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質問 # 61
A company is using an on-prem cluster in the data center as a runtime plane and MuleSoft-hosted control plane.
How can the company monitor the detailed performance metrics on the Mule applications deployed to the cluster from the control plane?
- A. Monitoring Agent must be installed on each node in the cluster
- B. There is no action needed as the on-prem runtime automatically sends the performance data to the control plane
- C. The settings of the Monitoring section in the control plane must be updated to enable detailed logging on the metrics to be captured
- D. Due to the potential performance impact on the runtime nodes, the Monitoring agent should be installed on a separate server
正解：A
解説：
Monitoring On-Premise Mule Applications:
For Mule applications deployed on an on-premises cluster, monitoring detailed performance metrics requires communication with the MuleSoft-hosted control plane. The control plane, when used with on-premises runtimes, relies on Anypoint Monitoring and requires a Monitoring Agent to gather and send detailed performance metrics.
Setting Up Monitoring:
To enable detailed metrics, the Monitoring Agent must be installed on each node in the cluster where Mule applications are deployed. This agent collects data on memory usage, CPU load, response times, and other metrics, and sends it to the control plane for aggregation and visualization.
Evaluating the Options:
Option A: Updating settings in the control plane alone does not enable detailed monitoring; the agent must be installed on each node to capture detailed metrics.
Option B (Correct Answer): Installing the Monitoring Agent on each node ensures that each runtime node in the cluster can send its metrics to the control plane, enabling detailed monitoring.
Option C: Installing the agent on a separate server would not be effective, as each node in the cluster needs to independently report its metrics to ensure full visibility.
Option D: The on-prem runtime does not automatically send detailed metrics to the control plane without the Monitoring Agent installed.
Conclusion:
Option B is the correct answer, as installing the Monitoring Agent on each node is essential for detailed performance monitoring of on-prem applications in a cluster.
Refer to MuleSoft's documentation on configuring Anypoint Monitoring for on-premises deployments and using the Monitoring Agent.
質問 # 62
To minimize operation costs, a customer wants to use a CloudHub 1.0 solution. The customer's requirements are:
* Separate resources with two Business groups
* High-availability (HA) for all APIs
* Route traffic via Dedicated load balancer (DLBs)
* Separate environments into production and non-production
Which solution meets the customer's needs?
- A. One production and one non-production Virtual Private Cloud (VPC).
Use availability zones to differentiate between Business groups.
Allocate maximum CIDR per VPCs to ensure HA across availability zones - B. One production and one non-production Virtual Private Cloud (VPC) per Business group.
Minimize CIDR aligning with projected application total.
Divide availability zones during deployment of APIs for HA. - C. One production and one non-production Virtual Private Claud (VPC).
Configure subnet to differentiate between business groups.
Allocate maximum CIDR per VPCs to make it easier to add Child groups.
Span VPC to cover three availability zones. - D. One production and one non-production Virtual Private Cloud (VPC) per Business group.
Minimize CIDR aligning with projected application total.
Choose a MuleSoft CloudHub 1.0 region with multiple availability zones.
Deploy multiple workers for HA,
正解：D
解説：
Understanding the Requirements:
Business Groups: The solution must support two business groups, which typically require separate VPCs for logical separation.
High Availability (HA): Requires deploying resources across multiple availability zones.
Dedicated Load Balancer (DLB): Traffic should be routed via DLBs, which operate within VPCs on CloudHub.
Separate Environments: There needs to be separation between production and non-production environments.
Evaluating the Options:
Option A: Using a single production and non-production VPC and differentiating business groups via availability zones is not ideal as it does not provide full separation for each business group, and using maximum CIDR allocation is wasteful.
Option B (Correct Answer): Creating separate production and non-production VPCs per business group with minimized CIDR blocks, multiple availability zones, and multiple workers per application for HA meets all requirements effectively.
Option C: While this option separates VPCs per business group, it does not fully address the requirement for HA across availability zones by specifying multi-zone deployment only during API deployment, which may not guarantee redundancy.
Option D: Configuring subnets to differentiate business groups within a single production and non-production VPC does not fully separate the business groups, which is a requirement.
Conclusion:
Option B is the best choice as it meets the requirements for high availability, business group separation, and cost efficiency by using minimized CIDR allocations and deploying multiple workers across availability zones.
For further reference, refer to MuleSoft's documentation on VPC configuration and high availability deployment strategies.
