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MuleSoft Platform Architect

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MuleSoft Platform 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
  • Marketing Cloud Account Engagement Specialist
  • Marketing Cloud Next 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 Communications AP
  • Agentforce Consumer Goods AP
  • Agentforce Financial Services AP
  • Agentforce Health AP
  • Agentforce Manufacturing AP
  • Public Sector Solutions AP
  • Revenue Cloud Consultant
  • 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 Next Consultant
  • Tableau Architect
  • Slack Administrator
  • Slack Consultant
  • Slack Developer

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Identify and differentiate between technologies typically used to implement API-led connectivity

0/6

API-led connectivity versus point-to-point and ESB-centric integration

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REST, SOAP, RPC, GraphQL and gRPC as interface technologies

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API specification languages as the contract technology of API-led connectivity

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Mule runtime, connectors and APIkit as API implementation technology

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Roles of API implementations, API proxies and API clients at runtime

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Synchronous web APIs versus asynchronous messaging in an application network

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Describe the role and characteristics of web APIs

0/5

  • APIs, web services and web APIs compared
  • HTTP methods and resource semantics in web APIs
  • Web API contracts and decoupling clients from implementations
  • Web APIs as building blocks of an application network
  • Internal, partner and external APIs by audience and access

Correctly assign APIs to tiers according to ownership, functional focus, and rate of change

