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Platform Integration Architect

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0/170Learned

Platform Integration Architect

Study Checklist

  • Platform Administrator
  • Platform App Builder
  • Platform Foundations
  • Platform Developer
  • Platform Administrator II
  • Agentforce Sales Consultant
  • Agentforce Service Consultant
  • Platform Data Architect
  • Platform Development Lifecycle and Deployment Architect
  • Platform Identity and Access Management Architect
  • Platform Integration Architect
  • Platform Sharing and Visibility Architect
  • Heroku Architect
  • B2C Solution Architect
  • Experience Cloud Consultant
  • Agentforce Field Service and Operations Consultant
  • Agentforce Nonprofit Consultant
  • Data 360 Consultant
  • Omnistudio Consultant
  • CRM Analytics and Einstein Discovery Consultant
  • Platform User Experience Designer
  • Platform Strategy Designer
  • B2C Commerce Developer
  • JavaScript Developer
  • Omnistudio Developer
  • Platform Developer II
  • Marketing Cloud Engagement Administrator
  • Marketing Cloud Engagement Specialist
  • Marketing Cloud Engagement Consultant
  • Agentforce Sales Foundations
  • Business Analyst
  • Marketing Cloud Engagement Developer
  • Marketing Cloud Engagement Foundations
  • Agentforce Specialist
  • Agentforce Life Sciences Consultant
  • B2B Commerce Administrator AP
  • B2B Commerce Developer AP
  • Agentforce Consumer Goods AP
  • Agentforce Financial Services AP
  • Agentforce Health AP
  • Agentforce Manufacturing AP
  • MuleSoft Integration Foundations
  • MuleSoft Developer
  • MuleSoft Developer II
  • MuleSoft Platform Integration Architect
  • MuleSoft Platform Architect
  • Tableau Desktop Foundations
  • Tableau Data Analyst
  • Tableau Consultant
  • Tableau Server Administrator
  • Tableau Architect

