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MuleSoft Platform Architect

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MuleSoft Platform Architect Study Checklist
  • Platform Administrator
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  • Platform Data Architect
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0/228Learned

  • API-led connectivity versus point-to-point and ESB-centric integration
  • REST, SOAP, RPC, GraphQL and gRPC as interface technologies
  • API specification languages as the contract technology of API-led connectivity
  • Mule runtime, connectors and APIkit as API implementation technology
  • Roles of API implementations, API proxies and API clients at runtime
  • Synchronous web APIs versus asynchronous messaging in an application network
  • 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
  • Assigning APIs to layers by functional focus
  • 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

Identify appropriate APIs to implement a business process and assign them to layers of API-led connectivity

0/5

Benefits of decoupled API-led layers over point-to-point integration

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Decomposing a business process into System, Process and Experience APIs

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Matching protocols and interface styles to API-led layers

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When to build Experience APIs, per channel or shared across channels

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Bypassing an API-led layer versus adding a pass-through API

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Assign APIs to layers according to ownership, functional focus, and rate of change

0/3

  • 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

Recommend the most appropriate approach relating the API data model of System APIs to that of their backend system based on specific requirements and organizational characteristics

0/3

  • 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

Recommend the use of an Enterprise Data Model or Bounded Context Data Models based on a set of APIs and specific preferences and organizational characteristics

0/4

  • 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

Select DataGraph only for suitable roles in an application network, according to its high-level features

0/2

  • 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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  1. MuleSoft Platform Architect Checklist
  3. Designing APIs using System, Process, and Experience Layers

When to build Experience APIs, per channel or shared across channels

Explainer

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Practice Question

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This is one of 228 concepts on the Salesforce MuleSoft Platform Architect exam.

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Next conceptBypassing an API-led layer versus adding a pass-through API

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0 of 228 concepts learned

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  1. MuleSoft Platform Architect Checklist
  3. Designing APIs using System, Process, and Experience Layers
When to build Experience APIs, per channel or shared across channels

Explainer

Learn More

Practice Question

Studying for this exam?

This is one of 228 concepts on the Salesforce MuleSoft Platform Architect exam.

Looking for practice exams? Click below to try one!

Keep going

Next conceptBypassing an API-led layer versus adding a pass-through API

Explainer

Experience APIs are the top layer of API-led connectivity. They take the data and processes unlocked by System and Process APIs and shape them for a specific audience, such as a mobile app or an internal customer portal. The design question is whether a channel needs an Experience API at all, and if so, whether each channel gets its own tailored Experience API or several channels share one.
Core information
  • API-led connectivity sorts APIs into three layers by role. System APIs unlock data from core systems of record. Process APIs combine data and orchestrate System APIs for a business purpose. Experience APIs expose that data to its intended consumer.
More details and nuances
  • Choosing between per-channel and shared Experience APIs is a trade-off between tailoring and footprint. Per-channel APIs fit each consumer most closely, while a shared API cuts the number of APIs, flows and apps. The full API-led approach is highly recommended for its low maintenance costs and support for future growth. Creating APIs only where necessary is a simplification for teams that want a less complex architecture.

Explainer

Experience APIs are the top layer of API-led connectivity. They take the data and processes unlocked by System and Process APIs and shape them for a specific audience, such as a mobile app or an internal customer portal. The design question is whether a channel needs an Experience API at all, and if so, whether each channel gets its own tailored Experience API or several channels share one.
Core information
  • API-led connectivity sorts APIs into three layers by role. System APIs unlock data from core systems of record. Process APIs combine data and orchestrate System APIs for a business purpose. Experience APIs expose that data to its intended consumer.
More details and nuances
  • Choosing between per-channel and shared Experience APIs is a trade-off between tailoring and footprint. Per-channel APIs fit each consumer most closely, while a shared API cuts the number of APIs, flows and apps. The full API-led approach is highly recommended for its low maintenance costs and support for future growth. Creating APIs only where necessary is a simplification for teams that want a less complex architecture.