質問 # 63
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. An SLA for the upstream API CANNOT be provided
- 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
正解：B
解説：
Correct Answer : An SLA for the upstream API CANNOT be provided.
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>> 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 # 64
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. 4.0.0
- C. 3.0.1
- D. 3.0.2
正解：B
解説：
Correct Answer : 4.0.0
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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 # 65
An API has been updated in Anypoint exchange by its API producer from version 3.1.1 to 3.2.0 following accepted semantic versioning practices and the changes have been communicated via the APIs public portal. The API endpoint does NOT change in the new version. How should the developer of an API client respond to this change?
- A. The API client code only needs to be changed if it needs to take advantage of the new features
- B. The API clients need to update the code on their side and need to do full regression
- C. The API producer should be requested to run the old version in parallel with the new one
- D. The API producer should be contacted to understand the change to existing functionality
正解：A
質問 # 66
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. Unit tests (Whitebox)
- D. Performance tests
正解：C
解説：
Correct Answer : Unit tests (Whitebox)
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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.
質問 # 67
What is a key performance indicator (KPI) that measures the success of a typical C4E that is immediately apparent in responses from the Anypoint Platform APIs?
- A. The number of production outage incidents reported in the last 24 hours
- B. The number of API specifications in RAML or OAS format published to Anypoint Exchange
- C. The number of API implementations that have a publicly accessible HTTP endpoint and are being managed by Anypoint Platform
- D. The fraction of API implementations deployed manually relative to those deployed using a CI/CD tool
正解：B
解説：
Correct Answer : The number of API specifications in RAML or OAS format published to Anypoint Exchange
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>> The success of C4E always depends on their contribution to the number of reusable assets that they have helped to build and publish to Anypoint Exchange.
>> It is NOT due to any factors w.r.t # of outages, Manual vs CI/CD deployments or Publicly accessible HTTP endpoints
>> Anypoint Platform APIs helps us to quickly run and get the number of published RAML/OAS assets to Anypoint Exchange. This clearly depicts how successful a C4E team is based on number of returned assets in the response.
質問 # 68
An established communications company is beginning its API-led connectivity journey, The company has been using a successful Enterprise Data Model for many years. The company has identified a self-service account management app as the first effort for API-led, and it has identified the following APIs.
Experience layer: Mobile Account Management EAPI, Browser Account Management EAPI Process layer: Customer Lookup PAPI, Service Lookup PAPI, Account Lookup PAPI System layer: Customer SAPI, Account SAPI, Product SAPI, Service SAPI According to MuleSoft's API-led connectivity approach, which API would not be served by the Enterprise Data Model?
- A. Customer Lookup PAPI
- B. Mobile Account Management EAPI
- C. Customer SAPI
- D. Service SAPI
正解：B
解説：
In the API-led connectivity approach, APIs are categorized into Experience, Process, and System layers:
Enterprise Data Model Scope:
The Enterprise Data Model (EDM) generally supports System APIs and some Process APIs by defining standard data structures used across the organization. Experience APIs, however, are tailored to specific applications or interfaces and are less likely to be served directly by the EDM, as they may require customized data representations to meet the unique needs of each user interface.
Why Option C is Correct:
The Mobile Account Management EAPI serves mobile-specific needs and often requires data formatted differently from the standardized data models. Thus, it would be outside the direct scope of the EDM and might employ custom mappings to fit mobile application requirements.
of Incorrect Options:
Option A (Customer SAPI), Option B (Customer Lookup PAPI), and Option D (Service SAPI) would typically align with the EDM as they are closer to the core data and services the EDM supports.
Reference
For additional guidance, review MuleSoft's best practices on API-led connectivity and data modeling.
質問 # 69
Which of the below, when used together, makes the IT Operational Model effective?
- A. Create reusable assets, Make them discoverable so that LOB teams can self-serve and browse the APIs, Get active feedback and usage metrics
- B. Create resuable assets, make them discoverable so that LOB teams can self-serve and browse the APIs
- C. Create reusable assets, Do marketing on the created assets across organization, Arrange time to time LOB reviews to ensure assets are being consumed or not
正解：B
解説：
Correct Answer : Create reusable assets, Make them discoverable so that LOB teams can self-serve and browse the APIs, Get active feedback and usage metrics.
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質問 # 70
An API is protected with a Client ID Enforcement policy and uses the default configuration. Access is requested for the client application to the API, and an approved contract now exists between the client application and the API How can a consumer of this API avoid a 401 error "Unauthorized or invalid client application credentials"?