Checklist progress

0/170Learned

  • Identifying integration standards (REST vs SOAP vs event-driven) and protocol constraints when analyzing an existing multi-system landscape
  • Recognizing legacy system protocols (SOAP/XML, EDI, flat file) and determining interoperability requirements when modernizing integrations
  • Mapping an existing system landscape to identify the canonical data model used across systems and where data format mismatches require transformation
  • Differentiating between synchronous and asynchronous integration boundaries and when each is mandated by existing system constraints
  • Identifying firewall, network boundary, and DMZ constraints that affect inbound vs outbound integration choices
  • Determining which Salesforce platform limits (API call limits, governor limits, concurrent request limits) constrain an integration design given a system landscape
  • Identifying Salesforce org topology constraints (multi-org, sandbox, production, Government Cloud) that affect integration architecture decisions
  • Analyzing tight coupling pain points in point-to-point integrations and recommending architectural improvements using middleware or an ESB
  • Identifying data duplication and synchronization conflicts as integration pain points when multiple systems hold the same data (system of record ambiguity)
  • Identifying data latency and throughput bottlenecks in an existing integration landscape that prevent real-time synchronization requirements from being met
  • Recognizing Salesforce API limit consumption as a constraint when multiple existing integrations share the same org's daily API allocation
  • Assessing on-premise vs cloud system hosting as a landscape constraint that determines whether a Salesforce-hosted integration (callouts) or hybrid middleware (MuleSoft on-prem, Mule Gateway) is required
  • Identifying the absence of event-driven capability in existing point-to-point integrations as a scalability pain point and assessing the migration path to pub/sub architecture
  • Choosing between user-level OAuth (user context) and system-level OAuth (service account / client credentials) based on integration data access requirements
  • Selecting OAuth 2.0 Web Server flow when a user-context integration requires browser redirect for third-party app access
  • Selecting OAuth 2.0 JWT Bearer flow for server-to-server integrations where no user interaction is possible
  • Selecting OAuth 2.0 Client Credentials flow for system-level integrations that use a service account
  • Determining OAuth scopes for a Connected App based on integration data access needs and principle of least privilege
  • Determining OAuth policies (IP relaxation, admin-approved users, refresh token) for Connected App security in inbound integrations
  • Evaluating when to use Named Credentials vs hardcoded credentials vs externally managed secrets for outbound callout authentication
  • Configuring IP restrictions for inbound integration access to enforce network-level security requirements
  • Evaluating SAML-based SSO vs OAuth for authentication when integrating Salesforce with an enterprise identity provider (IdP)
  • Determining when certificate-based authentication (mTLS) is required by existing system security standards and how Salesforce stores and uses X.509 certificates for outbound connections
  • Distinguishing functional integration requirements (data transformation rules, business process triggers) from non-functional requirements (latency SLAs, availability, scalability)
  • Translating business SLA requirements (e.g., data must sync within 15 minutes) into technical integration pattern selection criteria
  • Recognizing idempotency as a non-functional requirement and its implications for integration design (duplicate message handling)
  • Identifying availability and disaster recovery requirements that dictate integration architecture choices (retry logic, failover, redundancy)
  • Identifying observability requirements (logging, tracing, alerting) as non-functional requirements that must be designed into the integration architecture from the start
  • Determining the maintainability and operational requirements (on-call runbooks, integration ownership, support SLAs) that influence build vs buy decisions for integration middleware
  • Classifying Salesforce data (PII, financial records, public marketing data) and applying appropriate integration security controls based on data classification
  • Determining transport security requirements (TLS version, mutual TLS, encryption at rest) based on data classification in an integration context
  • Identifying which data classification requires field-level masking, tokenization, or encryption before transmission to external systems
  • Determining data residency requirements based on data classification and how they constrain the geographic deployment of integration middleware
  • Identifying the need for a 360-degree customer view as a CRM success requirement and determining which external systems must be integrated to achieve it
  • Defining the system of record for each data domain as a foundational CRM integration requirement to prevent data conflicts
  • Identifying data quality requirements (completeness, accuracy, deduplication) as integration requirements critical to CRM adoption and success
  • Determining user experience requirements that mandate real-time vs near-real-time integration, particularly for sales reps accessing live data in Salesforce
  • Assessing data volume growth and new geographies as factors requiring scalable, multi-region integration solutions
  • Evaluating data residency constraints from GDPR, CCPA, and HIPAA in integration design
  • Evaluating data retention and cross-border transfer restrictions from regulations in integration design
  • Determining how industry-specific compliance mandates (financial services, healthcare) influence the choice between cloud-based and on-premise integration middleware
  • Assessing M&A requirements where newly acquired systems must be rapidly integrated with Salesforce
  • Mapping data flows between systems in a landscape to identify which integration patterns are in use and where gaps or risks exist
  • Identifying hub-and-spoke vs point-to-point integration topology from a landscape diagram and understanding the operational trade-offs of each
  • Identifying request-reply and fire-and-forget integration patterns from a system landscape diagram
  • Identifying batch and publish-subscribe integration patterns from a system landscape diagram
  • Identifying event-driven patterns (Platform Events, Change Data Capture, Streaming API) in a landscape diagram
  • Distinguishing event-driven integration patterns from polling-based integration patterns in a landscape diagram
  • Identifying process choreography integration patterns from a system landscape diagram
  • Classifying Salesforce-to-Salesforce (SF2SF) integration patterns in a multi-org landscape and identifying when the native SF2SF feature or external middleware is more appropriate
  • Evaluating Salesforce governor limits (concurrent API requests, callout timeout limits, heap size) as architectural constraints that force asynchronous integration patterns
  • Evaluating transaction boundary constraints in Salesforce (trigger execution context, two-phase commit limitations) that prevent synchronous cross-system transactions
  • Evaluating Salesforce callout restrictions from synchronous Apex contexts (no callouts after DML in same transaction) as a constraint requiring an asynchronous workaround pattern
  • Assessing the 100-callout-per-transaction limit and other per-execution governor limits as constraints that shape the design of high-frequency integration triggers
  • Assessing external system rate limits and throttling policies as constraints that require queuing, backoff, or bulk integration approaches
  • Identifying process constraints such as approval workflows, business hours, and maintenance windows that affect integration scheduling and error handling design
  • Determining the correct integration user setup (dedicated integration user, profile, permission set) to enforce least-privilege access for automated integrations
  • Specifying OAuth 2.0 scopes required for a Connected App based on the integration's data access needs and principle of least privilege
  • Determining IP allowlisting requirements for external systems calling Salesforce APIs to prevent unauthorized inbound access
  • Translating data sensitivity requirements into Salesforce field-level security and sharing model constraints that integration user profiles must respect
  • Identifying mutual TLS (mTLS) requirements for high-security integrations and how Salesforce supports or requires certificate-based authentication
  • Identifying when SAML assertions must be passed through middleware to Salesforce for identity federation, and how the Salesforce identity provider and service provider roles are configured
  • Recommending Bulk API vs REST API vs Composite API based on data volume, response time requirements, and Salesforce API limits
  • Understanding the Salesforce Bulk API 2.0 job lifecycle and when it is the appropriate choice over Bulk API 1.0 or REST Collections
  • Choosing between synchronous callouts and asynchronous queueable/batch jobs for high-volume integration scenarios to avoid Salesforce callout limits
  • Determining when Platform Events or Change Data Capture (CDC) meets near-real-time latency requirements better than polling-based REST API calls
  • Evaluating data virtualization (Salesforce Connect / External Objects) as an alternative to full data replication when low latency and read-only access to external data is required
  • Evaluating Heroku Connect as a data replication solution and when its eventual consistency model meets or conflicts with business latency requirements

