Salesforce MuleSoft-Platform-Architect-I Salesforce Certified MuleSoft Platform Architect 1 Exam (WI25) Exam Practice Test

Explanation

Correct Answer: System Layer

The APIs used in an API-led approach to connectivity fall into three categories:

System APIs – these usually access the core systems of record and provide a means of insulating the user from the complexity or any changes to the underlying systems. Once built, many users, can access data without any need to learn the underlying systems and can reuse these APIs in multiple projects.

Process APIs – These APIs interact with and shape data within a single system or across systems (breaking down data silos) and are created here without a dependence on the source systems from which that data originates, as well as the target channels through which that data is delivered.

Experience APIs – Experience APIs are the means by which data can be reconfigured so that it is most easily consumed by its intended audience, all from a common data source, rather than setting up separate point-to-point integrations for each channel. An Experience API is usually created with API-first design principles where the API is designed for the specific user experience in mind.

Explanation

Correct Answer: IP whitelist

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>> OAuth 2.0 access token and Client ID enforcement policies are VERY common to apply on Experience APIs as API consumers need to register and access the APIs using one of these mechanisms

>> JSON threat protection is also VERY common policy to apply on Experience APIs to prevent bad or suspicious payloads hitting the API implementations.

>> IP whitelisting policy is usually very common in Process and System APIs to only whitelist the IP range inside the local VPC. But also applied occassionally on some experience APIs where the End User/ API Consumers are FIXED.

>> When we know the API consumers upfront who are going to access certain Experience APIs, then we can request for static IPs from such consumers and whitelist them to prevent anyone else hitting the API.

However, the experience API given in the question/ scenario is intended to work with a consumer mobile phone or tablet application. Which means, there is no way we can know all possible IPs that are to be whitelisted as mobile phones and tablets can so many in number and any device in the city/state/country/globe.

So, It is very LEAST LIKELY to apply IP Whitelisting on such Experience APIs whose consumers are typically Mobile Phones or Tablets.

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..

Anypoint Platform allows organizations to integrate one external identity provider (IdP) for identity and access management (IAM), supporting SSO and centralized user authentication.

Identity Provider Limit:

Anypoint Platform supports configuring a single IdP for the organization, which can be used to authenticate all users across business groups and environments within that Anypoint organization.

Explanation of Correct Answer (C):

Configuring one IdP ensures centralized and secure identity management, aligned with MuleSoft’s architecture.

Explanation of Incorrect Options:

Option A is incorrect because SAML 2.0 bearer tokens from external IdPs can indeed be used for invoking Anypoint Platform APIs.

Option B is incorrect as client providers can be assigned to specific business groups and environments.

Option D is incorrect since only identity management strictly requires an IdP; client management does not.

ReferencesFor further details on identity management options, consult MuleSoft documentation on Anypoint Platform’s IAM capabilities​.

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.

Explanation

Correct Answer: The test runs immediately after the Mule application has been compiled and packaged

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>> Integration tests are the last layer of tests we need to add to be fully covered.

>> These tests actually run against Mule running with your full configuration in place and are tested from external source as they work in PROD.

>> These tests exercise the application as a whole with actual transports enabled. So, external systems are affected when these tests run.

So, these tests do NOT run immediately after the Mule application has been compiled and packaged.

FYI... Unit Tests are the one that run immediately after the Mule application has been compiled and packaged.

Explanation

Correct Answer: Increase the size of the CloudHub worker(s)

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The key details that we can take out from the given scenario are:

>> API implementation uses a database bulk insert command to submit all the purchase data to a database

>> JDBC driver processes the data into a set of several temporary disk files on the CloudHub worker

>> Sometimes a request fails and the logs show a message indicating an out-of-file-space message

Based on above details:

>> Both auto-scaling options does NOT help because we cannot set auto-scaling rules based on error messages. Auto-scaling rules are kicked-off based on CPU/Memory usages and not due to some given error or disk space issues.

>> Increasing the number of CloudHub workers also does NOT help here because the reason for the failure is not due to performance aspects w.r.t CPU or Memory. It is due to disk-space.

