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MuleSoft Platform Integration Architect

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MuleSoft Platform Integration Architect Study Checklist

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  • Platform Data Architect
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  • Marketing Cloud Engagement Administrator
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  • MuleSoft Developer
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  • MuleSoft Platform Integration Architect
  • MuleSoft Platform Architect
  • Tableau Desktop Foundations
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Differentiate between functional and non-functional requirements for integration solutions

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Classifying integration requirements as functional or non-functional

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Non-functional requirements as drivers of architecture decisions

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Select features of Anypoint Platform for designing and managing web and event-driven application programming interfaces

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  • Anypoint Platform components across the API lifecycle
  • Design-first API specification workflow
  • Reusable API fragments and shared data types across specifications
  • Anypoint Exchange, API portals and cataloging for discovery and reuse
  • Managing API access and traffic with API Manager policies
  • API Autodiscovery versus API proxy for managing an API
  • Mule Gateway versus Omni Gateway for API management
  • API analytics and functional monitoring for managed APIs
  • API versioning across Exchange assets and API Manager instances
  • API Governance rulesets and conformance checks
  • Platform features for designing and managing event-driven APIs

Select deployment options of the Anypoint Platform control plane and runtime plane

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  • Control plane versus runtime plane responsibilities
  • Choosing a control plane hosting option for data residency and compliance
  • MuleSoft Government Cloud deployment models
  • Anypoint Platform Private Cloud Edition for a fully customer-hosted platform
  • Choosing a runtime plane option by management model and built-in capabilities
  • CloudHub versus CloudHub 2.0 as the MuleSoft-managed runtime
  • Runtime Fabric versus Hybrid Standalone on customer infrastructure
  • Runtime Fabric shared responsibility model
  • Hybrid Standalone versus unregistered on-premises Mule runtimes
  • Hosting combinations of control plane and runtime plane
  • Control plane and runtime plane compatibility
  • Runtime region versus control plane data residency
  • Runtime behaviour when the control plane is unreachable
  • Distributed shared memory grid in a Mule runtime cluster
  • Active-active node processing, failover and load balancing in customer-hosted clusters
  • Mule runtime cluster prerequisites and node operations
  • Inbound source coordination in clustered versus unclustered scaled instances
  • Idempotency and redelivery state in clustered versus unclustered instances
  • Clusters versus server groups as Runtime Manager deployment targets
  • Performance and scalability trade-offs of clustering Mule runtimes
  • High availability and clustering across runtime plane deployment options
  • CloudHub 2.0 runtime cluster mode versus replica scale-out without clustering
  • Shared and durable state on CloudHub without a runtime cluster
  • Mule runtime cluster versus JMS queue, load balancer and shared-database HA designs
  • CloudHub deployment region, application domain and regional load balancer
  • Shared load balancer versus dedicated load balancer on CloudHub
  • Edge TLS, client certificates and allowlisting at the CloudHub ingress
  • Static IP addresses for allowlisting CloudHub applications
  • CloudHub 2.0 shared space versus private space networking
  • Anypoint VPC and private space firewall rules
  • Choosing VPN, VPC peering, Direct Connect or Transit Gateway
  • Keeping internal APIs off the public internet on CloudHub
  • Mule domain shared resources, sharing limits and benefits
  • Sharing one HTTP listener port across co-located Mule applications
  • When a Mule domain is justified versus per-application configuration
  • Domain class loading, inherited modules and resource export
  • Purpose and benefits of Mule 4 class-loading isolation
  • Making application dependencies visible to connectors through shared libraries
  • Shared libraries versus additional plugin dependencies
  • Exporting packages and resources from Mule artifacts
  • Reactive nonblocking execution and its effect on thread use
  • Event processor processing types and thread scheduling
  • UBER shared thread pool and runtime self-tuning
  • Thread pool tuning options across deployment targets
  • Mule event immutability in concurrent processing
  • Transactions and thread switching in the reactive engine
  • API-led connectivity versus point-to-point integration
  • System, Process and Experience API layer responsibilities
  • Isolating backend system changes behind System APIs
