Salesforce MuleSoft-Platform-Architect-I最新問題集[2025]高得点を掴み取れ
MuleSoft-Platform-Architect-I問題集JPNTest100%合格率保証
Salesforce MuleSoft-Platform-Architect-I 認定試験の出題範囲：
| トピック | 出題範囲 |
|---|---|
| トピック 1 |
|
| トピック 2 |
|
| トピック 3 |
|
| トピック 4 |
|
| トピック 5 |
|
| トピック 6 |
|
| トピック 7 |
|
質問 # 88
Version 3.0.1 of a REST API implementation represents time values in PST time using ISO 8601 hh:mm:ss format. The API implementation needs to be changed to instead represent time values in CEST time using ISO 8601 hh:mm:ss format. When following the semver.org semantic versioning specification, what version should be assigned to the updated API implementation?
- A. 3.1.0
- B. 3.0.1
- C. 4.0.0
- D. 3.0.2
正解：C
解説：
Correct Answer : 4.0.0
*****************************************
As per semver.org semantic versioning specification:
Given a version number MAJOR.MINOR.PATCH, increment the:
- MAJOR version when you make incompatible API changes.
- MINOR version when you add functionality in a backwards compatible manner.
- PATCH version when you make backwards compatible bug fixes.
As per the scenario given in the question, the API implementation is completely changing its behavior. Although the format of the time is still being maintained as hh:mm:ss and there is no change in schema w.r.t format, the API will start functioning different after this change as the times are going to come completely different.
Example: Before the change, say, time is going as 09:00:00 representing the PST. Now on, after the change, the same time will go as 18:00:00 as Central European Summer Time is 9 hours ahead of Pacific Time.
>> This may lead to some uncertain behavior on API clients depending on how they are handling the times in the API response. All the API clients need to be informed that the API functionality is going to change and will return in CEST format. So, this considered as a MAJOR change and the version of API for this new change would be 4.0.0
質問 # 89
What is typically NOT a function of the APIs created within the framework called API-led connectivity?
- A. They reduce the dependency on the underlying backend systems by helping unlock data from backend systems In a reusable and consumable way.
- B. They provide an additional layer of resilience on top of the underlying backend system, thereby insulating clients from extended failure of these systems.
- C. 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.
- D. They can compose data from various sources and combine them with orchestration logic to create higher level value.
正解：B
解説：
Correct Answer : 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
質問 # 90
How are an API implementation, API client, and API consumer combined to invoke and process an API?
- A. The API consumer creates an API implementation, which receives API invocations from an API such that they are processed for an API client
- B. The API client creates an API consumer, which receives API invocations from an API such that they are processed for an API implementation
- C. The ApI consumer creates an API client, which sends API invocations to an API such that they are processed by an API implementation
- D. The ApI client creates an API consumer, which sends API invocations to an API such that they are processed by an API implementation
正解：C
解説：
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
*****************************************
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.
質問 # 91
What best describes the Fully Qualified Domain Names (FQDNs), also known as DNS entries, created when a Mule application is deployed to the CloudHub Shared Worker Cloud?
- A. The FQDNs are determined by both the application name and the Anypoint Platform organization
- B. The FQDNs are determined by the application name, but can be modified by an administrator after deployment
- C. The FQDNs are determined by the application name chosen, IRRESPECTIVE of the region
- D. A fixed number of FQDNs are created, IRRESPECTIVE of the environment and VPC design
正解：C
解説：
Correct Answer : The FQDNs are determined by the application name chosen, IRRESPECTIVE of the region
*****************************************
>> 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.
質問 # 92
A system API has a guaranteed SLA of 100 ms per request. The system API is deployed to a primary environment as well as to a disaster recovery (DR) environment, with different DNS names in each environment. An upstream process API invokes the system API and the main goal of this process API is to respond to client requests in the least possible time. In what order should the system APIs be invoked, and what changes should be made in order to speed up the response time for requests from the process API?
- A. In parallel, invoke the system API deployed to the primary environment and the system API deployed to the DR environment, and ONLY use the first response
- B. In parallel, invoke the system API deployed to the primary environment and the system API deployed to the DR environment using a scatter-gather configured with a timeout, and then merge the responses
- C. Invoke the system API deployed to the primary environment, and if it fails, invoke the system API deployed to the DR environment
- D. Invoke ONLY the system API deployed to the primary environment, and add timeout and retry logic to avoid intermittent failures
正解：A
解説：
Correct Answer : In parallel, invoke the system API deployed to the primary environment and the system API deployed to the DR environment, and ONLY use the first response.