>> Moreover, the API is doing bulk insert to submit the received batch data. Which means, all data is handled by ONE worker only at a time. So, the disk space issue should be tackled on "per worker" basis. Having multiple workers does not help as the batch may still fail on any worker when disk is out of space on that particular worker.

Therefore, the right way to deal this issue and resolve this is to increase the vCore size of the worker so that a new worker with more disk space will be provisioned.

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/

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.

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.

Explanation 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.

ReferencesFor more details, consult MuleSoft’s documentation on Client ID Enforcement policies and expected request configurations

Understanding Anypoint Monitoring and Its Capabilities:

Anypoint Monitoring provides comprehensive visibility into Mule applications, offering metrics and analytics such as request counts, response times, CPU utilization, memory usage, and other key performance indicators (KPIs). This tool is designed to help teams monitor API usage, troubleshoot issues, and optimize application performance.

Evaluating the Options:

Option A (Correct Answer): Anypoint Monitoring is the ideal tool for this requirement. It provides real-time insights into metrics such as the number of requests, response times, CPU utilization, and active API usage.

Option B (API Manager): API Manager focuses on API lifecycle management, including applying policies, managing contracts, and setting access controls. It does not provide performance monitoring or KPI tracking.

Option C (Runtime Alerts): Runtime Alerts can notify users of specific conditions, like high CPU usage, but they do not provide a full suite of metrics or insights over a given time period.

Option D (Functional Monitoring): Functional Monitoring focuses on functional testing of APIs rather than performance and usage metrics. It does not provide continuous KPI tracking.

Conclusion:

Option A is the correct answer. Anypoint Monitoring is the most suitable tool to track the specified metrics, providing detailed insights into API requests, response times, CPU usage, and active API counts.

For further details, refer to MuleSoft’s Anypoint Monitoring documentation on configuring dashboards and tracking performance metrics.

When a Basic Authentication policy is applied to an API on CloudHub but clients can still access the API without authentication, the likely cause is a missing Autodiscovery element. Here’s how this affects API security:

Autodiscovery in MuleSoft:

The Autodiscovery element is essential for linking an API implementation deployed in CloudHub with its API instance defined in API Manager. This connection allows the policies applied in API Manager, such as Basic Authentication, to be enforced on the deployed API.

Why Option B is Correct:

Without Autodiscovery, the deployed application does not "know" about the policies configured in API Manager, resulting in unrestricted access. Adding Autodiscovery enables the API to enforce the policies correctly.

Explanation of Incorrect Options:

Option A (incorrect Exchange version) would not cause bypassing of security policies.

Option C (missing client applications) does not impact authentication policy enforcement.

Option D (worker restart) is irrelevant to policy enforcement.

ReferencesRefer to MuleSoft documentation on Autodiscovery configuration and linking API Manager policies for additional information on setting up secure API policies​.

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​​.

Center for Enablement (C4E) KPIs:

A Center for Enablement (C4E) in MuleSoft focuses on enabling self-service and reuse by providing APIs that can be consumed across the organization. A key metric of success is how many consumers are utilizing the published APIs.

The number of consumers who have requested and received access to an API indicates the level of adoption and reuse, which aligns with the goals of a C4E.

Evaluating the Options:

Option A: This metric could indicate deployment automation, but it is not a direct measure of C4E’s success in enabling API reuse and consumption.

Option B: Bandwidth usage per API implementation provides insight into API traffic but does not measure C4E enablement or consumer engagement.

Option C: The number of developers downloading an API specification can be an indicator of interest but does not confirm actual usage or enablement.

Option D (Correct Answer): The number of consumers who have requested and received access to an API in production is a key metric indicating API adoption and reuse, which aligns with C4E’s goals.

Conclusion:

Option D is the correct answer as it provides a direct measure of consumer engagement and adoption, indicating the success of the C4E in promoting API usage across the organization.

Refer to MuleSoft’s documentation on C4E KPIs and API usage metrics for additional insights.