0/1

  • Assigning APIs to layers by functional focus

Describe the capabilities and high-level components of Anypoint Platform

0/6

  • Anypoint Platform components across the API lifecycle
  • API mocking service in design-first API development
  • Anypoint Exchange as the application network's discovery catalog
  • API Manager versus Runtime Manager responsibilities
  • Anypoint Monitoring, API analytics and Anypoint Visualizer capabilities
  • Business groups and environments in Access Management
  • Center for Enablement versus traditional Center of Excellence
  • C4E mandate and cross-functional team composition
  • Sequencing the steps to establish a C4E
  • C4E-driven asset reuse and self-service through Anypoint Exchange
  • Selecting KPIs that measure C4E success
  • MuleSoft Catalyst methodology and its benefits for the IT delivery gap
  • MuleSoft Catalyst three pillars and their focus
  • Control plane versus runtime plane responsibilities
  • Choosing among MuleSoft-hosted, hybrid and customer-hosted plane combinations
  • When Anypoint Platform Private Cloud Edition fits the requirements
  • Selecting a MuleSoft-hosted control plane cloud for regional requirements
  • MuleSoft Government Cloud for FedRAMP-regulated organizations
  • Control plane cloud versus runtime region as independent choices
  • Identity Management versus Client Management on Anypoint Platform
  • SAML 2.0 versus OpenID Connect for Anypoint Platform SSO
  • Organization-wide identity providers versus environment-associated client providers
  • Supported external client provider options
  • External client providers for client registration and OAuth 2.0 enforcement
  • Control plane versus runtime plane data residency
  • Payload residency with customer-hosted Mule runtimes
  • Monitoring metrics and API analytics data residency
  • Application log residency and payload content in logs
  • Object Store v1 versus v2 data location on CloudHub
  • Meeting a regional data residency mandate across both planes
  • Roles of the API, API specification, API implementation and API client
  • Spec-first parallel development of API clients and implementations
  • APIkit scaffolding and request routing from the API specification
  • Client binding to API instance endpoints versus implementation endpoints
  • Impact of API implementation changes on existing API clients
  • API instance asset versions across minor, patch and major API changes
  • Dependency chains between API implementations and the APIs they invoke
  • Specification-driven design of reusable APIs
  • Anypoint Exchange as the catalog of reusable assets
  • API specification fragments as reusable design assets
  • Auto-generated Exchange asset portal for published API specifications
  • Exchange asset visibility versus runtime access protection
  • External developer portals and how new API versions reach them
  • Changes that require a new major API version
  • Minor versus patch increments for backward-compatible API changes
  • Quality-of-service and output value-domain changes in API versioning
  • Major version in the API URL and deprecation of superseded versions
  • Exchange asset version versus API version alignment
  • Anticorruption layer between Bounded Context data models
  • Safe versus idempotent HTTP methods
  • Client retry safety by HTTP method after a timeout or lost response
  • PUT versus POST versus PATCH for create and update operations
  • HTTP-native optimistic concurrency control
  • Benefits of decoupled API-led layers over point-to-point integration
  • Decomposing a business process into System, Process and Experience APIs
  • Matching protocols and interface styles to API-led layers
  • When to build Experience APIs, per channel or shared across channels
  • Bypassing an API-led layer versus adding a pass-through API
  • Functional focus of System, Process and Experience APIs
  • Ownership as a criterion for assigning APIs to layers
  • Rate of change as a criterion for assigning APIs to layers
  • System APIs exposing an existing Enterprise Data Model
  • Bounded Context data models for System APIs without an enterprise model
  • Approximately mirroring a backend data model in a System API
  • Enterprise canonical data model benefits and adoption costs
  • Bounded Context Data Model characteristics and context boundaries
  • Enterprise Data Model versus Bounded Context Data Models for an organization
  • Data model strategy across Experience, Process and System APIs
  • DataGraph unified schema linking, merging and collaboration
  • DataGraph versus a new Experience or Process API for consumer data needs
  • API version, API instance and environment relationships
  • Aligning API Manager environments with backend system environments
  • Separate API instances of one API version for distinct consumer groups
  • Deprecating versus deleting an API instance during consumer migration
  • Gateway policies versus API implementation logic for meeting NFRs
  • Policy application scope: resource-level, API-wide and automated
  • Rate Limiting versus Spike Control for throughput NFRs
  • SLA-based rate limiting and SLA tier design for API consumers
  • HTTP Caching policy for response-time and backend-load NFRs
  • CORS, header and message logging policies for non-security NFRs
  • Contract-changing policies versus policies transparent to API clients
  • Client ID Enforcement and the credentials declared in the API specification
  • Reflecting a policy in the spec: traits, snippets, republishing and redeploying
  • Control plane versus runtime roles in Mule Gateway policy enforcement
  • API proxy types and request validation levels
  • API proxy placement relative to clients, backends and network boundaries
  • When to choose Anypoint Service Mesh for policy enforcement
  • Experience API policy selection for external and partner consumers
  • Restricting Process and System API access to callers from the layer above
  • Traffic-protection policies placed by API-led tier
  • Edge-only versus layered policy enforcement across API-led tiers
  • Situations that require API clients to present client credentials
  • Credential placement options for the Client ID Enforcement policy
  • Client application registration and access requests from Exchange
  • SLA tiers and automatic versus manual approval of access requests
  • Approving or rejecting pending access requests in API Manager
  • Revoking versus deleting a client application contract
  • Contract scope across API instances, environments and API Groups
  • Client ID enforcement versus OAuth 2.0 access token enforcement
  • Basic Authentication Simple versus LDAP policies
  • IP allowlist and blocklist policies for restricting call origin
  • JSON and XML threat protection for malicious request payloads
  • Tokenization and detokenization policies for sensitive data
  • Combining and ordering several policies on one API instance
  • Anypoint Platform, external IdP and API gateway roles in an OAuth 2.0 token flow
  • External IdP as SSO identity provider versus OAuth 2.0 client provider
  • Native Anypoint client provider versus external OAuth 2.0 client providers
  • Matching the token enforcement policy to the OAuth 2.0 provider and token type
  • Client provider scope across root organization, business groups and environments