*****************************************
>> The API requirement in the given scenario is to respond in least possible time.
>> The option that is suggesting to first try the API in primary environment and then fallback to API in DR environment would result in successful response but NOT in least possible time. So, this is NOT a right choice of implementation for given requirement.
>> Another option that is suggesting to ONLY invoke API in primary environment and to add timeout and retries may also result in successful response upon retries but NOT in least possible time. So, this is also NOT a right choice of implementation for given requirement.
>> One more option that is suggesting to invoke API in primary environment and API in DR environment in parallel using Scatter-Gather would result in wrong API response as it would return merged results and moreover, Scatter-Gather does things in parallel which is true but still completes its scope only on finishing all routes inside it. So again, NOT a right choice of implementation for given requirement The Correct choice is to invoke the API in primary environment and the API in DR environment parallelly, and using ONLY the first response received from one of them.
質問 # 93
An online store's marketing team has noticed an increase in customers leaving online baskets without checking out. They suspect a technology issue is at the root cause of the baskets being left behind. They approach the Center for Enablement to ask for help identifying the issue. Multiple APIs from across all the layers of their application network are involved in the shopping application.
Which feature of the Anypoint Platform can be used to view metrics from all involved APIs at the same time?
- A. API Manager
- B. Built-in dashboards
- C. Functional monitoring
- D. Custom dashboards
正解：B
解説：
Understanding the Need for Cross-API Monitoring:
The Center for Enablement (C4E) needs to investigate potential technical issues across multiple APIs in the application network that may be causing customers to abandon their carts.
This requires a solution that allows viewing metrics across several APIs in real-time to identify any performance issues or bottlenecks.
Evaluating Anypoint Platform Features:
Built-in Dashboards: Anypoint Platform provides built-in dashboards in Anypoint Monitoring, allowing teams to view metrics from multiple APIs in a single interface. This feature is designed to monitor API performance, latency, errors, and throughput, and is ideal for tracking performance across all layers of the application network.
Custom Dashboards: While custom dashboards allow for more tailored views, the built-in dashboards already aggregate metrics for multiple APIs, making it unnecessary to build a custom solution for this scenario.
Functional Monitoring: This feature is used to set up tests to monitor specific API functionality and uptime but is not suited for tracking metrics across multiple APIs in real-time.
API Manager: API Manager primarily focuses on managing API policies, contracts, and access control rather than providing detailed, real-time metrics across the entire application network.
Conclusion:
Option B (Built-in dashboards) is the best choice because it provides a comprehensive view of metrics from all APIs involved, enabling the C4E team to quickly identify any issues that may be contributing to abandoned shopping carts.
Refer to MuleSoft's documentation on Anypoint Monitoring and built-in dashboards for more details on configuring and using these dashboards effectively.
質問 # 94
A company requires Mule applications deployed to CloudHub to be isolated between non-production and production environments. This is so Mule applications deployed to non-production environments can only access backend systems running in their customer-hosted non-production environment, and so Mule applications deployed to production environments can only access backend systems running in their customer-hosted production environment. How does MuleSoft recommend modifying Mule applications, configuring environments, or changing infrastructure to support this type of per-environment isolation between Mule applications and backend systems?
- A. Modify properties of Mule applications deployed to the production Anypoint Platform environments to prevent access from non-production Mule applications
- B. Create separate Anypoint VPCs for non-production and production environments, then configure connections to the backend systems in the corresponding customer-hosted environments
- C. Create non-production and production environments in different Anypoint Platform business groups
- D. Configure firewall rules in the infrastructure inside each customer-hosted environment so that only IP addresses from the corresponding Anypoint Platform environments are allowed to communicate with corresponding backend systems
正解：B
解説：
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.
*****************************************
>> 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.
Reference:
Bottom of Form
Top of Form
質問 # 95
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. The invocation of the downstream API will run to completion without timing out
- B. A toad-dependent timeout of less than 1000 ms will be applied by the Mule runtime in which the downstream API implementation executes
- C. An SLA for the upstream API CANNOT be provided
- D. A default timeout of 500 ms will automatically be applied by the Mule runtime in which the upstream API implementation executes
正解：C
解説：
Correct Answer : 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.
質問 # 96
A manufacturing company has deployed an API implementation to CloudHub and has not configured it to be automatically restarted by CloudHub when the worker is not responding.
Which statement is true when no API Client invokes that API implementation?