- A. Send the obtained clent_id and clent_secret as URI parameters in every call
- B. Send the obtained: client_id and client_secret in the request body
- C. Send the obtained clent_id and client_secret in the header of every API Request call
- D. Send the obtained token as a header in every call
正解：A
解説：
When using the Client ID Enforcement policy with default settings, MuleSoft expects the client_id and client_secret to be provided in the URI parameters of each request. This policy is typically used to control and monitor access by validating that each request has valid credentials. Here's how to avoid a 401 Unauthorized error:
URI Parameters Requirement:
The default configuration for the Client ID Enforcement policy requires the client_id and client_secret to be included in each request's URI parameters. This is a straightforward way to authenticate API requests without additional configurations.
Why Option C is Correct:
Providing client_id and client_secret in the URI parameters meets the policy's requirements for each request, ensuring authorized access and avoiding the 401 error.
of Incorrect Options:
Option A (sending a token in the header) would be applicable for token-based authentication (like OAuth 2.0), not Client ID Enforcement.
Option B (request body) and Option D (header) are not valid locations for client_id and client_secret under the default configuration of Client ID Enforcement, which expects them in the URI.
Reference
For more details, consult MuleSoft's documentation on Client ID Enforcement policies and expected request configurations
質問 # 71
An organization makes a strategic decision to move towards an IT operating model that emphasizes consumption of reusable IT assets using modern APIs (as defined by MuleSoft).
What best describes each modern API in relation to this new IT operating model?
- A. Each modem API must be treated like a product and designed for a particular target audience (for instance, mobile app developers)
- B. Each modern API must be easy to consume, so should avoid complex authentication mechanisms such as SAML or JWT D
- C. Each modern API must be REST and HTTP based
- D. Each modern API has its own software development lifecycle, which reduces the need for documentation and automation
正解：A
解説：
Correct Answers:
1. Each modern API must be treated like a product and designed for a particular target audience (for instance mobile app developers)
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質問 # 72
What should be ensured before sharing an API through a public Anypoint Exchange portal?
- A. The API should be functional with at least an initial implementation deployed and accessible for users to interact with
- B. The API should be secured using one of the supported authentication/authorization mechanisms to ensure that data is not compromised
- C. The users needing access to the API should be added to the appropriate role in Anypoint Platform
- D. The visibility level of the API instances of that API that need to be publicly accessible should be set to public visibility
正解：D
解説：
Correct Answer : The visibility level of the API instances of that API that need to be publicly accessible should be set to public visibility.
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Reference:
https://docs.mulesoft.com/exchange/to-share-api-asset-to-portal
質問 # 73
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. TRUE
- B. FALSE
正解：A
解説：
Correct Answer : TRUE
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>> 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.
質問 # 74
When using CloudHub with the Shared Load Balancer, what is managed EXCLUSIVELY by the API implementation (the Mule application) and NOT by Anypoint Platform?
- A. The logging configuration that enables log entries to be visible in Runtime Manager
- B. The assignment of each HTTP request to a particular CloudHub worker
- C. The number of DNS entries allocated to the API implementation
- D. The SSL certificates used by the API implementation to expose HTTPS endpoints
正解：D
解説：
Correct Answer : The SSL certificates used by the API implementation to expose HTTPS endpoints
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>> The assignment of each HTTP request to a particular CloudHub worker is taken care by Anypoint Platform itself. We need not manage it explicitly in the API implementation and in fact we CANNOT manage it in the API implementation.
>> The logging configuration that enables log entries to be visible in Runtime Manager is ALWAYS managed in the API implementation and NOT just for SLB. So this is not something we do EXCLUSIVELY when using SLB.
>> We DO NOT manage the number of DNS entries allocated to the API implementation inside the code. Anypoint Platform takes care of this.
It is the SSL certificates used by the API implementation to expose HTTPS endpoints that is to be managed EXCLUSIVELY by the API implementation. Anypoint Platform does NOT do this when using SLBs.
質問 # 75
What are 4 important Platform Capabilities offered by Anypoint Platform?
