Named Credentials simplify and secure external endpoint management and auth. Custom Objects and Page Layouts are unrelated. Permission Set Groups manage access, not integrations.

Daily API usage trends show how close the org is to its API limits and help prevent disruptions. Login IPs, role hierarchies, and dashboards do not impact API thresholds.

Platform Events can be configured to include a retry flag and reprocess logic on failure, which is effective for async retries. Recursion causes logic issues. Emails don’t retry. Screen Flows are not meant for backend retries.

Named Credentials centralize endpoint and authentication details securely, reducing performance issues from external lookups and improving maintainability. Option 1 exposes keys. Option 2 is insecure. Option 4 is not their core purpose.

Increased Apex CPU time suggests inefficient logic or overload, which often causes slow batch execution. Reduced test coverage doesn’t impact runtime. Smaller batch sizes help. Login rate is unrelated.

Scheduling jobs during off-hours distributes load and prevents hitting API limits. Named Credentials don’t throttle. Hard-coded retries can cause bursts. Custom Settings help configure, but don’t throttle directly.

Event Monitoring provides dashboards and logs that detail historical latency and throughput for integrations. Data Export is static. Debug Logs are for code, not performance trends. External Debugger is real-time.

API requests per second is a strong indicator of load on the system’s integration endpoints. Concurrent users can be idle. Comments and dashboards are unrelated.

Idempotent integrations can be retried safely and help ensure consistency across retries or downtime recovery. Tests don’t affect run-time reliability. VPNs aren’t standard Salesforce components. Emails don’t impact process stability.

Remote Process Invocation – Request and Reply is best when immediate, synchronous confirmation is needed from the legacy system. Option 1 (Batch Data Sync) introduces delay. Option 2 (Remote Call-In) refers to external systems initiating the call, not Salesforce. Option 3 (Fire and Forget) lacks synchronous response.

Batch Data Synchronization is ideal for large-volume, scheduled nightly data loads. UI Update Based is unrelated. Fire and Forget is real-time, not batch. Remote Call-In is for Salesforce being called, not calling out.

Request and Reply fits well for real-time access, allowing the app to get immediate data. Fire and Forget doesn’t return a result. Data Virtualization isn’t a pattern, and batch sync is too delayed.

Fire and Forget is ideal for non-blocking operations where no response is needed. Remote Call-In and Request/Reply require interactions. Batch is overkill for single callouts.

Request and Reply suits on-demand queries where immediate feedback is expected, like user-initiated checks. Batch sync isn‘t real-time. Fire and Forget lacks responses. Remote Call-In is the reverse direction.

Middleware Transformation Logic handles data mapping and transformation between systems, ensuring data structure compatibility. Lightning and Site.com are UI-focused. External ID helps with upserts but not solution logic.

Event Monitoring latency logs provide direct insight into delays, allowing admins to isolate where bottlenecks occur. Profile logs and permission audits relate to access. Field-level security is about visibility, not performance.

Apex Callout enables complex logic and external interaction. Custom Settings store config data. Flow Builder is limited for logic. Approval Processes are unrelated.

Flow Orchestration is purpose-built for step-by-step automation, including integration steps. Workflow and Process Builder are deprecated/limited. External Objects expose data but don’t manage processes.

Validation Rules and External IDs ensure clean data and correct matching during sync. Sandboxes and Layouts aren’t used for integration. External Services define APIs but not data integrity logic.

Remote Process Invocation – Request and Reply is ideal for real-time data synchronization as it enables Salesforce to call out to an external system and receive an immediate response. Batch Data Synchronization is not suitable for real-time requirements due to its scheduled nature. Event-Driven Messaging is useful for loosely coupled systems where latency is acceptable, but not for strict real-time needs. Remote Call-In is used when an external system needs to initiate a process in Salesforce, which is not the case here.

Remote Process Invocation – Fire and Forget is best when Salesforce needs to push data to another system without waiting for a response, which fits near real-time order updates. UI Update Based on Data Changes is used for rendering UI changes, not data transfer. Remote Call-In would allow the ERP to call Salesforce, which is the opposite of the requirement. Batch Data Synchronization doesn‘t provide the immediacy needed in this case.

