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Platform Development Lifecycle and Deployment Architect

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0/133Learned

Platform Development Lifecycle and Deployment 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

0/133Learned

  • Assessing ALM maturity of a customer organization — identifying signals of low, medium, and high maturity and the governance gaps each implies
  • Ad hoc and repeatable ALM maturity levels — characteristics and the tooling, process, or structural changes to advance
  • Defined and managed ALM maturity levels — characteristics and the tooling, process, or structural changes to advance
  • Optimizing ALM maturity level — characteristics and continuous improvement practices
  • Comparing waterfall vs. agile vs. hybrid development methodologies in the context of Salesforce ALM — when to recommend each based on project risk, team maturity, and delivery cadence constraints
  • Risk-based trade-offs between agile speed and governance control — recommending iterative delivery models while maintaining compliance and change management requirements
  • Governance strategy selection based on team size, project complexity, and regulatory requirements — choosing the appropriate level of process formality and oversight
  • The role of a Center of Excellence (CoE) in Salesforce ALM governance — when to recommend establishing one and what it should own
  • Release approval gates in a Salesforce deployment pipeline — purpose and appropriate use cases
  • Change advisory board (CAB) — purpose, composition, and appropriate use cases in a Salesforce deployment pipeline
  • Identifying ALM maturity gaps across people, process, and technology dimensions for a customer with ad-hoc deployments and no version control
  • Developer, QA, admin, and architect roles and handoffs in a mature Salesforce ALM team
  • Release manager role and responsibilities in a mature Salesforce ALM team
  • Source control and CI/CD tool evaluation for a Salesforce ALM pipeline
  • Static analysis and deployment tooling evaluation for a Salesforce ALM pipeline
  • Building a roadmap to improve ALM maturity for a customer currently using manual change sets — sequencing process, tooling, and training improvements
  • Risks introduced by direct production changes and the mitigation steps required to restore ALM integrity
  • Configuration drift and data leakage as environment risks — recommending mitigation strategies
  • Technical debt accumulation risk in long-lived orgs — identifying signals and recommending governance controls to prevent further drift
  • Mitigating the risk of conflicting parallel development streams sharing a single sandbox — recommended environment isolation strategies
  • Sandbox refresh lag as an environment risk — recommending mitigation strategies
  • Selecting a governance framework (ITIL, SAFe, Agile, custom hybrid) for a Salesforce program based on organizational culture and release cadence requirements
  • Defining approval gates and escalation paths within a Salesforce governance framework for an enterprise customer with multiple business units
  • Release coordination across vendor partners in multi-vendor Salesforce implementations
  • Change ownership and IP rights in multi-vendor Salesforce implementations
  • Strategies for assessing the impact of a Salesforce seasonal release on an existing org — sandbox preview, release notes analysis, and regression testing planning
  • Salesforce release preview sandbox window and opt-out mechanisms — understanding which sandbox types receive preview instances, the window duration, and how to opt an org out of a release window
  • Using sandbox preview instances to evaluate seasonal release impacts before production upgrade — timing, access, and testing scope considerations
  • Impact of a Salesforce seasonal release on an active CI/CD pipeline — identifying which automated tests, API versions, or deployment scripts may break and planning remediation before the upgrade window
  • Critical-update opt-in and auto-activation timelines — architectural implications of deferring or accepting critical updates in production
  • Communicating seasonal release changes to business stakeholders and coordinating UAT cycles around Salesforce's release schedule
  • Capabilities and benefits of agile project management tools (e.g., Jira, Azure Boards) in managing Salesforce sprint work, backlog grooming, and release planning
  • Integrating agile tracking tools with Salesforce source control and CI pipelines — traceability from story to deployment artifact
  • Single-org vs. multi-org architecture trade-offs — evaluating data isolation, licensing, complexity, and integration overhead given business unit separation requirements
  • Org strategy considerations for an acquisition scenario — deciding whether to merge into an existing org or maintain separate orgs based on business and technical constraints
  • Governor limits and platform capacity constraints as architectural drivers in org strategy — identifying when limits should influence org design decisions
  • Developer and Developer Pro sandbox types — capabilities, refresh frequency, and appropriate use cases
  • Partial Copy and Full Copy sandbox types — capabilities, data volume, refresh frequency, and appropriate use cases
  • Sandbox refresh strategy — when to refresh, impact on active development, and sequencing refreshes around release cycles and seasonal Salesforce releases
  • Designing a sandbox environment hierarchy for an org with multiple parallel development streams, a dedicated training environment, and a staging environment for UAT
  • Hotfix environment strategy — using a dedicated sandbox to isolate critical production fixes without contaminating ongoing development branches
  • Scratch org usage as part of a package-based development environment strategy — when scratch orgs replace or complement traditional sandboxes
  • Native deployment tools — Change Sets, Salesforce CLI (sf deploy), and ANT Migration Tool — strengths, weaknesses, and appropriate scenarios
  • Change Set limitations — no destructive changes in the same deployment, no version tracking, point-to-point topology only, manual dependency ordering
  • When Change Set limitations warrant migration to CLI or CI/CD tooling
  • Deployment validation vs. quick deploy — when to use quick deploy, the conditions that qualify a deployment for quick deploy, and the risks