- A. Alerts on the API invocations are raised but no alerts on the API implementation can be raised
- B. No alert on the API invocations is raised but alerts on the API implementation can be raised
- C. No alert on the API invocations and APT implementation can be raised
- D. Alerts on the APT invocation and API implementation can be raised
正解：B
解説：
When an API implementation is deployed on CloudHub without configuring automatic restarts in case of worker non-responsiveness, MuleSoft's monitoring and alerting behavior is as follows:
API Invocation Alerts:
If no clients are invoking the API, there will be no invocation alerts triggered, as alerts related to invocations depend on actual client requests.
Implementation-Level Alerts:
Even without invocation, CloudHub can still monitor the state of the API implementation. If the worker becomes unresponsive, an alert related to the API implementation's health or availability could still be raised.
Why Option C is Correct:
This option correctly identifies that no invocation-related alerts would be triggered in the absence of client requests, while implementation-level alerts could still be generated based on the worker's state.
Reference
For additional information, check MuleSoft documentation on CloudHub monitoring and alert configurations to understand worker status alerts versus invocation alerts.
質問 # 97
What CANNOT be effectively enforced using an API policy in Anypoint Platform?
- A. Backend system overloading
- B. Maintaining tamper-proof credentials between APIs
- C. Logging HTTP requests and responses
- D. Guarding against Denial of Service attacks
正解：D
解説：
Correct Answer : Guarding against Denial of Service attacks
*****************************************
>> Backend system overloading can be handled by enforcing "Spike Control Policy"
>> Logging HTTP requests and responses can be done by enforcing "Message Logging Policy"
>> Credentials can be tamper-proofed using "Security" and "Compliance" Policies However, unfortunately, there is no proper way currently on Anypoint Platform to guard against DOS attacks.
質問 # 98
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%
正解：D
解説：
Correct Answer : 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.
質問 # 99
Which APIs can be used with DataGraph to create a unified schema?
- A. APIs 1, 2, s5, 6
- B. APIs 2, 4 ,6
- C. APIs 1, 3, 5
- D. APIs 1, 2, 3, 4
正解：D
解説：
To create a unified schema in MuleSoft's DataGraph, APIs must be exposed in a way that allows DataGraph to pull and consolidate data from these APIs into a single schema accessible to consumers. DataGraph provides a federated approach, combining multiple APIs to form a single, unified API endpoint.
In this setup:
APIs 1, 2, 3, and 4 are suitable candidates for DataGraph because they are hosted within the Customer VPC on CloudHub and are accessible either through a Shared Load Balancer (LB) or a Dedicated Load Balancer (DLB). Both of these load balancers provide public access, which is a necessary condition for DataGraph as it must access the APIs to aggregate data.
APIs 5 and 6 are hosted on Customer Hosted Server 2, which is explicitly marked as "Not public". Since DataGraph requires API access through a publicly reachable endpoint to aggregate them into a unified schema, APIs 5 and 6 cannot be used with DataGraph in this configuration.
APIs 3 and 4 on Customer Hosted Server 1 appear accessible through a Shared LB, implying public accessibility that meets DataGraph's requirements.
By combining APIs 1, 2, 3, and 4 within DataGraph, you can create a unified schema that enables clients to query data seamlessly from all these APIs as if it were from a single source.
This setup allows for efficient data retrieval and can simplify API consumption by reducing the need to call multiple APIs individually, thus optimizing performance and developer experience.
Reference
For more detailed information on setting up and managing unified schemas in DataGraph, refer to the DataWeave documentation and MuleSoft DataGraph resources which provide in-depth guidelines on schema aggregation and API federation.
質問 # 100
An organization has created an API-led architecture that uses various API layers to integrate mobile clients with a backend system. The backend system consists of a number of specialized components and can be accessed via a REST API. The process and experience APIs share the same bounded-context model that is different from the backend data model. What additional canonical models, bounded-context models, or anti-corruption layers are best added to this architecture to help process data consumed from the backend system?
- A. Create a canonical model that combines the backend and API-led models to simplify and unify data models, and minimize data transformations.
- B. Create an anti-corruption layer for every API to perform transformation for every data model to match each other, and let data simply travel between APIs to avoid the complexity and overhead of building canonical models
- C. Create a bounded-context model for every layer and overlap them when the boundary contexts overlap, letting API developers know about the differences between upstream and downstream data models
- D. Create a bounded-context model for the system layer to closely match the backend data model, and add an anti-corruption layer to let the different bounded contexts cooperate across the system and process layers
正解：D
解説：
Correct Answer : Create a bounded-context model for the system layer to closely match the backend data model, and add an anti-corruption layer to let the different bounded contexts cooperate across the system and process layers
*****************************************
>> Canonical models are not an option here as the organization has already put in efforts and created bounded-context models for Experience and Process APIs.
