Integration-Architect Exam Dumps - Salesforce Integration Architecture Designer Questions and Answers

The foundational step in a CRM transformation project is to understand the business context and data landscape before selecting technical tools or middleware.

Before proposing a technical solution, an architect must evaluate current and future data and system usage to identify specific integration requirements. This includes documenting business processes, mapping data flows between the Marketing system, Outlook, and the ERP, and identifying which system will serve as the "System of Record" for each data entity. For example, the architect needs to determine if lead data from the Marketing system should flow unidirectionally to Salesforce or if activities from Outlook need bidirectional synchronization to maintain a "360-degree view".

While out-of-the-box connectors (Option B) and middleware (Option C) are valuable, they are implementation tactics that follow the discovery phase. Jumping directly to connectors may overlook unique business processes that require custom integration logic, while proposing middleware prematurely can lead to unnecessary costs if native tools or point-to-point connections are sufficient. A thorough evaluation ensures that the integration architecture is scalable, avoids data corruption, and aligns with UC's strategic goal of breaking down information silos to provide sales and support staff with meaningful, unified customer insights.

Comprehensive and Detailed 250 to 350 words of Explanation From Salesforce Pl14atform Integration Architect documents: In the rol15e of a Salesforce Platform Integration Architect, evaluating a legacy landscape requires a clinical mapping of current system functions against Salesforce’s native capabilities, while strictly adhering to the "Constraints and Goals" provided by the business. The objective here is to maximize the ROI of the Salesforce implementation by consolidating redundant systems into the core platform.

According to Goal 2, the business intends to utilize Salesforce for Marketing, Sales, and Service processes. Salesforce is architected to handle these three domains through its core clouds: Marketing Cloud (replacing the legacy Email Marketing System), Sales Cloud (replacing the Sales Activity System), and Service Cloud (replacing the Case Management System). By consolidating these three specific functions into Salesforce, the organization achieves Goal 1, which is the creation of a 360-degree view of the customer. When these activities occur on a single platform, the data is unified, eliminating the silos that existed in the previous landscape.

However, the architect must also respect the technical constraints defined in Goal 3, which explicitly states the need to reuse enterprise capabilities built for quoting and order management. In architectural design, this designates the "Quoting System" and the "Order Management System" as systems of record that must remain in the future-state landscape. These systems likely contain complex, proprietary logic or are tightly coupled with back-end ERP systems like SAP Business Suite, making them "non-negotiable" for retirement at this stage.

Therefore, because Email Marketing, Sales Activity, and Case Management map directly to Salesforce’s primary strengths and are not excluded by the "reuse" requirement, they are the three systems that should be retired. This strategic retirement simplifies the integration architecture, allowing the architect to focus on building robust integration patterns (such as Request-Reply or Fire-and-Forget) between Salesforce and the remaining Quoting and Order Management systems.

With a volume of 2 million records per day, this integration exceeds the practical limits of standard near-real-time patterns like Outbound Messaging or synchronous Apex Callouts. Sending 2 million individual REST requests would likely exhaust the daily API limit and could cause significant performance degradation in Salesforce due to transaction overhead.

An Integration Architect must recommend an Asynchronous Batch Data Synchronization pattern, typically facilitated by a third-party ETL/Middleware tool (e.g., MuleSoft, Informatica, or Boomi). Staging the records off-platform is essential for several reasons:

Throttling: The homegrown system behind a firewall may not be able to handle a massive, sudden burst of 2 million records. A middleware tool can ingest the data from Salesforce and "drip-feed" it into the target system at an acceptable rate.

Error Handling and Retries: Middleware provides sophisticated persistence and "Dead Letter Queues" to ensure that if the homegrown system goes offline, no data is lost.

API Efficiency: The middleware can use the Salesforce Bulk API 2.0 to extract the data in large chunks, which is significantly more efficient than individual REST calls and consumes far fewer API limits.

Option A is a valid concern but is a symptom of the wrong choice of tool (REST). Option B describes an inbound integration to Salesforce, whereas the requirement is outbound. By utilizing a third-party tool to stage and manage the 2 million record flow, the architect ensures that the integration is scalable, respects the corporate firewall constraints (via a secure agent or VPN), and maintains the performance of the Salesforce production environment.

When designing integrations that specifically involve Chatter, social collaboration, or Experience Cloud sites, the Connect REST API (formerly known as the Chatter REST API) is the architecturally recommended choice. While the standard REST API is optimized for CRUD operations on data records, the Connect REST API is specifically designed to handle the complex, nested structures of social feeds, including posts, comments, hashtags, and mentions.

The Connect REST API provides a specialized resource for feed elements that simplifies the process of identifying specific social markers like hashtags. In this use case, the mobile app or a middleware service can subscribe to or query the feed. The Connect REST API returns structured data where hashtags are identified as distinct message segments, making it trivial for an application logic to detect the #thanksNTO tag and trigger a subsequent call to the Twitter API.

From an architectural standpoint, using the Connect REST API offers several advantages over the standard REST API or Streaming API for this requirement:

Efficiency: Connect REST API responses are structured specifically for presentation in mobile applications, providing only the relevant social metadata and localized content needed for the feed.

Feature Richness: It provides native support for social actions such as liking, sharing, and following, which are often required alongside feed monitoring.

Scalability: It is designed to handle the high-volume, real-time nature of social interactions within Experience Cloud and mobile ecosystems.