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: By refining the response definitions by adding the x-ratelimit-* response headers with description, type, and example

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References:

https://docs.mulesoft.com/api-manager/2.x/rate-limiting-and-throttling#response-headers

https://docs.mulesoft.com/api-manage r/2.x/rate-limiting-and-throttling-sla-based-policies#response-headers

Explanation

Correct Answer: By default, the Anypoint CLI and Mule Maven plugin are NOT included in the Mule runtime, so are NOT available to be used by deployed Mule applications

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>> We CANNOT apply API policies to the Anypoint Platform APIs like we can do on our custom written API instances. So, option suggesting this is FALSE.

>> Anypoint Platform APIs can be used for automating interactions with both CloudHub and customer-hosted Mule runtimes. Not JUST the CloudHub. So, option opposing this is FALSE.

>> Mule Maven plugin is NOT mandatory for deployment to customer-hosted Mule runtimes. It just helps your CI/CD to have smoother automation. But not a compulsory requirement to deploy. So, option opposing this is FALSE.

>> We DO NOT have any such special roles and permissions on the platform to separately control access for some users to have Anypoint CLI and others to have Anypoint Platform APIs. With proper general roles/permissions (API Owner, Cloudhub Admin etc..), one can use any of the options (Anypoint CLI or Platform APIs). So, option suggesting this is FALSE.

Only TRUE statement given in the choices is that - Anypoint CLI and Mule Maven plugin are NOT included in the Mule runtime, so are NOT available to be used by deployed Mule applications.

Maven is part of Studio or you can use other Maven installation for development.

CLI is convenience only. It is one of many ways how to install app to the runtime.

These are definitely NOT part of anything except your process of deployment or automation.

Understanding API-led Connectivity Layers:

In MuleSoft’s API-led connectivity approach, APIs are categorized into three layers:

Experience Layer: This layer is responsible for providing data to the end-user applications and is often customized to meet the needs of different user interfaces.

Process Layer: This layer is used to orchestrate and combine data from multiple System APIs. It acts as a mediator and business logic layer without directly interacting with the backend systems.

System Layer: This layer provides direct access to the backend systems (e.g., databases, ERPs) and is usually focused on exposing atomic data operations.

Evaluating the Architectural Constraints:

Option A: Always using a strict tiered approach by creating exactly one API per layer is not necessarily an architectural constraint of API-led connectivity. While a layered approach is recommended, it is common to have multiple APIs in each layer as needed for different functionalities.

Option B (Correct Answer): In API-led connectivity, Process APIs are generally responsible for orchestrating calls to System APIs and should not call other Process APIs. This maintains a clear separation of concerns, ensuring that Process APIs aggregate data from System APIs only and provide it to Experience APIs.

Option C: System APIs are generally designed to provide only the necessary data to meet current business requirements. Allowing them to return extra data that is not needed by Process or Experience APIs is not a best practice, as it can lead to inefficiencies.

Option D: Customizations specific to end-user applications are typically handled at the Experience Layer rather than the Process Layer, as the Experience Layer is intended to tailor the data to fit the needs of each specific client or front-end application.

Conclusion:

Option B is the correct answer as it aligns with the API-led connectivity principles. In this architectural style, Process APIs should orchestrate System APIs but should avoid interacting with other Process APIs to keep a clear separation of responsibilities across the layers.

For additional details, refer to MuleSoft documentation on API-led connectivity best practices, particularly around the roles of each layer in API orchestration and data handling.

Explanation

Correct Answer: 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

Explanation

Correct Answer: Build several Bounded Context Data Models that align with coherent parts of the business processes and the definitions of associated business entities.

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>> The options w.r.t building API data models using XML schema/ Agile API-centric practices are irrelevant to the scenario given in the question. So these two are INVALID.

>> Building EDM (Enterprise Data Model) is not feasible or right fit for this scenario as the teams and LOBs work in silo and they all have different initiatives, budget etc.. Building EDM needs intensive coordination among all the team which evidently seems not possible in this scenario.

So, the right fit for this scenario is to build several Bounded Context Data Models that align with coherent parts of the business processes and the definitions of associated business entities.

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.

