MuleSoft-Platform-Architect-I Salesforce Certified MuleSoft Platform Architect 1 Exam (WI25) Questions and Answers

Explanation

Correct Answer: Apply a JSON threat protection policy to all APIs to detect potential threat vectors

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>> Usually, if the APIs are designed and developed for specific consumers (known consumers/customers) then we would IP Whitelist the same to ensure that traffic only comes from them.

>> However, as this scenario states that the APIs are publicly available and being used by so many mobile and web applications, it is NOT possible to identify and blacklist all possible bad actors.

>> So, JSON threat protection policy is the best chance to prevent any bad JSON payloads from such bad actors.

Explanation

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

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>> 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-2 019

https://help.mulesoft.com/s/article/On-premise-Runtimes-Appear-Disconnected-in-Runtime-Manager-May-29th-2018

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Security Guidelines Overview:

The production environment is hosted on a dedicated Virtual Private Cloud (VPC) on CloudHub 1.0, with a specific requirement from the security team that no traffic should occur over HTTP. This implies that only secure HTTPS traffic should be permitted, and HTTP access (port 8081, the default HTTP port in Mule applications) should be disabled.

Evaluating the Options:

Option A (Correct Answer): Configuring the HTTPS protocol in the HTTP listener in the Mule application ensures that all traffic is encrypted and occurs over HTTPS (port 8092 by default for HTTPS on Mule applications). This directly aligns with the security guideline to prevent unencrypted HTTP traffic.

Option B: Creating a custom policy for incoming and outgoing HTTP requests could provide some control over access, but it does not enforce the use of HTTPS exclusively. This option does not disable HTTP traffic and, therefore, does not meet the guideline effectively.

Option C (Correct Answer): Removing the entry for HTTP (port 8081) in the VPC firewall rule ensures that HTTP traffic is completely blocked at the firewall level. This prevents any HTTP requests from reaching the application, adding a layer of security that complies with the guidelines.

Option D: The IP Blocklist policy controls access based on IP addresses but does not enforce the use of HTTPS. This policy does not address the specific requirement of preventing HTTP traffic.

Option E: Adding a firewall rule entry for HTTP (port 8081) would enable HTTP traffic, which directly contradicts the security guidelines. Therefore, this option should be avoided.

Conclusion:

Option A and Option C are the correct choices. Configuring the HTTPS protocol in the Mule application’s HTTP listener ensures that only HTTPS traffic is allowed, and removing the firewall rule for HTTP (port 8081) blocks any HTTP traffic from reaching the application. Together, these options enforce secure traffic as required by the security guidelines.

Refer to MuleSoft documentation on configuring HTTP listeners and managing VPC firewall rules for further details on implementing these security controls.

To accommodate the new requirement of allowing updates to existing quotes, the following actions should be taken:

Update the RAML Definition (Option C):

The RAML specification defines the structure and behavior of the API. Adding a new method (such as PUT or PATCH) for updating quotes requires modifying the RAML to include this new endpoint. This ensures the API specification is up-to-date and accurately reflects the new functionality.

Update the API Implementation (Option A):

Once the RAML is updated, the backend API implementation must also be modified to handle the new update requests. This could involve adding logic to process and validate update requests, connect to necessary backend resources, and apply the changes to existing quotes.

Explanation of Incorrect Options:

Option B (removing and creating new clients) is unnecessary; client applications can remain as they are, with no need for complete replacement.

Option D (deprecating existing versions) may not be required if backward compatibility is maintained.

Option E (adding a new policy) does not facilitate functional changes and is unrelated to implementing the update feature.

ReferencesFor more details on updating RAML definitions and API implementations, refer to MuleSoft’s API Design documentation on RAML and RESTful API practices​.

Explanation

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.

Explanation

Correct Answers: API Design and Development and API Runtime Execution and Hosting

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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

Explanation

Correct Answers: API Operations and Management and 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

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Explanation

Correct Answer: SLA-based rate limiting

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>> Client Id enforement policy is a "Compliance" related NFR and does not help in maintaining the "Quality of Service (QoS)". It CANNOT and NOT meant for protecting the backend systems from scalability challenges.