  • Experience APIs for a new consumer channel versus reusing existing APIs
  • When to simplify or collapse API-led layers
  • Choosing a web API style: REST, SOAP, GraphQL or gRPC
  • HTTP method semantics, safety and idempotency
  • Authentication, authorization and HTTPS for web APIs
  • Caching HTTP API responses at the client, gateway or Mule layer
  • Fault-tolerant invocation of unreliable downstream APIs
  • Event-driven architecture components and producer-consumer decoupling
  • When event-driven architecture fits versus synchronous request-reply
  • Point-to-point queues versus publish-subscribe fan-out
  • Broker-based buffering between producers and slow or unavailable consumers
  • Choosing VM queues, Anypoint MQ, a JMS broker or Kafka for messaging
  • Orchestration versus event-driven choreography for coordinating APIs
  • AsyncAPI contracts and APIkit for AsyncAPI implementations
  • Salesforce Platform Events and Change Data Capture in Mule event-driven designs
  • Enterprise Integration Patterns and their Mule 4 counterparts
  • Matching data and process integration patterns to requirements
  • Splitter and aggregator patterns in Mule 4 flows
  • Request-reply messaging over queues with publish-consume
  • Guaranteed delivery and consumer acknowledgment modes
  • Dead letter queues for undeliverable and poison messages
  • Message ordering with FIFO queues and concurrent consumers
  • JMS topic subscriptions in clustered and scaled Mule deployments
  • Configuration properties files in a Mule 4 application
  • System properties and environment variables as runtime property sources
  • Environment-specific property file selection with a placeholder
  • Secure Configuration Properties module for encrypted values
  • Mule property resolution hierarchy across property sources
  • Runtime Manager application properties versus bundled property files
  • CloudHub reserved properties and HTTP listener port placeholders
  • Mule event structure and variable propagation
  • DataWeave expressions and Mule runtime functions in flows
  • Flows versus subflows and Flow Reference
  • Modularizing Mule configuration files
  • Target variables for content enrichment
  • For Each versus Parallel For Each scopes
  • When to use the Async scope
  • Mule 4 error types and operation error mappings
  • On Error Propagate versus On Error Continue
  • Try scope for localized error handling
  • Scheduler source behaviour across deployment targets
  • Choice router for content-based routing
  • Scatter-Gather fan-out and aggregated result handling
  • Round Robin router for distributing events across routes
  • First Successful router versus Until Successful scope for failover
  • Mapping enterprise integration routing patterns to Mule 4 components
  • Salesforce API coverage of the Salesforce Connector
  • Salesforce Connector authentication and connection types
  • Bulk API versus record operations for Salesforce data writes
  • Invoking custom Apex logic through Apex REST and Apex SOAP
  • Salesforce Connector streaming event sources
  • Replaying missed Salesforce events after subscriber downtime
  • Salesforce integration patterns with the Salesforce Connector
  • Connectors, modules and core components as versioned dependencies
  • Global connector configurations and connection providers
  • Connection pooling and connectivity testing in connector configurations
  • Connector sources versus connector operations in flow design
  • Polling connector sources with watermarking and scheduling strategies
  • File, FTP and SFTP new-file pickup and post-read handling
  • HTTP Request timeouts and response status validation
  • Database connector bulk operations and parameterized queries
  • JMS connector acknowledgment modes and topic subscriptions
  • Metadata sources available to the Transform Message component
  • Defining metadata for components that do not expose it
  • Reusable custom metadata types in a Mule project
  • Reader and writer configuration for Transform Message formats
  • Metadata needs of the graphical mapper versus script-only DataWeave
  • Canonical data model versus point-to-point data translation
  • Canonical data models across Experience, Process and System API layers
  • Publishing canonical data types as reusable Exchange assets
  • Mapping between system-specific and canonical formats with DataWeave
  • Enterprise-wide canonical model versus bounded-context data models
  • Validation Module validators and their failure behaviour
  • Validation Module All scope versus individual validators
  • JSON and XML schema validation of message payloads
  • Request validation against an API specification with APIkit
  • Filtering invalid messages: validators with error handling versus Choice routing
  • MUnit suite structure and Behavior, Execution and Validation scopes
  • Running, debugging and reading MUnit tests in Anypoint Studio
  • Isolating a flow under test with Mock When
  • Spy, Verify Call and assertion processors for validating behaviour
  • Before and After Suite versus Before and After Test scopes
  • Enabling flow sources for listener- and scheduler-triggered flows
  • Testing error-handling paths in MUnit