Explanation

Correct Answer: Traffic between Mule applications deployed to an Anypoint VPC and on-premises systems can stay within a private network

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>> The private IP address range of an Anypoint VPC is NOT automatically chosen by CloudHub. It is chosen by us at the time of creating VPC using thr CIDR blocks.

CIDR Block: The size of the Anypoint VPC in Classless Inter-Domain Routing (CIDR) notation.

For example, if you set it to 10.111.0.0/24, the Anypoint VPC is granted 256 IP addresses from 10.111.0.0 to 10.111.0.255.

Ideally, the CIDR Blocks you choose for the Anypoint VPC come from a private IP space, and should not overlap with any other Anypoint VPC’s CIDR Blocks, or any CIDR Blocks in use in your corporate network.

that each CloudHub environment requires a separate Anypoint VPC. Once an Anypoint VPC is created, we can choose a same VPC by multiple environments. However, it is generally a best and recommended practice to always have seperate Anypoint VPCs for Non-Prod and Prod environments.

>> We use Anypoint VPN to link the underlying AWS VPC to an on-premises (non AWS) private network. NOT VPC Peering.

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.

Explanation 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.

ReferencesFor additional guidance, review MuleSoft's best practices on API-led connectivity and data modeling​.

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: API Consumer

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>> API clients are piece of code or programs that use the client credentials of API consumer but does not directly interact with Anypoint Exchange to get the access

>> API consumer is the one who should get registered and request access to API and then API client needs to use those client credentials to hit the APIs

So, API consumer is the one who needs to request access on the API from Anypoint Exchange

Explanation

Correct Answer: Search Anypoint exchange for a suitable existing fallback API, and then implement invocations to this fallback API in addition to the order API

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>> It is not ideal and good approach, until unless there is a pre-approved agreement with the API clients that they will receive a HTTP 3xx temporary redirect status code and they have to implement fallback logic their side to call another API.

>> Creating separate entry of same Order API in API manager would just create an another instance of it on top of same API implementation. So, it does NO GOOD by using clone od same API as a fallback API. Fallback API should be ideally a different API implementation that is not same as primary one.

>> There is NO option currently provided by Anypoint HTTP Connector that allows us to invoke a fallback API when we receive certain HTTP status codes in response.

The only statement TRUE in the given options is to Search Anypoint exchange for a suitable existing fallback API, and then implement invocations to this fallback API in addition to the order API.

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.

MUnit is MuleSoft’s testing framework for creating and running automated tests within Anypoint Studio. It is specifically designed for unit testing Mule applications and is best suited when testing doesn’t require understanding the inner workings or implementation details of the components being tested.

Ideal Use Cases for MUnit:

MUnit is optimal when testing individual flows, functions, or components in isolation. This type of testing focuses on verifying the behavior of each unit without needing to understand the complete system.

Since unit tests do not require external integrations or dependencies to be live, mocking is commonly used in MUnit to simulate the behavior of external services and APIs.

Why Option B is Correct:

Option B aligns with the concept of unit testing, where the emphasis is on testing functionality rather than system integration. Integration tests, on the other hand, would require implementation knowledge and live endpoints, making them unsuitable for MUnit’s scope.

Explanation of Incorrect Options:

Option A (read-only interactions) and Option C (no mocking) do not suit MUnit’s typical testing environment as MUnit is designed with mocking capabilities to simulate dependencies.

Option D (SoapUI-based tests) suggests an external testing tool, while MUnit is specific to MuleSoft.

ReferencesFor more on MUnit best practices, refer to MuleSoft’s MUnit documentation​.

Role of Process API in API-led Connectivity:

In MuleSoft’s API-led connectivity approach, a Process API is used to coordinate, aggregate, and orchestrate data from various System APIs. It is primarily responsible for implementing business logic that spans multiple backend systems or entities, transforming and combining data as needed to support business processes.

Process APIs are not directly exposed to end-user clients; rather, they work between System APIs and Experience APIs, providing business logic and orchestration capabilities.

Evaluating the Options:

Option A: Process APIs are not typically responsible for large data dumps or data synchronization. That function would be handled by a System API or a specialized batch process.

Option B: Managing direct, secure communication between back-end systems and end-user clients is typically the role of Experience APIs rather than Process APIs.