>> IP Whitelisting and OAuth 2.0 token enforcement are "Security" related NFRs and again does not help in maintaining the "Quality of Service (QoS)". They CANNOT and are NOT meant for protecting the backend systems from scalability challenges.

Rate Limiting, Rate Limiting-SLA, Throttling, Spike Control are the policies that are "Quality of Service (QOS)" related NFRs and are meant to help in protecting the backend systems from getting overloaded.

https://dzone.com/articles/how-to-secure-apis

For a European company with requirements such as zero-downtime redeployment, deployment to multiple runtime versions, secure and fast performance, and the use of Anypoint MQ without additional network configuration, CloudHub is the best choice for the following reasons:

Zero-Downtime Redeployment: CloudHub supports zero-downtime deployment, which allows seamless redeployment of applications without impacting availability.

Support for Multiple Runtime Versions: CloudHub allows deploying applications across different Mule runtime versions, giving flexibility to test and migrate applications as needed.

Integrated Anypoint MQ: Anypoint MQ, which is fully integrated with CloudHub, provides reliable messaging across applications. Choosing CloudHub removes the need for additional network configurations, as Anypoint MQ can be directly accessed in this hosted environment.

Security and Performance: CloudHub offers secure networking, automatic scaling, and optimized performance without requiring a complex setup. This is managed by MuleSoft’s infrastructure, meeting the speed and security requirements with minimal overhead.

Explanation of Incorrect Options:

Option A and D (Runtime Fabric on VMs or Self-Managed Kubernetes): While Runtime Fabric offers flexibility, it requires more complex network and infrastructure configurations, which is not ideal if the company seeks simplicity.

Option B (Customer-hosted): This would require additional network and security configuration, which does not align with the requirement of minimizing setup complexity.

ReferencesFor more information on CloudHub’s capabilities regarding zero-downtime deployments and integration with Anypoint MQ, refer to MuleSoft documentation on CloudHub​​.

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.

Explanation 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.

ReferencesFor more on CloudHub’s autoscaling configuration, refer to MuleSoft documentation on CloudHub autoscaling policies​.

Explanation

Correct Answer: Separate Experience APIs for the mobile and web app, but a common Process API that invokes separate System APIs created for the database and CRM system

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As per MuleSoft's API-led connectivity:

>> Experience APIs should be built as per each consumer needs and their experience.

>> Process APIs should contain all the orchestration logic to achieve the business functionality.

>> System APIs should be built for each backend system to unlock their data.

In scenarios where multiple API implementations are deployed across different environments (CloudHub and customer-hosted runtimes), Anypoint Functional Monitoring is the most effective tool to monitor API availability and trigger alerts when an API implementation becomes unresponsive. Here’s how it works:

Using Anypoint Functional Monitoring:

Functional Monitoring allows you to create monitors that periodically invoke specific endpoints on the API implementations, simulating a client request. This helps ensure that the API is responsive, even if no actual client requests are being made.

If an API implementation does not respond as expected, Functional Monitoring can generate alerts, notifying administrators of potential issues.

Why Option A is Correct:

By setting up Functional Monitoring to automatically invoke the API endpoints at regular intervals, you ensure continuous monitoring and alerting capabilities, which are especially useful for APIs that may experience periods of low or no traffic. This approach provides a proactive solution, allowing you to identify and address issues before actual users are impacted.

Explanation of Incorrect Options:

Option B suggests modifying client applications to trigger alerts, which is not a best practice as it shifts monitoring responsibility to clients, reducing control and consistency.

Option C involves handling exceptions within client applications, which does not address situations where no clients are making requests.

Option D proposes a Worker Not Responding alert in Runtime Manager, which is limited to worker-specific alerts and may not reliably monitor the API's actual responsiveness to requests.

ReferencesFor further information, refer to MuleSoft documentation on Anypoint Functional Monitoring setup and usage for API availability monitoring​.