- A. API Versioning, API Runtime Execution and Hosting, API Invocation, API Consumer Engagement
- B. API Design and Development, API Deprecation, API Versioning, API Consumer Engagement
- C. API Design and Development, API Runtime Execution and Hosting, API Operations and Management, API Consumer Engagement
- D. API Design and Development, API Runtime Execution and Hosting, API Versioning, API Deprecation
正解：C
解説：
Correct Answer : API Design and Development, API Runtime Execution and Hosting, API Operations and Management, API Consumer Engagement
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>> API Design and Development - Anypoint Studio, Anypoint Design Center, Anypoint Connectors
>> API Runtime Execution and Hosting - Mule Runtimes, CloudHub, Runtime Services
>> API Operations and Management - Anypoint API Manager, Anypoint Exchange
>> API Consumer Management - API Contracts, Public Portals, Anypoint Exchange, API Notebooks
質問 # 76
A system API is deployed to a primary environment as well as to a disaster recovery (DR) environment, with different DNS names in each environment. A process API is a client to the system API and is being rate limited by the system API, with different limits in each of the environments. The system API's DR environment provides only 20% of the rate limiting offered by the primary environment. What is the best API fault-tolerant invocation strategy to reduce overall errors in the process API, given these conditions and constraints?
- A. Invoke the system API deployed to the primary environment; add timeout and retry logic to the process API to avoid intermittent failures; if it still fails, invoke a copy of the process API deployed to the DR environment
- B. Invoke the system API deployed to the primary environment; add timeout and retry logic to the process API to avoid intermittent failures; if it still fails, invoke the system API deployed to the DR environment
- C. In parallel, invoke the system API deployed to the primary environment and the system API deployed to the DR environment; add timeout and retry logic to the process API to avoid intermittent failures; add logic to the process API to combine the results
- D. Invoke the system API deployed to the primary environment; add retry logic to the process API to handle intermittent failures by invoking the system API deployed to the DR environment
正解：B
解説：
Correct Answer : Invoke the system API deployed to the primary environment; add timeout and retry logic to the process API to avoid intermittent failures; if it still fails, invoke the system API deployed to the DR environment
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There is one important consideration to be noted in the question which is - System API in DR environment provides only 20% of the rate limiting offered by the primary environment. So, comparitively, very less calls will be allowed into the DR environment API opposed to its primary environment. With this in mind, lets analyse what is the right and best fault-tolerant invocation strategy.
1. Invoking both the system APIs in parallel is definitely NOT a feasible approach because of the 20% limitation we have on DR environment. Calling in parallel every time would easily and quickly exhaust the rate limits on DR environment and may not give chance to genuine intermittent error scenarios to let in during the time of need.
2. Another option given is suggesting to add timeout and retry logic to process API while invoking primary environment's system API. This is good so far. However, when all retries failed, the option is suggesting to invoke the copy of process API on DR environment which is not right or recommended. Only system API is the one to be considered for fallback and not the whole process API. Process APIs usually have lot of heavy orchestration calling many other APIs which we do not want to repeat again by calling DR's process API. So this option is NOT right.
3. One more option given is suggesting to add the retry (no timeout) logic to process API to directly retry on DR environment's system API instead of retrying the primary environment system API first. This is not at all a proper fallback. A proper fallback should occur only after all retries are performed and exhausted on Primary environment first. But here, the option is suggesting to directly retry fallback API on first failure itself without trying main API. So, this option is NOT right too.
This leaves us one option which is right and best fit.
- Invoke the system API deployed to the primary environment
- Add Timeout and Retry logic on it in process API
- If it fails even after all retries, then invoke the system API deployed to the DR environment.
質問 # 77
An API implementation is deployed to CloudHub.
What conditions can be alerted on using the default Anypoint Platform functionality, where the alert conditions depend on the API invocations to an API implementation?
- A. When the API invocations are not over-a- secure TLS/SSL communication channel
- B. When the APL invecations originate from a geography different than the API
- C. When the API invocations are sent directly to the internal DNS record of the API implementation
- D. When the number of API invocations are below a threshold
正解：D
解説：
Default Alert Capabilities in Anypoint Platform:
Anypoint Platform provides out-of-the-box alerting capabilities for monitoring API invocation conditions, including setting thresholds for the number of invocations.
Alerts can be configured for conditions such as high or low traffic (invocations exceeding or falling below a defined threshold).
Evaluating the Options:
Option A: Anypoint Platform does not provide direct alerting based on DNS records.
Option B: Anypoint Platform does not provide default alerts based on whether invocations use TLS/SSL; this would require custom configuration.
Option C: Geolocation-based alerting is not natively supported in Anypoint Platform.