  • CI/CD pipeline architecture for Salesforce — defining stages (build, validate, test, deploy) and the role of each tool in the pipeline
  • Salesforce DevOps Center — its role as a native release management layer, how it integrates with GitHub-based source control, and the scenarios where it is preferred over third-party tools
  • Third-party deployment tools (Copado, Gearset, Flosum) for Salesforce — strengths, weaknesses, and when to use
  • CI/CD platform selection for Salesforce — comparing GitHub Actions, Jenkins, Azure DevOps, and Bitbucket Pipelines for Salesforce pipeline orchestration based on team toolchain and pipeline complexity
  • Metadata API deployment order dependencies — understanding the order metadata components must be deployed to avoid dependency failures
  • Git branching strategies for Salesforce development — trunk-based development, Gitflow, and feature branching — trade-offs and recommended use cases for each
  • Branching strategy for a customer with a hotfix requirement alongside an active feature branch — coordinating the release of both without regression
  • Handling declarative changes (configuration) alongside programmatic changes (code) in the same source control repository — tracking flow and layout changes with metadata API
  • Managing merge conflicts in Salesforce XML metadata — strategies and tooling to detect, prevent, and resolve conflicts in metadata files
  • Semantic versioning and tagging strategies in Salesforce source control — aligning Git tags to production releases for rollback and audit purposes
  • Minimum Apex code coverage requirements for production deployment — understanding the 75% org-wide floor, the 1% per-trigger requirement, what counts toward coverage, and what does not
  • Positive path and negative path Apex unit test design — when each is required
  • Permission-based unit tests — testing record access, FLS, and profile/permission set assignments in Apex using System.runAs()
  • Test data isolation in Apex — @isTest(SeeAllData=false) as the default behavior, when SeeAllData=true is justified, and the risks of tests depending on org data
  • Test data factory patterns in Apex — building reusable test data factories vs. using @testSetup and when to prefer each approach
  • Apex mock frameworks for callout testing — implementing HttpCalloutMock and WebServiceMock (StubProvider) to isolate unit tests from external dependencies
  • Bulk and large data volume Apex unit test design — when required and how to implement
  • Large data volume testing in Apex — using Test.startTest/stopTest, governor limit resets, and test data volume to simulate production load conditions
  • Org-based development vs. package-based development — key differences in source tracking, deployment mechanism, and team collaboration model
  • Salesforce CLI source-tracking in org-based development — how sf project deploy/retrieve and source tracking differ from the traditional Metadata API pull/push model
  • Scratch org capabilities and limitations — shape, definition files, data seeding, and how scratch orgs enable consistent, reproducible development environments
  • Choosing between scratch orgs and developer sandboxes for a given team structure — factors including cost, lifecycle duration, concurrency, and team size
  • Scratch org expiry and lifecycle management — default 7-day expiry, maximum 30-day limit, implications for long-running feature branches, and strategies to avoid losing work
  • sfdx-project.json structure and packageDirectories configuration — how project definition files govern source format, package membership, and multi-package project layout
  • Unlocked packages as a migration path from org-based to package-based development — use case selection criteria and organizational prerequisites
  • Defining Salesforce Apex and LWC coding standards — naming conventions, trigger frameworks, bulkification requirements, and documentation standards
  • Pull request review process for Salesforce development — what reviewers should check, required approvals, and how PRs gate CI pipeline execution
  • PMD and Salesforce Code Analyzer for Apex static analysis — rules enforced and when to use each
  • Integrating static analysis into the CI pipeline — failing builds on critical violations vs. reporting-only gates and the governance tradeoff
  • Metadata API capabilities for deployment — supported metadata types, deployment size limits, and asynchronous deploy/retrieve lifecycle
  • Metadata API limitations — unsupported metadata types (e.g., some standard objects, reports in certain contexts), what must be migrated manually
  • Tooling API vs. Metadata API for deployment — when to use Tooling API (incremental Apex, real-time compile) vs. Metadata API (full deployments)
  • Deploying metadata with dependencies — managing the order and packaging of components when cross-component references would cause deployment failures
  • Destructive changes in Salesforce deployments — using destructiveChanges.xml and destructiveChangesPre.xml to delete metadata components, limitations when combined with additive changes in the same deployment
  • Deployment rollback strategies — Salesforce's lack of native rollback and the mitigation patterns (destructive changes, backup, version control revert) architects use
  • Deployment runbook design — documenting pre/post steps, rollback procedures, and validation steps for a production deployment
  • Pre-deployment checklist — disabling automation and preparing the target org before a deployment window
  • Managing user access before and during a deployment window
  • Post-deployment steps — re-enabling flows/workflows, assigning permission sets, and activating record types
  • Post-deployment data migrations and when they are required
  • Reports, dashboards, connected apps, and custom settings — Metadata API deployability limitations and manual deployment candidates
  • Distinguishing technical reference data (picklist values, custom settings, custom metadata) from transactional data — implications for deployment approach
  • Custom Metadata Types vs. Custom Settings for storing reference data — differences in deployability, access patterns, and packaging support
  • Managing environment-specific reference data (e.g., integration endpoints, feature flags) using Custom Metadata with deployment-time overrides vs. Custom Settings with manual updates
  • Deploying reference data using Salesforce CLI data commands, Data Loader scripts, or managed/unlocked package records — trade-offs in automation and reliability