For organizations with established CI/CD practices, the Mule Maven plugin is the recommended tool for automating packaging and deployment across multiple environments, including CloudHub, on-premise Mule runtimes, and Anypoint Runtime Fabric. Here’s why:

Automation with Maven:

The Mule Maven plugin allows for CI/CD integration by supporting automated build and deployment processes. It is commonly used in CI/CD pipelines to handle application packaging and deployment directly through Maven commands, making it ideal for teams that want consistent deployment automation across different MuleSoft environments.

Supported Deployment Targets:

The Mule Maven plugin supports deployment to various targets, including CloudHub, Runtime Fabric, and on-premises servers, thus meeting the needs of environments with diverse deployment destinations.

Why Option B is Correct:

The Mule Maven plugin is specifically designed for CI/CD pipelines and integrates with Jenkins, GitLab, and other CI/CD tools to facilitate continuous deployment. It is the most efficient MuleSoft-provided tool for this purpose.

Explanation of Incorrect Options:

Option A (Anypoint Runtime Manager) provides deployment management but does not automate CI/CD processes.

Option C (Anypoint Platform CLI) can script deployments but lacks direct integration with CI/CD tools.

Option D (Anypoint Platform REST APIs) requires custom scripting for deployment, which can be more complex than using the Mule Maven plugin.

ReferencesFor more details, refer to MuleSoft documentation on using the Mule Maven plugin for CI/CD​​.

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.

ReferencesFor 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​​.

Explanation

Correct Answer: A Non-Mule application

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>> 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.

Explanation

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.

Understanding Idempotency:

Idempotency is a concept in APIs where an operation can be performed multiple times without changing the result beyond the initial application. This is particularly important for operations that may be repeated due to network retries or client errors.

When to Consider Idempotency:

Idempotency should be taken into account when there is a risk of duplicate processing due to multiple requests being sent (e.g., retries or errors). This ensures that repeated requests do not result in unintended side effects, such as creating multiple records or processing the same transaction more than once.

Evaluating the Options:

Option A: While locked entities may need special handling, this is not directly related to idempotency.

Option B: Storing results for future responses could be useful but does not relate to idempotent operations.

Option C: Concurrent requests for the same entity might require handling for conflicts, but this scenario is better suited for transaction management or concurrency control.

Option D (Correct Answer): Preventing duplicate processing from multiple requests is a key reason to implement idempotency, ensuring that repeat requests have no adverse effects.

Conclusion:

Option D is the correct answer as idempotency is specifically used to handle scenarios where duplicate requests might be sent, preventing unintended processing.

Refer to MuleSoft's documentation on best practices for idempotency in API design for more details.

Explanation

Correct Answer: API Consumer requests access to API >> API Client implementes logic to call an API >> API routes the request to >> API Implementation

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>> API consumer does not implement any logic to invoke APIs. It is just a role. So, the option stating "API Consumer implementes logic to call an API" is INVALID.

>> API Implementation does not route any requests. It is a final piece of logic where functionality of target systems is exposed. So, the requests should be routed to the API implementation by some other entity. So, the options stating "API Implementation routes the request to >> API" is INVALID

>> The statements in one of the options are correct but sequence is wrong. The sequence is given as "API Client implementes logic to call an API >> API Consumer requests access to API >> API routes the request to >> API Implementation". Here, the statements in the options are VALID but sequence is WRONG.

>> Right option and sequence is the one where API consumer first requests access to API on Anypoint Exchange and obtains client credentials. API client then writes logic to call an API by using the access client credentials requested by API consumer and the requests will be routed to API implementation via the API which is managed by API Manager.

Explanation

Correct Answer: Manually provisioned customer-hosted runtime plane and customer-hosted control plane

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There are two key factors that are to be taken into consideration from the scenario given in the question.

>> Company requires both data and metadata to be resided within the corporate firewall

>> Company would like to go with customer-hosted infrastructure.

Any deployment model that is to deal with the cloud directly or indirectly (Mulesoft-hosted or Customer's own cloud like Azure, AWS) will have to share atleast the metadata.

Application data can be controlled inside firewall by having Mule Runtimes on customer hosted runtime plane. But if we go with Mulsoft-hosted/ Cloud-based control plane, the control plane required atleast some minimum level of metadata to be sent outside the corporate firewall.

