Salesforce MuleSoft-Platform-Architect-I - Salesforce Certified MuleSoft Platform Architect (Mule-Arch-201)

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 a pragmatic approach with only limited isolation from the backend system is deemed appropriate.

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General guidance w.r.t choosing Data Models:

>> If an Enterprise Data Model is in use then the API data model of System APIs should make use of data types from that Enterprise Data Model and the corresponding API implementation should translate between these data types from the Enterprise Data Model and the native data model of the backend system.

>> If no Enterprise Data Model is in use then each System API should be assigned to a Bounded Context, the API data model of System APIs should make use of data types from the corresponding Bounded Context Data Model and the corresponding API implementation should translate between these data types from the Bounded Context Data Model and the native data model of the backend system. In this scenario, the data types in the Bounded Context Data Model are defined purely in terms of their business characteristics and are typically not related to the native data model of the backend system. In other words, the translation effort may be significant.

>> If no Enterprise Data Model is in use, and the definition of a clean Bounded Context Data Model is considered too much effort, then the API data model of System APIs should make use of data types that approximately mirror those from the backend system, same semantics and naming as backend system, lightly sanitized, expose all fields needed for the given System API’s functionality, but not significantly more and making good use of REST conventions.

The latter approach, i.e., exposing in System APIs an API data model that basically mirrors that of the backend system, does not provide satisfactory isolation from backend systems through the System API tier on its own. In particular, it will typically not be possible to "swap out" a backend system without significantly changing all System APIs in front of that backend system and therefore the API implementations of all Process APIs that depend on those System APIs! This is so because it is not desirable to prolong the life of a previous backend system’s data model in the form of the API data model of System APIs that now front a new backend system. The API data models of System APIs following this approach must therefore change when the backend system is replaced.

On the other hand:

>> It is a very pragmatic approach that adds comparatively little overhead over accessing the backend system directly

>> Isolates API clients from intricacies of the backend system outside the data model (protocol, authentication, connection pooling, network address, …)

>> Allows the usual API policies to be applied to System APIs

>> Makes the API data model for interacting with the backend system explicit and visible, by exposing it in the RAML definitions of the System APIs

>> Further isolation from the backend system data model does occur in the API implementations of the Process API tier

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>> Application Network is a disposable architecture.

>> Which means, it can be altered without disturbing entire architecture and its components.

>> It bends as per requirements or design changes but does not break

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.

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.

Explanation

Correct Answer: Implement different system APIs named createCustomer, amendCustomer, retrieveCustomer and suspendCustomer as they are modular and has seperation of concerns

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>> It is quite normal to have a single API and different Verb + Resource combinations. However, this fits well for an Experience API or a Process API but not a best architecture style for System APIs. So, option with just one customerManagement API is not the best choice here.

>> The option with APIs in createCustomerInCRMZ format is next close choice w.r.t modularization and less maintenance but the naming of APIs is directly coupled with the legacy system. A better foreseen approach would be to name your APIs by abstracting the backend system names as it allows seamless replacement/migration of any backend system anytime. So, this is not the correct choice too.

>> createCustomer, amendCustomer, retrieveCustomer and suspendCustomer is the right approach and is the best fit compared to other options as they are both modular and same time got the names decoupled from backend system and it has covered all requirements a System API needs.

Understanding the Data Flow Between Mule Runtimes and Control Plane:

When Mule applications are deployed on customer-hosted Mule runtimes, the MuleSoft-hosted control plane (Anypoint Platform) can monitor and manage these applications. However, due to data privacy and security, the control plane only collects specific types of information.

Typically, only metadata about the request and response (such as headers, status codes, and timestamps) is sent to the MuleSoft-hosted control plane. The actual payload data is not transmitted unless explicitly configured, ensuring that sensitive data remains within the customer's network.

Evaluating the Options:

Option A (Only the data): This is incorrect because the payload data itself is not automatically sent to the control plane in default configurations.

Option B (No data): This is incorrect as well; while the payload is not sent, metadata is still collected and sent to the control plane.

Option C (The data and metadata): This option is incorrect because data (payload) is not transmitted to the control plane by default.

Option D (Correct Answer): Only the metadata is sent to the MuleSoft-hosted control plane by default, aligning with MuleSoft’s design to prioritize security and data privacy for customer-hosted runtimes.

Conclusion:

Option D is the correct answer, as by default, only metadata is sent to the MuleSoft-hosted control plane, and not the payload. This configuration is designed to protect sensitive data from being exposed outside the customer’s hosted environment.

For more details, refer to MuleSoft’s documentation on telemetry data collected in customer-hosted Mule runtimes and the MuleSoft control plane.

Understanding Control and Runtime Planes:

Control Plane: The control plane is responsible for managing, monitoring, and deploying Mule applications. In a Private Cloud Edition (PCE), this control plane is deployed on-premises within the customer’s infrastructure, meeting data residency and security requirements.

Runtime Plane: The runtime plane consists of Mule runtimes that execute Mule applications. By hosting these runtimes within the customer’s infrastructure, data and metadata can remain local, which complies with corporate policies regarding data residency.

Evaluating the Options:

Option A: Using Anypoint Platform Private Cloud Edition for the control plane and the MuleSoft-hosted runtime plane would not meet the requirement, as the runtime plane is hosted by MuleSoft and would not keep data local.

Option B: The MuleSoft-hosted control plane with Anypoint Runtime Fabric for the runtime plane would still mean that metadata is managed in MuleSoft’s cloud, which does not comply with the requirement to keep data and metadata on-premises.

Option C: A MuleSoft-hosted control plane and customer-hosted Mule runtimes also mean that metadata resides in the cloud, not on-premises, failing the residency requirement.

Option D (Correct Answer): Anypoint Platform Private Cloud Edition (PCE) for the control plane and customer-hosted Mule runtimes fulfill both requirements, as both the control plane and runtime plane would be hosted within the customer’s data center.

Conclusion:

Option D is the correct answer, as it ensures that both the control plane and runtime plane are hosted on-premises, allowing data and metadata to reside locally per the corporate IT policy.

Refer to MuleSoft's documentation on Private Cloud Edition deployment and on-premise runtime configurations for further details.

Explanation

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

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>> 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.