Given a customer scenario, describe and recommend an appropriate testing methodology.

0/5

  • Testing pyramid for Salesforce — unit tests, integration tests, system tests, and UAT — scope, ownership, and tooling for each level
  • Shift-left testing strategy for Salesforce — integrating automated testing earlier in the development cycle to reduce late-stage defect cost
  • Recommending test methodology for an org with complex automation and integrations — balancing Apex unit tests, end-to-end testing, and manual regression
  • Regression test strategy for a high-volume, frequently-released Salesforce org — selecting what to automate, what to test manually, and what to deprioritize
  • Browser/UI automation testing for Salesforce Lightning using tools like Selenium, Provar, or Copado Robotic Testing — when to introduce automated UI tests

Given a customer testing strategy, describe the appropriate test execution methodology and coverage requirements.

0/6

Code coverage calculation in Salesforce — how org-wide coverage is computed, what triggers a coverage check, and how to interpret test coverage results

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Apex test execution options — running local tests, all tests, or specified tests — and when each is appropriate for CI/CD validation vs. full regression

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RunSpecifiedTests vs RunLocalTests vs RunAllTests — when to use each and coverage implications

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When Salesforce requires RunLocalTests or higher for production deployments

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Managing test execution time in large orgs — parallelism, test class isolation, and strategies to keep CI pipeline duration acceptable

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Test coverage degradation risk in package-based development — ensuring dependent package tests run and cumulative coverage is maintained

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Given a customer scenario, describe and recommend a unified test data strategy that utilizes representative data in a secure manner throughout the development lifecycle.