Option D (Correct Answer): Alerts based on API invocation thresholds (e.g., invocations falling below a set threshold) are supported and can be configured as part of the default Anypoint alerting functionality.
Conclusion:
Option D is correct, as Anypoint Platform allows configuring alerts based on the number of API invocations falling below or exceeding a threshold.
Refer to MuleSoft's documentation on Anypoint Monitoring and alert configurations for more details.
質問 # 78
How can the application of a rate limiting API policy be accurately reflected in the RAML definition of an API?
- A. By refining the resource definitions by adding a description of the rate limiting policy behavior
- B. By refining the request definitions by adding a remaining Requests query parameter with description, type, and example
- C. By refining the response definitions by adding the x-ratelimit-* response headers with description, type, and example
- D. By refining the response definitions by adding the out-of-the-box Anypoint Platform rate-limit-enforcement securityScheme with description, type, and example
正解：C
解説：
Correct Answer : By refining the response definitions by adding the x-ratelimit-* response headers with description, type, and example
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Reference:
https://docs.mulesoft.com/api-manager/2.x/rate-limiting-and-throttling#response-headers
https://docs.mulesoft.com/api-manager/2.x/rate-limiting-and-throttling-sla-based-policies#response-headers
質問 # 79
What is a best practice when building System APIs?
- A. Expose to API clients all technical details of the API implementation's interaction wifch the backend system
- B. Model all API resources and methods to closely mimic the operations of the backend system
- C. Build an Enterprise Data Model (Canonical Data Model) for each backend system and apply it to System APIs
- D. Document the API using an easily consumable asset like a RAML definition
正解：B
解説：
Correct Answer : Model all API resources and methods to closely mimic the operations of the backend system.
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>> There are NO fixed and straight best practices while opting data models for APIs. They are completly contextual and depends on number of factors. Based upon those factors, an enterprise can choose if they have to go with Enterprise Canonical Data Model or Bounded Context Model etc.
>> One should NEVER expose the technical details of API implementation to their API clients. Only the API interface/ RAML is exposed to API clients.
>> It is true that the RAML definitions of APIs should be as detailed as possible and should reflect most of the documentation. However, just that is NOT enough to call your API as best documented API. There should be even more documentation on Anypoint Exchange with API Notebooks etc. to make and create a developer friendly API and repository..
>> The best practice always when creating System APIs is to create their API interfaces by modeling their resources and methods to closely reflect the operations and functionalities of that backend system.
質問 # 80
Question 10: Skipped
An API implementation returns three X-RateLimit-* HTTP response headers to a requesting API client. What type of information do these response headers indicate to the API client?
- A. A correlation ID that should be sent in the next request
- B. The HTTP response size
- C. The remaining capacity allowed by the API implementation
- D. The error codes that result from throttling
正解：C
解説：
Correct Answe r: The remaining capacity allowed by the API implementation.
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>> Reference: https://docs.mulesoft.com/api-manager/2.x/rate-limiting-and-throttling-sla-based-policies#response-headers
質問 # 81
A Mule application exposes an HTTPS endpoint and is deployed to three CloudHub workers that do not use static IP addresses. The Mule application expects a high volume of client requests in short time periods. What is the most cost-effective infrastructure component that should be used to serve the high volume of client requests?
- A. The CloudHub shared load balancer
- B. An API proxy
- C. Runtime Manager autoscaling
- D. A customer-hosted load balancer
正解：A
解説：
Correct Answer : The CloudHub shared load balancer
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The scenario in this question can be split as below:
>> There are 3 CloudHub workers (So, there are already good number of workers to handle high volume of requests)
>> The workers are not using static IP addresses (So, one CANNOT use customer load-balancing solutions without static IPs)
>> Looking for most cost-effective component to load balance the client requests among the workers.
Based on the above details given in the scenario:
>> Runtime autoscaling is NOT at all cost-effective as it incurs extra cost. Most over, there are already 3 workers running which is a good number.
>> We cannot go for a customer-hosted load balancer as it is also NOT most cost-effective (needs custom load balancer to maintain and licensing) and same time the Mule App is not having Static IP Addresses which limits from going with custom load balancing.
>> An API Proxy is irrelevant there as it has no role to play w.r.t handling high volumes or load balancing.
So, the only right option to go with and fits the purpose of scenario being most cost-effective is - using a CloudHub Shared Load Balancer.
質問 # 82
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