While the Streaming API (Option B) can notify an application of record changes, it does not provide the rich, formatted social context that the Connect REST API delivers. The standard REST API (Option A) could technically access FeedItem records, but it would require significant custom parsing logic to identify hashtags within the raw body text, whereas the Connect REST API handles this segmenting natively. Therefore, for building custom mobile experiences that interact with Chatter data, the Connect REST API is the superior solution.

When dealing with high-volume data (1 million transactions per day) that does not need to be stored natively in Salesforce, the architect should recommend Data Virtualization via Salesforce Connect.

Salesforce Connect allows the company to display external data as External Objects. This approach provides several architectural advantages for a banking community:

No Data Storage: Transactions remain in the core banking system, avoiding the massive storage costs and complex synchronization logic required to house millions of records natively in Salesforce.

Real-Time Visibility: Because External Objects are queried on-demand via the OData protocol or a custom Apex adapter, customers see the most up-to-date transaction history every time they refresh the page.

While an Iframe (Option B) is technically possible, it is often discouraged due to security concerns (such as clickjacking) and a poor user experience, as the Iframe does not natively integrate with Salesforce UI components or reporting. Salesforce Connect provides a "seamless" look and feel, allowing External Objects to be used in related lists and Lightning components just like standard Salesforce records, while keeping the heavy data burden on the performant core banking system.

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.

Platform Eve9nt triggers run asynchronously unde10r the "Automated Process" system user, which makes troubleshooting their failures in production difficult through standard user-level monitoring. When a trigger fails consistently after rollout, the integration architect must be able to inspect the specific execution errors and governor limit consumption.

The recommended approach is to set up debug logs for Platform Event triggers. By configuring a debug log for the Automated Process user, the architect can capture the full execution trace of the trigger. This allows the team to identify the root cause of the failure, such as:

Uncaught Exceptions: Null pointer exceptions or DML failures that occur during record updates.

Governor Limit Breaches: Exceeding CPU time or SOQL query limits within the trigger's execution context.

Logic Errors: Issues in the custom code responsible for processing the prediction score.

Option A focuses on high-level consumption limits; while important, it would not explain why a trigger is failing if the events are being successfully published. Option C is a design-time validation step and would not provide insight into a runtime failure. Proactive monitoring through debug logs provides the granular technical visibility required to maintain a reliable, AI-driven integration in a production environment.

To satisfy a requirement for a synchronous callout triggered by a user action, the architect should use a UI-driven approach, such as a Lightning component and a button.

In Salesforce, triggers (Option B) are primarily used for asynchronous logic in integration contexts. Because a trigger executes as part of the database save operation, making a synchronous callout directly from a trigger is prohibited as it would block the database transaction until the external system responds, leading to performance degradation and "uncommitted work pending" errors. If a trigger must initiate an integration, it must do so asynchronously (using @future or Queueable Apex), which violates the requirement for a synchronous call.

By using a Lightning component, the architect can initiate a synchronous Request-and-Reply pattern. When the sales rep clicks the "7Submit to OMS" button, the componen8t invokes an Apex method that makes the REST callout to the OMS in real-time. The user remains on the page while the system waits for the OMS to respond, allowing for immediate feedback—such as an order confirmation number or an error message—to be displayed in the UI. A Batch Apex job (Option C) is inherently asynchronous and delayed, making it unsuitable for a synchronous, real-time fulfillment requirement.

Implementing Shield Platform Encryption is a significant architectural change that requires careful planning before activation. The architect's first priority must be to Review Shield Platform Encryption configurations and understand the platform's functional limitations.

Encryption at rest affects how data interacts with other platform features. For example, encrypting a field can impact the ability to use that field in SOQL WHERE clauses, report filters, list views, or as a unique/external ID. Before encrypting Social Security Numbers, the architect must audit all existing integrations, Apex code, and reports that reference that field to ensure they will still function correctly.

Option A is incorrect because unnecessarily encrypting all data can negatively impact system performance and break standard functionality. Encryption should be applied selectively to sensitive fields based on a clear data classification policy. Option B is factually wrong; Shield is a data protection tool, not an authentication or authorization mechanism like OAuth or SSO. By reviewing the configurations first, the architect can identify potential "blockers"—such as a field being used in a formula or a criteria-based sharing rule—and address them before the encryption keys are generated and applied.

In a synchronous Request-Reply scenario where a bank agent is waiting "on the spot" for a credit check, the error-handling strategy must balance immediate feedback with system resilience.

Option A is the recommended architectural approach for enterprise resiliency. By placing a Middleware layer (like MuleSoft) between Salesforce and the credit agencies, the architect can implement sophisticated error-handling patterns that are invisible to the user but critical for success. If a credit agency's API is momentarily unreachable, the middleware can perform automated retries (e.g., three attempts with 500ms intervals). If the retries still fail, the middleware sends a clean, structured error response back to Salesforce.

Option B (Fire and Forget) is fundamentally unsuitable for this use case because the agent needs the result immediately to open the account; they cannot wait for a callback that might arrive hours later. Option C (Mock service) is only a testing tool and provides no value in a production environment where real financial data is required. By delegating the retry logic to the middleware, the architect protects Salesforce's concurrent request limits (since the agent only occupies a thread for the duration of the final response) and ensures that transient network issues do not result in a "failed" bank account application for the customer.