As the customer requirement is pretty clear about the data and metadata both to be within the corporate firewall, even though customer wants to move to production as quickly as possible, unfortunately due to the nature of their security requirements, they have no other option but to go with manually provisioned customer-hosted runtime plane and customer-hosted control plane.

Explanation

Correct Answer: A higher number of discoverable API-related assets in the application network.

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>> We do NOT get faster response times in fine-grained approach when compared to coarse-grained approach.

>> In fact, we get faster response times from a network having coarse-grained APIs compared to a network having fine-grained APIs model. The reasons are below.

Fine-grained approach:

1. will have more APIs compared to coarse-grained

2. So, more orchestration needs to be done to achieve a functionality in business process.

3. Which means, lots of API calls to be made. So, more connections will needs to be established. So, obviously more hops, more network i/o, more number of integration points compared to coarse-grained approach where fewer APIs with bulk functionality embedded in them.

4. That is why, because of all these extra hops and added latencies, fine-grained approach will have bit more response times compared to coarse-grained.

5. Not only added latencies and connections, there will be more resources used up in fine-grained approach due to more number of APIs.

That's why, fine-grained APIs are good in a way to expose more number of resuable assets in your network and make them discoverable. However, needs more maintenance, taking care of integration points, connections, resources with a little compromise w.r.t network hops and response times.

In the initial meeting for establishing a Center for Enablement (C4E), it’s essential to lay the foundational vision, objectives, and guiding principles for the team. Here’s why this is crucial:

Clear Vision and Mission:

Defining the mission statement and objectives at the start ensures alignment within the organization and clarifies the C4E’s role in supporting API-led development and integration practices.

Guiding Principles:

Establishing guiding principles will help the C4E maintain consistent practices and strategies across projects. This serves as a framework for decisions and fosters shared understanding among IT leaders and stakeholders.

Explanation of Correct Answer (A):

By prioritizing the C4E’s objectives and mission, the organization builds a solid foundation, paving the way for subsequent meetings focused on technical standards, processes, and operating models.

Explanation of Incorrect Options:

Option B (API monetization) and Option C (common services best practices) are specific topics better suited for later discussions.

Option D (specifying the operating model) is an important step but typically follows the establishment of the C4E’s objectives and vision.

ReferencesFor more on C4E objectives and foundational setup, refer to MuleSoft’s documentation on establishing a C4E and the roles and mission statements recommended for such initiatives​.

Explanation

Correct Answer: Set a timeout of 100ms; that leaves 400ms for other two downstream APIs to complete

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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.

Explanation

Correct Answer: When the API implementation changes the structure of the request or response messages

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>> 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..

Explanation

Correct Answer: Create API Notebooks and Include them in the relevant Anypoint exchange entries

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>> API Notebooks are the one on Anypoint Platform that enable us to provide code-centric API documentation

https://www.folkstalk.com/2019/11/mulesoft-integration-and-platform.html

Explanation

Correct Answer: To invoke OAuth 2.0-protected APIs managed by Anypoint Platform, API clients must submit access tokens issued by that same Identity Provider

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>> It is NOT necessary that single sign-on is required to sign in to Anypoint Platform because we are using an external Identity Provider for Client Management

>> It is NOT necessary that all APIs managed by Anypoint Platform must be protected by SAML 2.0 policies because we are using an external Identity Provider for Client Management

>> Not TRUE that the application network must include System APIs that interact with the Identity Provider because we are using an external Identity Provider for Client Management

Only TRUE statement in the given options is - "To invoke OAuth 2.0-protected APIs managed by Anypoint Platform, API clients must submit access tokens issued by that same Identity Provider"

References:

https://docs.mulesoft.com/api-manager/2.x/external-oauth-2.0-token-validation-policy

https://blogs.mulesoft.com/dev/api-dev/api-security-ways-to-authenticate-and-authorize/

Explanation

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.