Remote Call-In is the correct pattern as it allows external systems to access Salesforce services and data by invoking APIs. Request and Reply is used when Salesforce initiates the call, which is the opposite of this use case. Data Virtualization is a concept, not a defined Salesforce pattern. Event-Driven Messaging doesn‘t support on-demand, synchronous access.

Event-Driven Messaging is the correct pattern for asynchronous, high-volume communication, especially when the systems involved are loosely coupled. Remote Call-In supports synchronous integrations initiated externally. Batch Synchronization is not event-based and runs on a schedule. Request and Reply is synchronous and tightly coupled.

Middleware is essential in multi-cloud integration because it provides routing, transformation, and orchestration capabilities that increase resilience and adaptability across platforms. Load Balancers distribute traffic but don’t provide logic or error handling. An Event Bus enables communication but doesn’t manage complex workflows or system-specific logic. Retry Queues help with temporary failures but are only a part of the overall solution.

Event Bus promotes loose coupling by allowing systems to publish and subscribe to events without knowing about each other’s existence or implementation. Direct API Integration creates tight coupling. Middleware can help with decoupling but often still involves specific endpoint knowledge. Custom Apex Code tends to be tightly integrated and harder to maintain across multiple systems.

Message Broker is designed for reliability and sequencing by managing queues and delivering messages even if systems are temporarily unavailable. API Gateway manages APIs but not message persistence. Platform Events support event-driven architectures but are not as robust as a broker for sequencing. Apex Triggers are used for logic execution and don‘t handle messaging concerns.

Using a dedicated middleware layer separates concerns and provides flexibility for transformation, orchestration, and translation tasks across systems. Embedding logic in flows or Apex limits scalability and maintainability. Custom REST endpoints in Salesforce are powerful but not suited to handle external orchestration and transformation comprehensively.

Remote Process Invocation + Middleware offers real-time synchronization while handling routing, transformation, and retries. Batch jobs and data exports are not real-time. Platform Events work well in pub-sub models but may not fully support legacy systems. Remote Call-In alone is insufficient without orchestration.

Logging Framework and Middleware allow centralized logging, error capture, and orchestration across systems, which supports auditability and structured error handling. Platform Events and Triggers handle events but not long-term logging. Email Alerts notify users but don’t provide systemic audit trails. Remote Process Invocation and Apex focus more on execution than visibility.

Option 3 is correct because real-time integration often introduces operational complexity such as concurrency handling, system availability concerns, and monitoring overhead. Option 1 is incorrect because real-time systems are typically more expensive to develop and maintain. Option 2 is incorrect because traceability can be implemented in either method but is not inherently better in batch. Option 4 is incorrect because real-time provides immediate response while batch introduces latency.

Option 3 is correct because SLAs set measurable standards and allow for proactive issue handling. Option 1 and 2 are negative consequences. Option 4 is unrelated.

Option 2 is correct because checkpointing saves progress and allows partial restarts without duplicating effort. Option 1 wastes resources. Option 3 leads to data loss. Option 4 provides visibility but lacks resilience.

Option 4 is correct because logging errors helps developers, while user-friendly messages help users understand impact without technical detail. Option 1 prevents user awareness. Option 2 limits team visibility. Option 3 doesn’t help with issue context.

Option 3 is correct because alert rules in monitoring platforms detect failure patterns and escalate automatically. Option 1 is manual and reactive. Option 2 is not scalable. Option 4 collects data but does not act on it.

Option 3 is correct because 4xx vs 5xx codes indicate different issues, and responses should be tailored accordingly. Option 1 causes more harm for client errors. Option 2 reduces flexibility. Option 4 overlooks actionable issues.

Option 1 is correct because circuit breakers prevent cascading failures in synchronous calls under repeated errors. Option 2 doesn’t benefit from live failure handling. Option 3 and 4 are not failure-prone.

Option 3 is correct because rate limiting respects platform limits and avoids throttling. Option 1 doesn’t address limits. Option 2 may take time. Option 4 disrupts the process unnecessarily.

Option 3 is correct because a dead-letter queue isolates unrecoverable messages for manual or automated inspection. Option 1 refers to logging. Option 2 is unrelated. Option 4 relates to timing but not failure recovery.

Option 4 is correct because idempotency keys track if a request has already been processed, preventing duplication. Option 1 is partially true but incomplete. Option 2 is irrelevant. Option 3 conflicts with safe retry design.