  • Parameterized MUnit test suites
  • MUnit database and FTP server utilities for portable tests
  • Controlling MUnit test runs with tags, ignore and timeouts
  • MUnit coverage in Studio versus Maven coverage enforcement
  • Test Recorder versus APIkit test-suite scaffolding
  • Designing Mule applications for testability
  • Unit versus integration versus functional tests for Mule applications
  • Integration test environments, data state and endpoint configuration
  • Choosing load, stress, spike, soak or scalability testing
  • Performance test prerequisites and success criteria for Mule applications
  • Performance test environment design on CloudHub versus customer-hosted Mule
  • Component isolation and representative workloads in performance tests
  • VM Connector operations and message sources
  • Transient versus persistent VM queue trade-offs
  • Serialization requirements for persistent VM queue payloads
  • Persistent VM queue storage on a single runtime versus a Mule runtime cluster
  • VM queues for work distribution across Mule runtime cluster nodes
  • Inter-application messaging with VM queues and Mule domains
  • CloudHub persistent queues for durability across workers and restarts
  • Delivery semantics and duplicate messages with CloudHub persistent queues
  • Durable queueing options on CloudHub 2.0 and Runtime Fabric
  • Transactions with VM Connector listeners and operations
  • Reliability pattern with persistent VM queues
  • Choosing VM queues versus flow references, Anypoint MQ, or JMS
  • Default, global and private object store scoping
  • Persistent versus transient object stores
  • Object Store Connector operations and key-handling options
  • Entry TTL, max entries and expiration interval settings
  • Object store options for standalone on-premises and hybrid runtimes
  • Object stores in an on-premises Mule runtime cluster
  • Key-level locking and concurrent object store access across nodes
  • Object Store v2 versus local-disk object stores on CloudHub
  • Enabling and configuring Object Store v2 for CloudHub deployments
  • Object Store v2 TTL modes, partitions and data retention
  • Persistent object stores on Runtime Fabric with Persistence Gateway
  • Cross-application data sharing with Object Store v2 versus Anypoint MQ
  • Object stores versus databases for transactional or high-contention state
  • Mule components that keep state in an object store
  • Idempotent Message Validator object store design for duplicate rejection
  • Cache scope versus cache-aside caching with object stores
  • Redelivery policy counter storage in object stores
  • Watermark state persistence for incremental synchronization
  • OAuth token persistence in object stores for connectors
  • Aggregators module for stateful message aggregation
  • Stateful component state in clusters and multi-worker deployments
  • When to choose an alternative to local or XA transactions
  • At-least-once delivery with acknowledgements and idempotent receivers
  • Reliability pattern for non-transactional event sources
  • Reliable acquisition for socket-based versus resource-based sources
  • Choosing the reliability-pattern queue by deployment target
  • Until Successful scope retry behavior and exhaustion handling
  • Until Successful variable and payload state across retry attempts
  • Reconnection strategy options and deployment-time connection testing
  • Scope of reconnection strategies across sources, operations and error types
  • Redelivery policy for poison messages on message sources
  • Until Successful versus reconnection strategy versus redelivery policy
  • High availability versus disaster recovery
  • RTO and RPO in disaster recovery planning
  • Cold, warm, hot standby and active-active topologies
  • Sizing active-active, fault-tolerant and zero-downtime models to an SLA
  • MuleSoft versus customer HA and DR responsibilities by deployment model
  • Stateless design and externalized state for HA and DR
  • HA through multiple workers or replicas on CloudHub and CloudHub 2.0
  • Cross-region DR for CloudHub applications with load balancing
  • CloudHub 2.0 multiregion DR configurations by application impact and state
  • Region outage impact on control plane access and persisted state
  • Runtime Fabric HA replicas versus cluster backup and restore for DR
  • Mule runtime clustering for HA on Hybrid Standalone and on-premises runtimes
  • Single Resource versus XA transaction type selection
  • Transactional connector support and XA connection configuration
  • Starting a transaction at the event source versus a Try scope
  • Transactional actions for listeners, operations and Try scopes
  • Error handler outcomes for commit versus rollback in transactions
  • Scope and router behavior inside a transaction
  • Transactional publish and consume semantics on messaging connectors
  • Nested XA transactions with Try scopes
  • Bitronix transaction manager for XA recovery
  • Mule 4 self-tuning and tuning scope by deployment target
  • Backend response time as a throughput and scalability bottleneck
  • Flow and transformation design practices that reduce event overhead
  • Recursive flow references and iteration antipatterns