In this scenario, the best approach to satisfy the API-led connectivity principles and support future scalability is:

Experience APIs:

Create separate Experience APIs for the web application and the mobile application. This allows each application to have an optimized interface, supporting different needs and potential differences in request/response structures or security configurations.

Process API:

A single Process API can be used to orchestrate the workflow, including retrieving the email template from a database and preparing the email content. By centralizing this logic in the Process layer, we can ensure it is reusable and easily adaptable for different notification channels in the future.

System API:

A System API specifically designed for sending emails (using the Anypoint Connector for Email) abstracts the email-sending functionality from the business logic. This approach ensures that the email-sending function is reusable and scalable, and it can easily be extended or modified if other notification channels (like SMS or push notifications) are added later.

Why Option A is Correct:

This structure aligns with API-led connectivity principles by separating concerns across Experience, Process, and System layers. It provides flexibility for future notification channels and isolates each layer’s responsibility, making it easier to maintain and scale.

Explanation of Incorrect Options:

Option B lacks a separate System API for sending emails, which goes against the principle of isolating back-end functionality in System APIs.

Option C similarly lacks a dedicated System API, reducing flexibility and reusability.

Option D suggests creating multiple Process APIs for database retrieval, which adds unnecessary complexity and does not adhere to the single-orchestration principle typically followed in API-led design.

ReferencesFor further guidance on API-led connectivity and the responsibilities of each API layer, refer to MuleSoft’s documentation on API-led architecture and design best practices​​.

Explanation

Correct Answer: Ask the Marketing Department to interact with a mocking implementation of the API using the automatically generated API Console.

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As per MuleSoft's IT Operating Model:

>> API consumers need NOT wait until the full API implementation is ready.

>> NO technical test-suites needs to be shared with end users to interact with APIs.

>> Anypoint Platform offers a mocking capability on all the published API specifications to Anypoint Exchange which also will be rich in documentation covering all details of API functionalities and working nature.

>> No needs of arranging days of workshops with end users for feedback.

API consumers can use Anypoint Exchange features on the platform and interact with the API using its mocking feature. The feedback can be shared quickly on the same to incorporate any changes.

Explanation

Correct Answer: When server-side load-balanced TLS mutual authentication is required between API implementations and API clients

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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.

Explanation

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

Understanding API Functional Monitoring:

API Functional Monitoring is a feature within MuleSoft's Anypoint Platform that enables users to monitor the health and performance of APIs and endpoints by running functional tests at scheduled intervals.

It checks whether APIs are functioning as expected by running test calls and then evaluating if the response meets the desired conditions. This is particularly useful for testing endpoint availability, checking for specific data in responses, and measuring API performance over time.

Component Features:

Scheduled Intervals: Functional monitoring allows configuring tests to run at regular intervals, such as every minute, hour, or day, depending on the monitoring requirements.

Reports on Test Pass/Fail Status: After each test run, API Functional Monitoring reports whether the API passed or failed the test conditions.

Performance Statistics: It displays metrics like average response time, success rate, and error rates, giving insights into API health and performance.

Evaluating the Options:

Option A (API Analytics): API Analytics provides insights on API usage and metrics but does not involve scheduled tests for pass/fail status or endpoint health checks.

Option B (Anypoint Monitoring Dashboards): These dashboards display API metrics but do not actively test API endpoints or provide pass/fail reporting on a scheduled basis.

Option C (Correct Answer): API Functional Monitoring fits the description, as it is designed to monitor API and endpoint health with scheduled test runs and display statistics about performance.

Option D (Anypoint Runtime Manager Alerts): Runtime Manager alerts notify users of issues with application status but do not actively test endpoints at scheduled intervals.

Conclusion:

Option C (API Functional Monitoring) is the correct answer because it provides the necessary tools to test API functionality, monitor endpoint health, and display performance statistics in real-time.

Refer to MuleSoft documentation on API Functional Monitoring for further guidance on setting up and configuring these tests in Anypoint Platform.