Option 2 is correct because average response time reflects system performance and delays in integration. Option 1 affects payload but not speed. Option 3 is more relevant for compatibility. Option 4 is not performance related.

Option 3 is correct because real-time alerts enable immediate awareness and faster resolution. Option 1 is not scalable. Option 2 is reactive. Option 4 delays error visibility.

Option 2 is correct because correlation IDs link logs from multiple systems, helping trace transactions. Option 1 is a drawback. Option 3 limits visibility. Option 4 isn’t related to traceability.

Option 2 is correct because queue processing time reveals how long messages wait before execution. Option 1 affects speed but not delays. Option 3 is too generic. Option 4 reflects availability, not performance.

Option 3 is correct because response logging records external errors for diagnosis. Option 1 lacks response visibility. Option 2 hides root causes. Option 4 eliminates traceability.

Option 3 is correct because trend reports show patterns over time, helping identify chronic issues. Option 1 is not dynamic. Option 2 is reactive. Option 4 is limited in scope.

Option 2 is correct because exponential backoff spaces retries, avoiding overloading the receiving system. Option 1 is incorrect; usage decreases with backoff. Option 3 doesn‘t always improve throughput. Option 4 relates more to idempotency logic than retry strategy.

Option 2 is correct because latency affects how quickly data is transferred in real-time scenarios. Option 1 is batch-related. Option 3 and 4 are irrelevant to delivery speed.

Option 3 is correct because throughput analysis confirms if the system handles projected volume. Option 1 is theoretical. Option 2 is limited in scope. Option 4 neglects potential problems.

Option 3 is correct because external Application Performance Monitoring (APM) tools offer advanced insights into metrics and alerting. Option 1 is limited in scope. Option 2 is for developers. Option 4 lacks automation.

Platform Event Logs are purpose-built to capture and track integration-related events, especially success and failure messages that are useful for audit reporting. Option 1 (Debug Logs) are developer-oriented and temporary. Option 2 (Event Monitoring) tracks user activity but not system integration results. Option 3 (Scheduled Reports) are good for visualization but not suited for capturing backend integration logs.

Event Monitoring Analytics App provides visual insights into API usage, making it ideal for tracking trends across business units. Flow Orchestration Logs are limited in scope and not tailored for analytics. External Debugger is a dev tool and not reporting-focused. Custom Report Types are useful but lack built-in visual API tracking features.

API call success/failure rate is a direct indicator of integration health and should be included in any monitoring dashboard. User profiles are irrelevant to integration monitoring. Flow run durations are useful but do not provide success/failure clarity. Queueable job counts can help but are indirect indicators.

Custom Logging Object is the right choice as it provides persistent storage of error messages and metadata, supporting long-term reporting. External ID fields are for record matching. Email alerts are transient and not stored for historical reporting. Flow Labels are metadata and not useful for logs.

Using dynamic dashboards ensures stakeholders only see data relevant to their roles, improving usability and compliance. Field-level filters should be used, not avoided. Quarterly delivery is too infrequent. Excluding failed transactions defeats the purpose of monitoring.

Platform Events can be published by flows or Apex to capture third-party API responses, including errors, and are easily reportable. Permission Sets control access, not data. Custom Metadata is for configurations. Named Credentials are for auth, not logging.

A Custom Object with structured fields allows storing, querying, and reporting on historical integration errors. System.debug statements are not stored in a durable or reportable way. Site Pages expose data publicly and are insecure. Activity Timeline is for user-level interactions.

Flow Orchestration provides timeline views of multi-step integrations, showing what succeeded or failed in a visual format. Process Builder is outdated and limited. Platform Cache is for data storage, not visualization. External Services map APIs but do not provide execution visuals.

Success/failure rate reflects integration reliability and helps identify declining trends. API Version may cause issues but doesn‘t reflect performance. Login times and page load are unrelated to integration.

Platform Events with Flow-based alerts enable real-time alerting based on integration errors, providing immediate visibility. Slack Connect is a channel, not a mechanism. Email-to-Case is for support, not integration monitoring. Sharing Rules manage access, not alerts.

Event Monitoring, when combined with custom dashboards, allows detailed tracking of latency and API performance trends. Debug Mode is for developers and not persistent. Session timeouts are not specific to API latency. Validation rules relate to data, not integrations.