Free Salesforce Integration-Architect Practice Exam with Questions & Answers

For developer-centric tools that need to access fine-grained technical data like code coverage and test results, the Tooling API is the correct architectural choice.

While the Metadata API (Option B) is used to deploy or retrieve code, it does not provide real-time query access to the underlying metrics of a test run. The Tooling API, however, exposes specialized objects such as ApexCodeCoverage, ApexCodeCoverageAggregate, and ApexTestResult. These objects allow the Java application to query exactly which lines of code were executed during a test and the overall percentage of coverage for the organization.

The Analytics REST API (Option A) is designed for querying and interacting with Einstein Analytics (CRM Analytics) datasets and dashboards, which is irrelevant to software development lifecycle (SDLC) metrics. By using the Tooling API, the Java application can perform RESTful queries to gather comprehensive data on test successes, failures, and coverage gaps. This allows NTO to integrate Salesforce into its existing enterprise-wide quality dashboard, ensuring a unified view of code health across all platforms.

When optimizing REST API consumption, an Integration Architect must select the specific composite resource that maximizes record throughput per request. While the Salesforce REST API offers several "Composite" resources to group multiple operations, they have vastly different payload limits.

The sObject Tree resource (`/services/data/vXX.X/composite/tree/`) is uniquely designed to handle up to 200 records in a single post request. Although its primary architectural purpose is to create complex nested hierarchies (parent-to-child relationships) in one transaction, it can also be used to send a flat list of up to 200 records of the same type. This directly satisfies the requirement to increase bulk capability within the REST framework.

In contrast, the Batch and Composite resources have much more restrictive limits. The Composite resource allows you to execute a series of up to 25 subrequests in a single call. While these subrequests can be linked, the total number of records processed is significantly lower than the sObject Tree's 200-record threshold. Similarly, the Batch resource also limits the caller to 25 subrequests, where each subrequest is an independent REST API call executed in a single batch.

By implementing the sObject Tree resource, the architect achieves a 200:1 reduction in API call consumption for these updates. This approach respects the customer’s constraint to avoid major "architecture changes"—such as switching to the Bulk API 2.0 or introducing middleware—while effectively resolving the issue of exceeding daily governor limits. It is a highly efficient way to handle medium-volume data synchronization within the standard RESTful interface.

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For an outbound, declarative, Fire-and-Forget integration to a legacy SOAP-based system, Salesforce Outbound Messaging is the native tool of choice. Outbound Messaging sends an XML message to a designated endpoint when specific criteria are met. It is highly reliable as Salesforce will automatically retry the delivery for up to 24 hours if the target system is unavailable.

For the communication back from the legacy system to Salesforce, a strongly-typed SOAP API approach is required. The Enterprise WSDL is the correct recommendation here because it is a strongly-typed WSDL that is specific to the organization's unique data model (including custom objects and fields). Using the Enterprise WSDL allows the legacy system to communicate with Salesforce using specific data types, providing compile-time safety and reducing errors during the mapping process.

Option A is less efficient because Platform Events would likely require middleware to translate the event into the legacy system's SOAP format. Option B suggests the Partner WSDL, which is loosely-typed and designed for developers building tools that must work across many different Salesforce orgs. Since this is an internal integration for a specific company, the Enterprise WSDL provides a much more streamlined development experience with better data integrity. By combining Outbound Messaging (for fire-and-forget delivery) and the Enterprise WSDL (for the strongly-typed callback), the architect fulfills the technical requirements while minimizing custom code.

In Salesforce, you cannot perform real HTTP callouts during unit tests. To test integration logic, developers must use the HttpCalloutMock interface to simulate the API's response. To ensure that the Apex code adheres to the RAML contract, the architect should require that the test mock implementation strictly follows the RAML specifications.

By requiring the Apex REST API Clients to implement the HttpCalloutMock (or more specifically, creating a mock class that implements it), the developer creates a controlled testing environment. The mock class should be coded to return a payload that matches the RAML-defined structure (fields, data types, and status codes). When the test runs, the Apex client receives this "contract-compliant" response. The unit test then uses assertions to verify that the Apex code correctly parses and handles this specific data structure.