Explanation

Correct Answer: At the process and system tiers

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>> Experience Layer APIs are modeled and designed exclusively for the end user's experience. So, the data models of experience layer vary based on the nature and type of such API consumer. For example, Mobile consumers will need light-weight data models to transfer with ease on the wire, where as web-based consumers will need detailed data models to render most of the info on web pages, so on. So, enterprise data models fit for the purpose of canonical models but not of good use for experience APIs.

>> That is why, EDMs should be used extensively in process and system tiers but NOT in experience tier.

Explanation

Correct Answer: When regulatory requirements mandate on-premises processing of EVERY data item, including meta-data.

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We need NOT require to use Anypoint Platform PCE or PCF for the below. So these options are OUT.

>> We can make ALL applications highly available across multiple data centers using CloudHub too.

>> We can use Anypoint VPN and tunneling from CloudHub to connect to ALL backend systems in the application network that are deployed in the organization's intranet.

>> We can use Anypoint VPC and Firewall Rules to make ALL APIs private and NOT exposed to the public cloud.

Only valid reason in the given options that requires to use Anypoint Platform PCE/ PCF is - When regulatory requirements mandate on-premises processing of EVERY data item, including meta-data.

Understanding Circuit Breaker Policy:

A circuit breaker is a design pattern used to detect failures and prevent an application from continually trying to execute a failing operation. In this case, it will help improve response time and reduce demand on the downstream API.

The specified configuration includes conditions for opening, half-opening, and closing the circuit based on error rates over time:

Circuit Open: Triggered if there are more than 10 errors per minute for three consecutive minutes.

Circuit Half-Open: The circuit transitions to half-open if there is one error per minute.

Circuit Closed: The circuit closes if the error rate is less than one error per minute for five minutes.

Evaluating the Options:

Option A: Creating a custom policy with template expressions could work, but it would require custom development. Since the Anypoint Platform already has a Circuit Breaker policy available, this would be a less efficient and more complex solution.

Option B: Anypoint Monitoring alerts can be used for monitoring the API, but they do not provide circuit-breaking functionality. Additionally, implementing a retry strategy for the half-open state is not sufficient to achieve the required circuit breaker behavior.

Option C (Correct Answer): Adding the Circuit Breaker policy to the API instance on Anypoint Platform allows you to set up circuit-breaking conditions directly. This approach uses the built-in Circuit Breaker policy, where you can configure parameters such as error thresholds and time intervals to match the requirements. This solution is efficient, reliable, and leverages Anypoint’s out-of-the-box capabilities.

Option D: Implementing the strategy within a Mule application with a YAML configuration could be complex and less manageable. Additionally, it does not leverage Anypoint Platform’s built-in Circuit Breaker policy, which is more suited to this scenario.

Conclusion:

Option C is the correct choice, as it leverages Anypoint Platform’s Circuit Breaker policy. This solution allows for configuring thresholds and time intervals as specified, improving response time and reducing demand on the downstream API while utilizing Anypoint's managed policy feature.

Refer to MuleSoft’s documentation on implementing the Circuit Breaker policy in API Manager for detailed configuration guidance.

When setting up a custom domain for MuleSoft applications hosted on CloudHub 1.0 using a Dedicated Load Balancer (DLB), follow these steps:

Set Up the TLS Certificate: Configure the DLB (acme-dib-prod) with a TLS certificate that covers the custom domain api.acmecorp.com. This certificate will allow HTTPS traffic to be securely directed through the DLB to your Mule applications.

DNS Configuration with CNAME:

Create a CNAME record that points api.acmecorp.com to the DLB hostname acme-dib-prod.lb.anypointdns.net.

The CNAME record enables the custom domain to resolve to the DLB provided by MuleSoft's Anypoint Platform. This CNAME mapping directs all traffic to the correct DLB for processing and load distribution.

Why Option B is Correct:

A CNAME record provides the necessary aliasing to acme-dib-prod.lb.anypointdns.net, which is the endpoint managed by Anypoint Platform for your DLB.

Option B also correctly identifies the need to configure the DLB with a TLS certificate specifically for api.acmecorp.com rather than for the DLB's internal hostname.