Option C (Correct Answer): Process APIs are designed to coordinate and orchestrate calls to multiple other APIs in the network, which supports the automation of business processes.

Option D: Securing and managing communication with end-user clients is typically the role of Experience APIs, not Process APIs.

Conclusion:

Option C is the correct answer, as the main role of a Process API is to coordinate and orchestrate interactions between other APIs, enabling business processes to function seamlessly across multiple systems.

Refer to MuleSoft's API-led connectivity documentation for further explanation of the roles and responsibilities of Process APIs in an application network.

Explanation

Correct Answer: Data on past API invocations to help identify anomalies and usage patterns across various APIs

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API Invocation metrics provided by Anypoint Platform:

>> Does NOT provide any Return Of Investment (ROI) related information. So the option suggesting it is OUT.

>> Does NOT provide any information w.r.t how APIs are reused, whether there is effective usage of APIs or not etc...

>> Does NOT prodive any prediction information as such to help us proactively identify any future policy violations.

So, the kind of data/information we can get from such metrics is on past API invocations to help identify anomalies and usage patterns across various APIs.

In MuleSoft’s ecosystem, when a Process API needs to consume a System API (published to Exchange and protected by OAuth 2.0), the recommended approach is to utilize the REST Connect Connector. Here’s how it aligns with best practices:

Automated Connector Generation:

When a RAML or OAS specification is published in Exchange, MuleSoft automatically generates a REST Connect Connector for that API. This connector simplifies integration as it abstracts the complexity of making HTTP requests and handling OAuth authentication.

Streamlined Integration:

The Process API can import this generated connector from Exchange and configure OAuth credentials, streamlining secure access to the System API without manual HTTP setup.

Why Option C is Correct:

Using the REST Connect Connector directly leverages MuleSoft’s automated tooling, minimizing manual configurations and ensuring a more maintainable integration.

Explanation of Incorrect Options:

Option A (importing an OAuth module) is unnecessary; OAuth is handled within the connector’s configuration.

Option B (property YAML files with HTTP requests) involves manual setup, which is more error-prone and not recommended.

Option D (manually updating POM file) does not directly aid in invoking an API through Exchange.

ReferencesFor more information on using REST Connect Connectors and OAuth integration in MuleSoft, refer to the MuleSoft documentation on API Management and Connectors​​.

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: The FQDNs are determined by the application name chosen, IRRESPECTIVE of the region

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>> When deploying applications to Shared Worker Cloud, the FQDN are always determined by application name chosen.

>> It does NOT matter what region the app is being deployed to.

>> Although it is fact and true that the generated FQDN will have the region included in it (Ex: exp-salesorder-api.au-s1.cloudhub.io), it does NOT mean that the same name can be used when deploying to another CloudHub region.

>> Application name should be universally unique irrespective of Region and Organization and solely determines the FQDN for Shared Load Balancers.

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.

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: They are typically written using MUnit to run in an embedded Mule runtime that does not require external connectivity.

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Below TWO are characteristics of Integration Tests but NOT unit tests:

>> They test the validity of input and output of source and target systems.

>> They must be triggered by an external client tool or event source.

It is NOT TRUE that Unit Tests must be run in a unit testing environment with dedicated Mule runtimes for the environment.

MuleSoft offers MUnit for writing Unit Tests and they run in an embedded Mule Runtime without needing any separate/ dedicated Runtimes to execute them. They also do NOT need any external connectivity as MUnit supports mocking via stubs.

https://dzone.com/articles/munit-framework

Explanation

Correct Answer: Approve the client application request for the chosen SLA tier

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>> Only approving the client application request for the chosen SLA tier can be automated

>> Rest of the provided options are not valid

Explanation

Correct Answer: At the API proxy

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>> API Policies can be enforced at two places in Mule platform.

>> One - As an Embedded Policy enforcement in the same Mule Runtime where API implementation is running.

>> Two - On an API Proxy sitting in front of the Mule Runtime where API implementation is running.

>> As the deployment scenario in the question has API Proxy involved, the policies will be enforced at the API Proxy.

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: 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.

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.

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