>> Anti-corruption layers for ALL APIs is unnecessary and invalid because it is mentioned that experience and process APIs share same bounded-context model. It is just the System layer APIs that need to choose their approach now.
>> So, having an anti-corruption layer just between the process and system layers will work well. Also to speed up the approach, system APIs can mimic the backend system data model.
質問 # 101
A company uses a hybrid Anypoint Platform deployment model that combines the EU control plane with customer-hosted Mule runtimes. After successfully testing a Mule API implementation in the Staging environment, the Mule API implementation is set with environment-specific properties and must be promoted to the Production environment. What is a way that MuleSoft recommends to configure the Mule API implementation and automate its promotion to the Production environment?
- A. Use an API policy to change properties in the Mule API implementation deployed to the Staging environment and another API policy to deploy the Mule API implementation to the Production environment
- B. Modify the Mule API implementation's properties in Anypoint Exchange, then promote the Mule API implementation to the Production environment using Runtime Manager
- C. Modify the Mule API implementation's properties in the API Manager Properties tab, then promote the Mule API implementation to the Production environment using API Manager
- D. Bundle properties files for each environment into the Mule API implementation's deployable archive, then promote the Mule API implementation to the Production environment using Anypoint CLI or the Anypoint Platform REST APIsB.
正解：D
解説：
Correct Answer : Bundle properties files for each environment into the Mule API implementation's deployable archive, then promote the Mule API implementation to the Production environment using Anypoint CLI or the Anypoint Platform REST APIs
*****************************************
>> Anypoint Exchange is for asset discovery and documentation. It has got no provision to modify the properties of Mule API implementations at all.
>> API Manager is for managing API instances, their contracts, policies and SLAs. It has also got no provision to modify the properties of API implementations.
>> API policies are to address Non-functional requirements of APIs and has again got no provision to modify the properties of API implementations.
So, the right way and recommended way to do this as part of development practice is to bundle properties files for each environment into the Mule API implementation and just point and refer to respective file per environment.
質問 # 102
A customer has an ELA contract with MuleSoft. An API deployed to CloudHub is consistently experiencing performance issues. Based on the root cause analysis, it is determined that autoscaling needs to be applied.
How can this be achieved?
- A. Configure a policy so that when the response time reaches a certain threshold the worker/replica type increases (vertical scaling)
- B. Configure two separate policies: When CPU and memory reach certain threshold, increase the worker/replica type (vertical sealing) and the number of workers/replicas (horizontal sealing)
- C. Configure a policy so that when the number of HTTP requests reaches a certain threshold the number of workers/replicas increases (horizontal scaling)
- D. Configure a policy based on CPU usage so that CloudHub auto-adjusts the number of workers/replicas (horizontal scaling)
正解：D
解説：
In MuleSoft CloudHub, autoscaling is essential to managing application load efficiently. CloudHub supports horizontal scaling based on CPU usage, which is well-suited to applications experiencing variable demand and needing responsive resource allocation.
Autoscaling on CloudHub:
Horizontal scaling increases the number of workers in response to CPU usage thresholds, allowing the application to handle higher loads dynamically. This approach improves performance without downtime or manual intervention.
Why Option C is Correct:
Setting up autoscaling based on CPU usage aligns with MuleSoft's best practices for scalable and responsive applications on CloudHub, particularly in an environment with fluctuating load patterns.
Option C correctly leverages CloudHub's autoscaling features based on resource metrics, which are part of CloudHub's managed scaling solutions.
of Incorrect Options:
Option A (based on HTTP request thresholds) and Option B (separate policies for CPU and memory) do not represent CloudHub's recommended scaling practices.
Option D suggests vertical scaling based on response time, which is not how CloudHub handles autoscaling.
Reference
For more on CloudHub's autoscaling configuration, refer to MuleSoft documentation on CloudHub autoscaling policies.
質問 # 103
......
100%合格率リアルMuleSoft-Platform-Architect-I試験成功を掴み取れ：https://www.jpntest.com/shiken/MuleSoft-Platform-Architect-I-mondaishu
プレミアム良質なSalesforce MuleSoft-Platform-Architect-Iオンライン問題集：https://drive.google.com/open?id=1gMpBEjsqCK8Ekw-fa0kXZBLUeGwfPZQG