Explanation of Incorrect Options:

Options that suggest configuring the DLB with a TLS certificate for the DLB’s internal hostname or using an A record are not suitable in this scenario. MuleSoft CloudHub 1.0 DLBs work with CNAME records to provide flexible and scalable domain management, and a direct IP (A record) is not supported for these load balancers.

ReferencesFor more information on configuring custom domains and DLBs on CloudHub 1.0, refer to the MuleSoft documentation on DLB setup and DNS configuration​​.

Understanding the API Flow and Timeout Requirement:

The Order API initiates a call to the OrderItems process API, which in turn calls the Inventory system API to fetch details from the inventory database.

The requirement specifies that the Order API should not wait more than two seconds for a response from the OrderItems process API, even if there are delays further down the chain (between Inventory system API and the database).

Choosing the Appropriate Timeout Location:

Setting the timeout at the OrderItems process API level ensures that if the Inventory system API takes longer than two seconds to respond, the OrderItems process API will terminate the request and send a timeout response back to the Order API. This prevents the Order API from waiting indefinitely due to the unreliable connection to the database.

If the timeout were set in the Inventory system API or database, it would not help the Order API directly, as the OrderItems process API would still be waiting for a response.

Detailed Analysis of Each Option:

Option A (Correct Answer): Setting the timeout in the OrderItems process API allows it to control how long it waits for a response from the Inventory system API. If the Inventory system API does not respond within two seconds, the OrderItems process API can terminate the call and return a timeout response to the Order API, meeting the requirement.

Option B: Setting the timeout in the Order API would not limit the wait time at the OrderItems process API level, meaning the OrderItems process API could still wait indefinitely for the Inventory system API, leading to a longer delay.

Option C: Setting the timeout in the Inventory system API only affects the connection to the database and does not influence how long the OrderItems process API waits for the Inventory system API's response.

Option D: Setting a timeout in the database is not feasible in this context since database timeouts are typically configured for database operations and would not directly control the API response times in the overall API chain.

Conclusion:

Option A is the best choice, as it ensures that the OrderItems process API does not hold the Order API longer than the required two seconds, even if the downstream connection to the database hangs. This configuration aligns with MuleSoft best practices for setting timeouts in API orchestration to manage dependencies and prevent delays across a chain of API calls.

For additional information on timeout settings, refer to MuleSoft documentation on handling timeouts and API orchestration best practices.

Understanding the Requirements:

The business needs to transfer daily data changes from the Salesforce Account and Case objects to AWS DynamoDB in a single JSON format.

Only a subset of attributes (5 from Account and 8 from Case) is required, so it is not necessary to include all 219 attributes of the Account object.

Design Approach:

A System API (SAPI) should be created for each Salesforce object (Account and Case), exposing only the required fields (5 attributes for Account and 8 for Case).

A Process API (PAPI) can be used to aggregate and transform the data from these SAPIs, combining the 13 selected attributes from Account and Case into a single JSON structure for DynamoDB.

Evaluating the Options:

Option A: Mimicking all attributes in the SAPI is inefficient and unnecessary, as only 13 attributes are required.

Option B: Replicating all attributes in DynamoDB is excessive and would result in higher storage and processing costs, which is unnecessary given the requirement for only a subset of attributes.

Option C: Implementing an Enterprise Data Model could be useful in broader data management but is not required here, as the focus is on a lightweight integration.

Option D (Correct Answer): Creating separate entities in SAPI for Account and Case with only the required attributes and using the PAPI to aggregate them into a single JSON is the most efficient and meets the requirements effectively.

Conclusion:

Option D is the best choice as it provides a lightweight, efficient design that meets the requirements by transferring only the necessary attributes and minimizing resource use.

Refer to MuleSoft's best practices for API-led connectivity and data modeling to structure SAPIs and PAPIs efficiently.