0/4

  • Unified test data strategy design — how to provide representative, consistent test data across all environments without exposing production PII
  • Partial Copy sandbox and test data seeding — using a Partial Copy sandbox as the source of representative data for lower environments
  • Data masking and anonymization strategies for Salesforce test environments — tools and approaches to protect sensitive data in sandboxes
  • Test data lifecycle management — refreshing and maintaining test data sets aligned to sandbox refresh cycles to prevent test data staleness
  • Managed package use cases — ISV distribution, IP protection, subscriber upgrade lifecycle, and limitations on post-release changes
  • Unlocked package use cases — internal modular deployment, dependency management, and source-driven development without ISV constraints
  • Choosing between managed, unmanaged, and unlocked packages for a given customer release scenario — evaluating IP protection, upgradeability, and deployment speed requirements
  • Package versioning — major vs. minor versions and semantics
  • Package dependencies — defining and managing dependencies between unlocked packages in a multi-package project
  • Namespace considerations for managed vs. unlocked packages — when a namespace is required, its impact on component naming, and implications for future package evolution
  • Deprecated components and subscriber impact during package upgrades
  • Mapping a sandbox environment strategy to a release plan with three parallel development streams and an upcoming major release
  • Staging sandbox role in the release plan — full copy sandbox as pre-production gate, UAT execution environment, and performance testing target
  • Hotfix stream integration into an ongoing release plan — resolving conflicts between a hotfix branch and in-flight feature branches before next release
  • Integrating a training environment into the release plan — when to refresh, how to isolate from development changes, and who owns data in the training sandbox
  • Release management process — change freeze periods, go/no-go criteria, release notes, and post-release monitoring
  • Release cadence selection — continuous delivery, sprint-based, or scheduled major release approaches and their fit for different customer contexts
  • Coordinating a release across multiple Salesforce orgs — sequencing deployments, managing shared reference data, and handling rollback across orgs
  • Feature flagging and dark launching in Salesforce — using custom metadata or permission sets to release code to production without activating features
  • Blue-green and canary release patterns applied to Salesforce — using permission sets, profiles, or custom metadata to control phased feature rollout to user populations
  • Implications of making configuration changes directly in production — impact on source of truth, configuration drift, and downstream environment sync
  • Urgent production change request under pressure — evaluating the risk of a direct production change vs. the risk of delay through a proper change management process
  • Emergency change process design — defining a sanctioned fast-track path for production changes that preserves auditability and rollback capability
  • Steps to integrate a direct production change back into the ALM pipeline — retrieve, commit to source control, back-propagate to lower sandboxes, and document
  • Detecting and reconciling configuration drift when production has diverged from source control — tools and processes to identify delta and re-synchronize
  • Governance controls to prevent unauthorized direct production changes — change approval workflows, production org lockdown strategies, and audit trail monitoring
  • Strategies for managing shared metadata components across multiple Salesforce orgs — packages, template repos, and manual synchronization compared
  • Using unlocked packages to share and version common components across multiple orgs — package subscriber model and update propagation process
  • Release artifact governance in a multi-org landscape — defining ownership, approval, and promotion of shared components to prevent fragmentation
  • Cross-org change management coordination — aligning release windows, dependency tracking, and regression testing when changes affect multiple connected orgs
  • Event logs and deployment history for post-release monitoring and diagnosis

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RunSpecifiedTests vs RunLocalTests vs RunAllTests — when to use each and coverage implications

Explainer

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Practice Question

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Next conceptWhen Salesforce requires RunLocalTests or higher for production deployments

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Explainer

Deployment test levels determine which Apex tests execute during metadata deployment to ensure code quality and coverage. You select a test level in the deployment options to override default behavior or match specific testing requirements for sandbox and production environments.

Core information
  • RunSpecifiedTests executes only the test classes explicitly listed in the deployment options, requiring 75% code coverage for each deployed Apex class and trigger individually.
More details and nuances
  • Code coverage for RunSpecifiedTests is computed individually for each class and trigger in the deployment package, meaning the overall organization coverage percentage does not satisfy the requirement.