Given a use case, identify the integration pattern that meets business requirements.

0/11

  • Selecting the request-reply integration pattern for synchronous real-time data retrieval scenarios and understanding its latency and coupling trade-offs
  • Applying the Remote Process Invocation (Request-Reply) pattern when a remote process result is required before proceeding
  • Distinguishing fire-and-forget from request-reply and selecting fire-and-forget for long-running external processes where Salesforce does not need an immediate response
  • Contrasting Remote Process Invocation (Request-Reply) with fire-and-forget when no callback is needed from the remote process
  • Applying the batch data synchronization pattern when near-real-time is not required and operational impact must be minimized during off-hours
  • Choosing the publish-subscribe (pub/sub) pattern using Platform Events when decoupling producers and consumers is required for scalability
  • Choosing between Change Data Capture (CDC) and outbound messaging/workflow rules for event-driven outbound integration from Salesforce
  • Selecting the data virtualization (External Objects) pattern to surface external data inside Salesforce without replication and understanding when it is preferred over batch synchronization
  • Applying the process choreography pattern when a business process spans multiple systems and no central orchestrator is appropriate
  • Selecting the orchestration pattern using MuleSoft or a middleware layer when a single integration must coordinate multiple backend system calls
  • Identifying the UI Update or Remote Call-In pattern as appropriate when an external system needs to push updates into Salesforce without waiting for a Salesforce polling cycle

Given a use case, define the components which create a solution that meets business requirements.

0/7

  • Specifying the components of a Salesforce outbound integration using Apex callouts (HttpRequest, named credentials, remote site settings, async vs sync context)
  • Selecting appropriate Salesforce integration components (External Services, Flow, Apex) for a low-code vs pro-code integration solution based on team skills and requirements
  • Identifying the components of an event-driven Salesforce integration using Platform Events (event producer, Platform Event definition, CometD subscriber, replay mechanism)
  • Defining the components of a MuleSoft-based integration (Anypoint Platform, API Gateway, Mule flows, connectors) and their roles in a Salesforce integration architecture
  • Defining the components of a Salesforce Connect (OData) integration: External Data Source, External Object, OData adapter, and how they surface external records in Salesforce
  • Defining the components of a Salesforce-to-Salesforce (SF2SF) integration: Connection objects, Subscription objects, published records, and the limitations of the native SF2SF feature
  • Defining the components required for a Heroku Connect integration solution (Heroku app, Postgres database, Heroku Connect add-on, mappings) and their responsibilities

Given a use case, identify the trade-offs, limitations, and constraints that meet the proposed solution.