  • Included versus custom API policies for unmet or cross-cutting requirements
  • Purpose and runtime behaviour of Mule API autodiscovery
  • Prerequisites for pairing a Mule application with an API instance
  • Pairing cardinality between Mule applications and API instances
  • Autodiscovery API instance IDs and credentials across environments
  • Basic endpoint with autodiscovery versus a deployed API proxy
  • Requirements that call for an Anypoint VPC versus the shared worker cloud
  • Choosing among Anypoint VPC connectivity methods for a network topology
  • Private API exposure with VPC firewall rules and internal worker DNS
  • Dedicated Load Balancer needs that depend on an Anypoint VPC
  • Isolating environments and business groups with Anypoint VPCs
  • Anypoint VPC address-space sizing and overlap constraints
  • Runtime plane hosting options and operational responsibility
  • Managing customer-hosted Mule runtimes from the MuleSoft-hosted control plane
  • Control plane hosting choices and the runtime planes each supports
  • Unit tests versus integration tests for Mule applications
  • Where MUnit is best employed in an API implementation test strategy
  • Continuous integration responsibilities and benefits for Mule projects
  • Mule Maven plugin for automated deployment of API implementations
  • MUnit tests and coverage gates in an automated build
  • Build-once artifact promotion across Anypoint environments
  • Automated publishing of API specifications and assets to Exchange
  • Automating API Manager instances and policies in a CI/CD pipeline
  • API Functional Monitoring and BAT for automated API testing
  • Application state scenarios suited to Object Store on CloudHub
  • Preserving application state across CloudHub restarts and redeployments
  • Sharing state across multiple workers of one CloudHub application
  • Object Store versus CloudHub persistent queues for durability
  • Object Store v2 capacity, retention and throughput boundaries
  • Object Store regionality in multi-region and disaster recovery designs
  • Worker memory, heap and disk capacity when sizing a CloudHub application
  • vCore subscription consumption by worker size and worker count
  • Fractional versus whole-vCore CloudHub workers for performance consistency
  • Vertical worker sizing versus horizontal worker scale-out on CloudHub
  • CloudHub autoscaling policies for worker size and worker count
  • CloudHub platform redundancy, self-healing and zero-downtime updates
  • Worker count and availability zone failure outcomes
  • Persistent queues reliability and performance tradeoffs
  • Shared Load Balancer limits affecting app performance and reliability
  • Region placement effects on latency and data locality
  • Region outage behavior for apps deployed in one or several regions
  • Control plane outage effects on CloudHub apps and operations
  • Anypoint VPC prerequisite and environment scope of a Dedicated Load Balancer
  • Custom certificates and two-way TLS on Shared versus Dedicated Load Balancers
  • TLS versions and load-balancer-to-worker encryption on CloudHub
  • Vanity domains and DLB mapping rules for many APIs under one domain
  • Static inbound IP addresses for partner allowlisting through a load balancer
  • Shared Load Balancer rate limits versus Dedicated Load Balancer capacity
  • Environment association and deployment region for apps in an Anypoint VPC
  • CloudHub DNS records and ports for public and private worker access
  • Anypoint VPC firewall rules versus DLB allowlists for inbound access
  • Anypoint VPN versus AWS Direct Connect for on-premises data centers
  • VPC peering versus transit gateway attachment for customer-owned Amazon VPCs
  • Anypoint VPC address range and route planning for connected networks
  • Static IP allowlisting versus Anypoint VPC private connectivity to backends
  • Worker count and persistent queues for message-loss and downtime goals
  • Bypassing the CloudHub load balancer with worker-specific URLs
  • Dedicated Load Balancer resilience and client DNS handling
  • Redundant connectivity from Anypoint VPC to on-premises and AWS networks
  • Cross-region disaster recovery design for CloudHub API implementations
  • End-to-end single points of failure across API-led layers
  • Judging whether API data is a good candidate for caching
  • HTTP Caching policy versus Cache scope in the API implementation
  • HTTP Caching policy cache key and cacheable request design
  • Object Store as a shared cache for values from external systems
  • Sharing cached data across CloudHub workers and restarts
  • Object Store versus caching for API implementation requirements
  • Invocation timeouts and timeout budgets across API-led layers
  • Retrying transient API invocation failures
  • Retry safety for non-idempotent API operations
  • Circuit breaker pattern for failing downstream APIs
  • Fallback API invocation, sequential versus parallel
  • Cached fallback results versus static fallback results
  • Layering multiple resilience strategies in one API client
  • Client-side resilience versus provider-side safeguards
  • Vertical versus horizontal scaling of CloudHub workers
  • Response time versus throughput effects of horizontal scaling
  • Stateless design and shared-state overhead when scaling out
  • Workload types that do or do not benefit from scaling out
  • API implementation versus backend bottleneck when adding workers
  • Scaling one layer within an API-led invocation chain
  • Network placement latency and the limits of scaling out
  • Runtime-plane data producers versus control-plane monitoring tools
  • API gateway and API Manager as the source of API invocation data
  • Anypoint Monitoring agent as the source of Mule application metrics
  • Enabling monitoring data collection by runtime deployment target
  • Runtime Manager dashboards and CloudHub worker monitoring as data sources
  • Log data sources and log aggregation for Mule applications
  • Anypoint Visualizer data collection across the application network
  • API Functional Monitoring versus traffic-based API metrics and alerts
  • Alert families and the Anypoint Platform component that raises each
  • Metrics and dimensions captured for managed API invocations
  • Per-consumer usage metrics for API client applications
  • API invocation metrics versus Mule application metrics
  • Application-level invocation metrics versus network and payload data
  • Correlating invocation metrics across Experience, Process and System APIs
  • Matching API instance alert types to non-functional requirements
  • Request count alerts versus traffic-limiting policies
  • Alert design for Experience, Process and System API layers
  • API instance alert triggering and notification behavior
  • Runtime Manager versus Anypoint Monitoring alerts by deployment target
  • Worker availability and deployment outcome alerts for CloudHub apps
  • CPU and memory utilization alerts for Mule app workers
  • Message volume, error and response-time alerts for Mule apps
  • Server, server group and cluster alerts for on-premises Mule runtimes
  • Custom application alerts raised from flows via the CloudHub Connector
  • Alert threshold evaluation, retriggering and notification behaviour

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