Correct Answer: Persistent Object Store

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>> Redis distributed cache is performant but NOT out-of-the-box solution in Anypoint Platform

>> File-storage is neither performant nor out-of-the-box solution in Anypoint Platform

>> java.util.WeakHashMap needs a completely custom implementation of cache from scratch using Java code and is limited to the JVM where it is running. Which means the state in the cache is not worker aware when running on multiple workers. This type of cache is local to the worker. So, this is neither out-of-the-box nor worker-aware among multiple workers on cloudhub. https://www.baeldung.com/java-weakhashmap

>> Persistent Object Store is an out-of-the-box solution provided by Anypoint Platform which is performant as well as worker aware among multiple workers running on CloudHub. https://docs.mulesoft.com/object-store/

So, Persistent Object Store is the right answer.

For applications deployed on CloudHub in a foreign region (e.g., Singapore), MuleSoft handles log and monitoring data in the region where the control plane resides. This data storage policy is standard for CloudHub deployments to maintain centralized log and monitoring data.

Data Location:

For a U.S.-based control plane, all logs and monitoring data are stored in the United States, regardless of the deployment region.

Although the application itself runs in Singapore, data related to application performance and logs is not localized to the deployment region.

Explanation of Correct Answer (B):

Since the control plane is based in the United States, all operational data like logs and monitoring will also be stored there, ensuring compliance with MuleSoft’s data handling policies.

Explanation of Incorrect Options:

Option A and D are incorrect because MuleSoft does not store logs or monitoring data in the application deployment region when the control plane is located in the United States.

Option C suggests mixed storage, which does not align with MuleSoft’s data policy structure.

ReferencesFor details on data residency in CloudHub deployments, refer to MuleSoft’s documentation on CloudHub control planes and data handling policies​.

Understanding Nonfunctional Requirements (NFRs):

The NFRs in this context are related to security measures implemented for the Web API, such as rate limiting, LDAP-based authentication, JSON threat protection, and IP allowlist policies.

Evaluating the Options:

Option A (Correct Answer): The IP allowlist policy restricts access to known subnets, ensuring that API invocations come from a defined range of IPs.

Option B (Correct Answer): Basic Authentication with LDAP enforces a username/password validation, satisfying an NFR for identity verification.

Option C: Masking sensitive data is not part of the listed NFRs, as none of the mentioned policies address data masking.

Option D: XML threat protection is not mentioned, so this option is incorrect.

Option E: While rate-limiting implies performance control, it does not directly enforce a specific performance expectation.

Conclusion:

Options A and B are correct as they directly address the implemented security measures related to IP range restrictions and username/password authentication.

Refer to MuleSoft's documentation on API security policies for details on LDAP, rate limiting, and allowlist policies.

Understanding JSON Threat Protection Policies:

JSON Threat Protection policies are used to protect APIs from attacks that exploit JSON payloads, such as oversized payloads, deeply nested objects, and excessive array elements. This helps prevent Denial of Service (DoS) attacks and other malicious payload-related threats.

These policies are typically applied to safeguard APIs that are directly exposed to external clients, where the risk of receiving malicious payloads is highest.

API-led Connectivity Layers:

Experience Layer: This layer is designed to expose APIs to end-users or external API clients, often acting as the interface that interacts with users or applications.

Process Layer: This layer is used for orchestration and aggregation of data from various System APIs, typically operating within a trusted environment and not directly exposed to external clients.

System Layer: This layer provides access to backend systems and databases, often within the organization’s secure environment and not directly accessible to external clients.

Evaluating the Options:

Option A (All layers): While JSON Threat Protection can technically be applied to all layers, it is most commonly applied at the Experience layer, where APIs are exposed to external traffic and are more vulnerable to attacks.

Option B (System layer): The System layer is generally not exposed to external clients directly, so JSON Threat Protection is less critical here.

Option C (Process layer): Similar to the System layer, the Process layer is typically internal and not exposed directly to external clients, so JSON Threat Protection is less commonly applied.

Option D (Correct Answer): The Experience layer is the correct answer because it is the layer that directly interacts with external clients, making it the primary target for malicious payloads. Applying JSON Threat Protection here effectively protects the application network from external threats.

Conclusion:

Option D is the correct answer, as the Experience layer is the most common layer for applying JSON Threat Protection policies to protect against external attacks.

For further reference, consult MuleSoft's documentation on API security policies and best practices for securing APIs at the Experience layer.