0/7

  • Evaluating the trade-off between synchronous REST callouts (simplicity, immediate response) and asynchronous Platform Events (decoupling, scalability) for a given scenario
  • Identifying the Salesforce callout timeout limit (120 seconds) and its impact on synchronous integration patterns involving long-running external processes
  • Understanding the 50,000 Platform Events per hour org limit and how it constrains high-volume event-driven integration designs
  • Assessing the trade-offs between using Salesforce Connect (OData) for external data virtualization vs full data replication into Salesforce
  • Analyzing Heroku Connect replication lag and eventual consistency as trade-offs when near-real-time accuracy is required
  • Evaluating MuleSoft iPaaS cost and operational complexity trade-offs versus native Salesforce integration capabilities for simpler use cases
  • Evaluating the trade-offs of a native Salesforce-to-Salesforce (SF2SF) connection versus a middleware-brokered multi-org integration (data fidelity, error handling, SOQL relationship support)

Given a use case that includes technical requirements, constraints, or drivers, specify the appropriate Salesforce application programming interface(s) (API) for the proposed solution.

0/8

Choosing the REST API vs SOAP API based on data format requirements, client capability, and integration complexity

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Selecting the Bulk API 2.0 for high-volume data loads (greater than 2,000 records) and understanding its job-based asynchronous processing model

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Applying the Composite API (composite resources, SObject Collections, composite graph) to reduce API call consumption for multi-record operations

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Selecting Platform Events when custom event payloads and app-defined events are required for streaming

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Selecting Change Data Capture when external systems must subscribe to Salesforce record changes via the Streaming API

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Selecting PushTopic or Generic Streaming for SOQL-based subscription to record changes when custom event definitions are not needed

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Choosing the Metadata API vs Tooling API for deployment and configuration automation use cases in CI/CD pipelines

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Applying the GraphQL API (Salesforce) for flexible data querying to reduce over-fetching in mobile or external portal integrations

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Given a use case that includes technical requirements, constraints, or drivers, determine the standards, components, techniques, and security mechanism that should be used.