  • HTTP, HTTPS and APIkit configuration choices for performance
  • Connector pooling profiles for concurrent requests
  • Cache scope versus HTTP Caching policy for repeated lookups
  • Scatter-Gather and Async scope for reducing response latency
  • Back-pressure and maxConcurrency for protecting downstream systems
  • Spike Control versus Rate Limiting policies for capacity protection
  • Horizontal versus vertical scaling across deployment targets
  • Performance test criteria and capacity sizing
  • Performance test environment and execution practices
  • Repeatable versus non-repeatable streaming for flow throughput
  • File-stored versus in-memory repeatable stream strategies and buffer sizing
  • Object streaming, auto-paging and pagination for large result sets
  • Enabling DataWeave streaming with reader and deferred writer properties
  • Choosing For Each, Parallel For Each or Batch Job for large data sets
  • Batch Aggregator for grouped bulk operations
  • Batch step failed-record handling and job failure thresholds
  • Batch block size and concurrency tuning against memory and disk I/O
  • Aggregators module for grouping a stream of independent events
  • Queue-based load leveling and consumer scaling for high message volumes
  • Least-privilege control plane access with business groups, environments and teams
  • Federated SSO to Anypoint Platform with external identity providers
  • Access Management security settings for platform sign-in
  • Connected apps versus user credentials for programmatic platform access
  • Secure connectivity between customer-hosted runtimes and the control plane
  • Client applications, API contracts and SLA tiers for API consumers
  • Choosing API Manager policies for access control and threat protection
  • Client providers for external OAuth 2.0 authorization servers
  • Choosing an API policy enforcement point for Mule and non-Mule services
  • Gatekeeper protection during API deployment and restart
  • Anypoint Security layered edge protection model
  • Anypoint Security availability across deployment options
  • Choosing Anypoint Security Edge policies for threat scenarios
  • Edge security policies versus API Manager policies
  • Secrets Manager for TLS contexts and keystores
  • Inbound TLS termination and last-mile encryption on Runtime Fabric
  • Tokenization versus encryption for sensitive data
  • One-way versus mutual TLS for Mule HTTP listeners and requesters
  • Message-level encryption and signing versus transport-level TLS
  • Preventing injection and other OWASP Top 10 flaws in Mule flows
  • Protecting property values and the decryption key at deployment by runtime target
  • Mule runtime installation hardening on premises versus Runtime Fabric
  • Platform audit logs versus Mule application and runtime logs
  • Audit log entry contents and audited platform actions
  • Audit log access permissions and business group scoping
  • Audit log retention configuration for compliance requirements
  • Exporting audit logs to external SIEM and observability tools
  • Stages of a CI/CD pipeline for Mule applications
  • Mule Maven plugin goals and POM setup
  • MUnit tests and coverage gates in the CI build
  • Authenticating automated deployments to Anypoint Platform
  • Publishing application artifacts and API assets to Exchange from a pipeline
  • Choosing the Mule Maven plugin deployment strategy for each runtime target
  • Per-environment deployment settings in the Mule Maven plugin configuration
  • Promoting a tested artifact across environments without rebuilding
  • Anypoint Platform APIs for programmatic platform operations
  • Anypoint CLI capabilities and operating characteristics
  • Connected app grant types, scopes and allowlisting
  • Choosing among Maven plugin, Anypoint CLI, Platform APIs and Terraform for automation
  • Log4j 2 configuration resolution for Mule 4 applications
  • Logger categories and log levels in log4j2.xml
  • Asynchronous versus synchronous loggers in Mule applications
  • Built-in log viewing and search in Runtime Manager
  • Package-level log level changes across deployment targets
  • Forwarding CloudHub and CloudHub 2.0 logs to an external logging system
  • Runtime Fabric log forwarding versus external Log4j appenders
  • Logging design for hybrid and on-premises Mule runtimes
  • Centralized log aggregation across mixed deployment targets
  • Correlation IDs and MDC enrichment with the Tracing module
  • Anypoint Monitoring purpose and core capabilities
  • Runtime Manager monitoring versus Anypoint Monitoring
  • Enabling Anypoint Monitoring for CloudHub and CloudHub 2.0 apps
  • Anypoint Monitoring agent for hybrid servers, server groups and clusters
  • Anypoint Monitoring on Runtime Fabric
  • Built-in dashboards versus custom dashboards and custom metrics
  • Alerting options in Anypoint Monitoring and Runtime Manager
  • Anypoint Monitoring log management capabilities
  • API Functional Monitoring for scheduled endpoint checks
  • Distributed tracing of Mule app transactions
  • Subscription tier effects on Anypoint Monitoring features
  • Exporting monitoring data to external observability tools

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