Option B is technically imprecise; you don't "call" the mock from the client, you provide the mock to the test runtime using Test.setMock(). Option C describes the general process of testing but does not address the "design specification" needed to ensure contract adherence. By mandating a mock implementation that mirrors the RAML contract, the architect ensures that if the API contract changes in the RAML file, the unit tests will fail if the Apex code is not updated to match, thereby maintaining high integration quality and preventing runtime errors.

The primary architectural flaw in UC's current landscape is the reliance on a monolithic P2P integration layer. In such designs, any failure in one integration thread or a surge in volume for one system can monopolize resources (CPU, memory, threads), causing the entire service—and thus all other integrations—to fail. This lack of isolation leads to the "tight interdependencies" described.

To effectively decouple these systems, the architect should recommend a Microservices Architecture. By breaking the monolithic service into smaller, independent, and modular components, each integration (Billing, ERP, Data Lake) becomes its own isolated service. This approach provides several key architectural benefits:

Isolation of Failure: If the connection to the legacy billing application fails or times out, it no longer impacts the ERP or Data Lake integrations.

Independent Scalability: If the Data Lake integration requires high throughput, that specific microservice can be scaled horizontally without wasting resources on the others.

Technology Agility: Each microservice can be updated or patched independently, allowing for faster maintenance cycles.

Furthermore, moving a "monolithic" service to the cloud (Option B) is simply a "lift and shift" that preserves the underlying fragility. While the Bulk API (Option A) is excellent for high-volume data loading, it does not solve the fundamental problem of system interdependency and orchestration failure. Transitioning to a modular, service-oriented design allows UC to implement modern integration patterns, such as asynchronous queuing between the microservices, which significantly improves the overall resilience and performance of the Salesforce-to-back-office landscape.

For organizations using both Salesforce Service Cloud and B2C Commerce (formerly Demandware), Salesforce provides a specialized, pre-built integrat25ion known as the Salesforce B2C Commerce to Service Cloud Connector. This connector is part of the Salesforce B2C Solution Architecture and is the recommended choice because it offers "out-of-the-box" cross-cloud functionality.

The connector enables several critical business processes for service agents:

Unified Customer Profile: Synchronizes customer data between the two platforms, ensuring agents have the most current contact information.

Order History Visibility: Allows agents to view real-time order data and status from the Commerce system directly within the Service Console.

Service Actions: Enables agents to perform commerce-related tasks, such as "Order on Behalf Of," without leaving the Salesforce interface.

While an architect could build a custom integration using the Commerce REST API (Option A) or MuleSoft (Option C), these approaches require significant development, testing, and long-term maintenance effort. The B2C Connector reduces time-to-market and leverages Salesforce's own engineering to handle complex synchronization logic and API versioning. Recommending the standard connector aligns with the architectural principle of "clicks before code" and ensures that the integration remains supported by Salesforce as both platforms evolve.

When an enterprise architect identifies infrastructure-level requirements such as caching, queuing, and complex event handling, it signals a need for a dedicated integration layer. Salesforce is an engagement platform, not a dedicated message broker or Enterprise Service Bus (ESB). For complex event handling in a publish/subscribe scenario, the architect should recommend leveraging middleware to act as the central nervous system of the integration landscape.

The middleware provides a robust environment to manage the lifecycle of a message. It can receive a single event from a publisher (like Salesforce via Platform Events) and then route that message to multiple active subscribers. This decoupling ensures that Salesforce doesn't need to manage the connection state or retry logic for every downstream system. Middleware tools are specifically designed to handle "Quality of Service" (QoS) requirements like guaranteed delivery, message sequencing, and dead-letter queuing, which are difficult to manage natively at scale within Salesforce limits.

Option A is incorrect because shifting from Fire-and-Forget to Request-Reply generally decreases performance and scalability due to the synchronous nature of the wait time. Option C is also incorrect; protocol translation (e.g., SOAP to REST) and heavy message transformation are exactly what middleware tools are built for. By performing these tasks in middleware, you conserve Salesforce’s Apex CPU limits and maintain a cleaner, more maintainable CRM environment. Therefore, using middleware for routing and event handling is the standard architectural recommendation for a mature enterprise program.