself. So good candidates are Small pieces of modular code, Sub Flows, Custom transformers, Custom components, Custom expression evaluators etc.
Functional Tests - are those that most extensively exercise your application configuration. In these tests, you have the freedom and tools for simulating happy and unhappy paths. You also have the possibility to create stubs for target services and make them success or fail to easily simulate happy and unhappy paths respectively.
As the scenario in the question demands for API implementation to be tested before deployment to Staging and also clearly indicates that there is enough/ sufficient amount of mock data to test the various components of API implementations with no active connections to the backend systems, Unit Tests are the one to be used to incorporate this mocked data.


NEW QUESTION # 133
Refer to the exhibit.

An organization uses one specific CloudHub (AWS) region for all CloudHub deployments.
How are CloudHub workers assigned to availability zones (AZs) when the organization's Mule applications are deployed to CloudHub in that region?

  • A. Workers belonging to a given environment are assigned to the same AZ within that region
  • B. An AZ is randomly selected for a Mule application, and all the Mule application's CloudHub workers are assigned to that one AZ
  • C. AZs are selected as part of the Mule application's deployment configuration
  • D. Workers are randomly distributed across available AZs within that region

Answer: B

Explanation:
Correct Answer : Workers are randomly distributed across available AZs within that region.
*****************************************
>> Currently, we only have control to choose which AWS Region to choose but there is no control at all using any configurations or deployment options to decide what Availability Zone (AZ) to assign to what worker.
>> There are NO fixed or implicit rules on platform too w.r.t assignment of AZ to workers based on environment or application.
>> They are completely assigned in random. However, cloudhub definitely ensures that HA is achieved by assigning the workers to more than on AZ so that all workers are not assigned to same AZ for same application.
Reference:

Bottom of Form
Top of Form


NEW QUESTION # 134
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. 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
  • 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. Do not set a timeout; the Invocation of this API Is mandatory and so we must wait until it responds

Answer: C

Explanation:
Correct Answer : Set a timeout of 100ms; that leaves 400ms for other two downstream APIs to complete
*****************************************
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 # 135
What condition requires using a CloudHub Dedicated Load Balancer?

  • A. When cross-region load balancing is required between separate deployments of the same Mule application
  • B. When custom DNS names are required for API implementations deployed to customer-hosted Mule runtimes
  • C. When API invocations across multiple CloudHub workers must be load balanced
  • D. When server-side load-balanced TLS mutual authentication is required between API implementations and API clients

Answer: D

Explanation:
Correct Answe r: When server-side load-balanced TLS mutual authentication is required between API implementations and API clients
*****************************************
Fact/ Memory Tip: Although there are many benefits of CloudHub Dedicated Load balancer, TWO important things that should come to ones mind for considering it are:
>> Having URL endpoints with Custom DNS names on CloudHub deployed apps
>> Configuring custom certificates for both HTTPS and Two-way (Mutual) authentication.
Coming to the options provided for this question:
>> We CANNOT use DLB to perform cross-region load balancing between separate deployments of the same Mule application.
>> We can have mapping rules to have more than one DLB URL pointing to same Mule app. But vicevera (More than one Mule app having same DLB URL) is NOT POSSIBLE
>> It is true that DLB helps to setup custom DNS names for Cloudhub deployed Mule apps but NOT true for apps deployed to Customer-hosted Mule Runtimes.
>> It is true to that we can load balance API invocations across multiple CloudHub workers using DLB but it is NOT A MUST. We can achieve the same (load balancing) using SLB (Shared Load Balancer) too. We DO NOT necessarily require DLB for achieve it.
So the only right option that fits the scenario and requires us to use DLB is when TLS mutual authentication is required between API implementations and API clients.


NEW QUESTION # 136
An enterprise is embarking on the API-led digital transformation journey, and the central IT team has started to define System APIs. Currently there is no Enterprise Data Model being defined within the enterprise, and the definition of a clean Bounded Context Data Model requires too much effort.
According to MuleSoft's recommended guidelines, how should the System API data model be defined?

  • A. The System APIs should expose all back-end system fields
  • B. If there are misspellings of the data fields in the back-end system, Systerm APIs should not correct it, and expose it as-is to mirror the back-end systems
  • C. The data model should define its own naming convention, and not follow the same naming as the back-end systems
  • D. The data model of the System APIs should make use of data types that approximately mirror those from the back-end systems

Answer: D

Explanation:
When defining data models for System APIs without an established Enterprise Data Model, MuleSoft recommends mirroring the back-end systems' data types to achieve quick and effective integration without adding complexity. This approach has several benefits:
Alignment with Backend Systems:
Mirroring data types ensures consistency with backend data sources, which simplifies integration, reduces mapping requirements, and minimizes potential data transformation issues.
Flexibility for Future Enhancements:
By retaining close alignment with backend data structures, System APIs can evolve to support an Enterprise Data Model in the future without immediate restructuring.
of Incorrect Options:
Option A (exposing misspellings) is not recommended as System APIs should still ensure a professional and coherent interface.
Option C (custom naming) complicates the API structure without adding immediate value in the absence of a clear data model.
Option D (exposing all fields) is unnecessary and can reduce performance and add complexity.
Reference
Refer to MuleSoft best practices for data modeling in System APIs for additional information on mirroring backend systems.


NEW QUESTION # 137
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