0/8

  • Applying API-led connectivity (system layer, process layer, experience layer) using MuleSoft to design a reusable integration architecture
  • Applying the OpenAPI Specification (OAS/Swagger) or RAML to define and document integration APIs, and how API-first design improves integration reliability and reuse
  • Applying data transformation standards (XSLT, DataWeave, JSON-to-JSON mapping) in integration middleware to translate between Salesforce's data model and external system schemas
  • Applying OAuth 2.0 JWT Bearer flow for server-to-server integrations where no user interaction is possible and a service account is required
  • Selecting an API gateway (MuleSoft API Manager, AWS API Gateway) as the security enforcement point for inbound Salesforce integration requests and understanding its rate limiting and policy application role
  • Applying HMAC or digital signature verification for validating the authenticity and integrity of inbound webhook payloads to Salesforce
  • Selecting message queuing (e.g., MuleSoft VM queues, external MQ) as a buffering technique to handle traffic spikes between systems with different processing rates
  • Designing a Salesforce-connected MuleSoft Anypoint Platform deployment model (CloudHub, on-premise, hybrid) and selecting the appropriate runtime based on data residency and network requirements
  • Designing Salesforce as an API provider: versioning strategy, resource URL design, authentication requirements, and rate limit exposure to consumers
  • Designing a custom REST API in Apex (@RestResource) as a Salesforce API provider and understanding its security, versioning, and governor limit considerations
  • Designing a Salesforce SOAP (WSDL-based) API provider and when to use the enterprise vs partner WSDL for external system consumption
  • Implementing Salesforce as an API consumer using Apex HttpCallout: handling callout limits, timeout configuration, and callout-from-trigger restrictions
  • Understanding the restriction that Apex callouts cannot be made after DML in the same transaction and the design patterns to work around it (Queueable, Platform Events)
  • Configuring External Services in Salesforce to register an external REST API and invoke it declaratively via Flow or Apex
  • Selecting Named Credentials for secure credential management in outbound callouts and understanding per-user vs org-wide named credential configurations
  • Choosing between synchronous Apex callouts, Queueable Apex with callout, and Platform Events for outbound integration based on transaction context and volume
  • Evaluating Salesforce outbound messaging (workflow-based) vs Apex callouts vs Platform Events for pushing Salesforce data changes to external systems
  • Implementing Salesforce Flow (Screen Flow, Autolaunched Flow, Scheduled Flow) as a declarative outbound integration mechanism via External Services or Apex actions
  • Understanding how Change Data Capture (CDC) enables external systems to subscribe to Salesforce record changes without polling, and when to prefer CDC over outbound messaging
  • Configuring Change Data Capture: selecting entities to track, understanding enriched payloads (changedFields, header metadata), and implementing header-based change filtering in subscribers
  • Applying bulk-safe Apex trigger patterns to prevent governor limit violations when integration volumes increase unexpectedly
  • Designing an integration solution to handle Salesforce API daily limit growth by distributing API usage across connected apps or using Bulk API for high-volume operations
  • Partitioning high-volume batch integration jobs using Batchable Apex with appropriate scope sizes and chained Queueable patterns to stay within execution limits at scale
  • Evaluating Platform Event delivery guarantees (at-least-once delivery) and designing idempotent consumers to handle duplicate events at scale
  • Using the ReplayId mechanism in Platform Events / Streaming API to recover from subscriber failures and replay missed events
  • Applying horizontal scaling principles in MuleSoft (multiple workers, clustering) to meet throughput requirements for high-volume Salesforce integrations
  • Designing Salesforce Connect (External Objects) custom adapters using Apex for scalable access to external systems not covered by standard OData adapters
  • Designing Heroku Connect mappings with appropriate polling intervals and field selection to balance synchronization frequency with Salesforce API consumption
  • Distinguishing system errors (network failure, timeout) from application errors (validation failure, business rule violation) in integration error handling
  • Applying appropriate error handling strategies for system errors (retry, timeout handling) versus application errors (validation feedback, business rule messaging) in integrations
  • Implementing retry logic with exponential backoff for transient failures in Apex callouts and external API integrations
  • Implementing idempotent upsert operations using Salesforce external IDs to safely reprocess failed records without creating duplicates
  • Designing Salesforce outbound messaging acknowledgment handling and what happens when the external endpoint does not acknowledge receipt
  • Handling partial success in Salesforce Bulk API jobs (allOrNone vs partial success) and determining appropriate error record handling strategies
  • Designing error notification and alerting in MuleSoft flows using error handlers, on-error-continue vs on-error-propagate, and routing errors to a dedicated monitoring channel
  • Designing dead-letter queue patterns for failed Platform Event processing or MuleSoft flows to prevent data loss without blocking the integration pipeline
  • Implementing compensating transactions or saga patterns in distributed integration scenarios where two-phase commit is not possible
  • Implementing OAuth 2.0 Web Server flow for inbound integrations where a third-party application accesses Salesforce data on behalf of a user
  • Applying Named Credentials with per-user authentication for outbound integrations that must maintain data access consistent with the running user's Salesforce permissions
  • Configuring Connected App admin-approved users only policy to restrict integration access to pre-authorized users
  • Configuring Connected App OAuth policies: IP relaxation when network constraints require it for integration access
  • Configuring Connected App refresh token policy (expiration, revocation) for integration token management
  • Configuring Salesforce as a SAML identity provider (IdP) for delegated authentication to external systems and understanding when Salesforce acts as the IdP vs service provider (SP)
  • Implementing certificate-based authentication (mutual TLS) for high-security outbound callouts and configuring the Salesforce certificate keystore
  • Configuring Salesforce Shield Platform Encryption for integration scenarios: impact on SOQL, Bulk API, and outbound data visibility when fields are encrypted
  • Implementing the OAuth 2.0 Device Flow for IoT or headless integrations where a browser redirect is not possible
  • Applying API versioning strategies in Salesforce integrations to prevent breaking changes when Salesforce deprecates older API versions
  • Using Custom Metadata Types or Custom Settings to externalize integration endpoint configuration, enabling environment-specific values to be deployed independently of Apex code
  • Designing a sandbox-to-production integration promotion strategy: managing Named Credentials, Connected App configurations, and endpoint URLs across environments
  • Designing integrations resilient to Salesforce seasonal releases by avoiding hard dependencies on API version-specific behaviors and using stable API endpoints
  • Designing for Salesforce maintenance windows: understanding sandbox refresh impacts, scheduled maintenance, and how to build integrations that gracefully handle temporary unavailability
  • Building integration test harnesses using Apex mock callouts (StaticResourceCalloutMock, HttpCalloutMock) to enable CI/CD pipeline testing without live external endpoints
  • Designing integration tests and monitoring that detect integration failures caused by Salesforce metadata changes (field removal, picklist changes, validation rules)
  • Implementing circuit breaker patterns in integration middleware to prevent cascading failures when a downstream system (Salesforce or external) is unavailable
  • Defining KPIs and thresholds for integration monitoring (throughput, error rate, latency, queue depth) and mapping them to Salesforce API usage dashboards
  • Monitoring Salesforce API limit consumption using the /limits endpoint and setting up alerts before daily limits are exhausted
  • Using Salesforce Event Monitoring (EventLogFile) to audit API usage, identify integration performance bottlenecks, and detect unauthorized access patterns
  • Defining MuleSoft Anypoint Monitoring dashboards and alerts to track flow execution times, error rates, and message volumes for Salesforce integrations
  • Implementing Platform Event subscriber health checks (delivery status, replay position) to detect and recover from stalled streaming consumers
  • Using Salesforce transaction security policies and real-time event monitoring to detect anomalous integration access patterns (unusual API volumes, off-hours access)
  • Defining escalation paths for integration failures: when to auto-retry, when to alert operations, and when to trigger manual intervention based on error classification
  • Designing outbound message retry behavior and understanding Salesforce's built-in retry for unacknowledged outbound messages (retries up to 24 hours)
  • Implementing Platform Event replay (using stored ReplayId or -2 for earliest) for recovery when a subscriber was offline and missed events
  • Designing recovery procedures for failed Bulk API jobs: partial success handling, error record reprocessing, and idempotent upsert strategies
  • Identifying Salesforce custom objects or platform tools (e.g., EventLogFile, custom logs) to capture integration execution history for reporting and troubleshooting
  • Using Salesforce Reports and Dashboards on custom integration log objects to give business stakeholders self-service visibility into integration health without requiring direct system access
  • Designing integration audit trail reports that capture message volumes, success/failure rates, and SLA compliance over time for stakeholder reporting
  • Defining reporting requirements that distinguish transient failures (auto-recovered) from persistent failures (requiring business escalation) in integration monitoring dashboards
  • Establishing integration governance reporting: tracking API consumer usage by connected app, enforcing rate limits per consumer, and providing capacity planning data to integration architects

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Selecting the Bulk API 2.0 for high-volume data loads (greater than 2,000 records) and understanding its job-based asynchronous processing model

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Next conceptApplying the Composite API (composite resources, SObject Collections, composite graph) to reduce API call consumption for multi-record operations

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Explainer

Bulk API 2.0 is designed for asynchronous processing of large data sets, handling thousands to millions of records more efficiently than standard SOAP APIs. It utilizes a job-based model where data is streamed, batched, and processed in the background with automatic retry capabilities.

Core information
  • Bulk API is optimized for data loads ranging from a few thousand to millions of records, whereas SOAP API is intended for real-time updates of smaller record sets.
More details and nuances
  • Bulk API 2.0 query jobs are specifically designed for queries returning 2,000 records or more.