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Platform Data Architect

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Platform Data 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
  • Slack Administrator

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Compare and contrast various techniques and considerations for designing a data model for the Customer 360 Platform

0/15

Standard Sales and Service objects versus custom objects for core entities

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Normalized versus denormalized object design

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Lookup versus master-detail relationship selection

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Lookup deletion options and cascade-delete risk

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Roll-up summary fields versus other aggregation approaches

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Junction objects for many-to-many relationships

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Contacts to Multiple Accounts versus custom junction modeling

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Self-relationships and hierarchical relationships for modeling hierarchies

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Polymorphic relationship fields versus dedicated lookup fields

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External ID and unique fields for source-system keys

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Record types versus separate objects for process variants

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Person Accounts versus the business Account and Contact model

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Optional custom object features and their data model impact

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Salesforce Connect external objects versus copying or replicating external data

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Relationship types for linking external objects to Salesforce data

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Given a scenario, recommend approaches and techniques to design a scalable data model that obeys the current security and sharing model

0/9

  • Organization-wide defaults for confidential versus non-confidential data
  • Role hierarchy design for data access and scale
  • Sharing rules and public group design at scale
  • Lookup versus master-detail relationships for record access inheritance
  • Controlled by Parent and implicit sharing on account child records
  • Field-level security versus a separate restricted object for sensitive data
  • Object-level View All and Modify All versus org-wide data permissions
  • Data model requirements for high-volume external user access
  • Planning sharing recalculation for default changes and mass realignments

Compare and contrast various techniques, approaches and considerations for capturing and managing business and technical metadata

0/5

  • Business dictionary for shared definitions and data ownership
  • Technical data dictionary and ERDs maintained from org metadata
  • Taxonomy design with controlled vocabularies and reference objects
  • Field-level data classification for sensitivity and compliance
  • Data lineage for multi-source records and downstream dependencies

Compare and contrast the different reasons for implementing big objects versus standard/custom objects within a production instance, alongside the unique pros and cons of utilizing big objects in a Salesforce data model

0/6

  • Big objects versus standard and custom objects for high-volume data
  • Big object index design and record identity
  • Loading data into big objects and handling partial batch failures
  • Surfacing big object data in the UI, reports and dashboards
  • Automation and transaction limits when adopting big objects
  • Security, sharing and encryption limits of big objects for sensitive data

Given a customer scenario, recommend approaches and techniques to avoid data skew

0/6

  • Preventing parent-child account data skew in the data model
  • Remediating an existing skewed parent account
  • Ownership data skew and placement of skewed record owners
  • Lookup skew detection and mitigation
  • Relationship field count on heavily loaded objects
  • Reducing record lock contention in bulk loads and integrations
  • Choosing an MDM implementation style for a multi-system landscape
  • Canonical data model for harmonizing multiple source systems
  • Harmonizing and standardizing multi-source data before matching
  • Match methods, attribute weights and confidence thresholds
  • External reference data for enriching master records
  • Hierarchy management for master data in Salesforce
  • Classifying duplicate types before merging or relating customer records
  • Consolidating existing duplicate customer records into a master record
  • Data management plan controls for sustaining a single-org golden record
  • Survivorship criteria for choosing winning attribute values
  • Attribute-level source ownership versus whole-record survivorship
  • Middleware, in-org automation or Data 360 for applying survivorship
  • Data stewardship and business ownership for resolving master data conflicts
  • Source keys and provenance for tracing consolidated customer records
  • Shared data definitions and business metadata for customer rules
  • Reference data stores for consistent customer business rules
  • Salesforce versus Salesforce Platform licenses for internal users
  • Standard versus custom objects for Platform-licensed user populations
  • Permission set licenses layered on a user license
  • Chatter, Identity, and other narrow-scope licenses for limited-access users
  • Integration user licensing for API-only system access
  • Experience Cloud license tiers for external customers and partners
  • Seat-based versus login-based licensing for occasional users
  • License combination for a mixed internal and external user population
  • Enforcing required values across UI, API and import channels
  • Validation rules, field types and restricted picklists for consistent values
  • State and country/territory picklists for standardized addresses
  • Declarative automation versus Apex triggers to normalize data on save
  • Matching and duplicate rules to prevent duplicates at entry
  • Third-party enrichment and data integration rules for accurate records
  • Record locking and all-or-none scope for consistent related-record writes
  • System of record versus system of reference for customer data ownership
  • External ID keys and upsert for correlating records across systems
  • Keeping customer data synchronized across systems of interaction
  • Duplicate merge versus unified profile versus golden record for customer unification
  • Single-org consolidation versus a multi-org strategy for a growing enterprise
  • Hub-and-spoke topology for distributing data across multiple orgs
  • Cross-org adapter virtual access versus replicating another org's data
  • Keeping shared records consistent across orgs with cross-org synchronization
  • Aggregating data from several orgs into a consolidated org or data store
  • Consolidating records from multiple source orgs into one target org
  • Personal, sensitive, pseudonymous and anonymous data in a Salesforce model
  • Individual object for tracking data privacy preferences
  • Consent data model for purpose-specific and channel-specific consent
  • Data subject access and portability request fulfillment
  • Deleting versus anonymizing personal data for erasure requests
  • Data minimization and retention design for personal data
  • Classification-driven protection versus uniform protection of every field
  • Layered access controls versus encryption for restricting who sees personal data
  • Shield Platform Encryption, Classic Encrypted Custom Fields or tokenization
  • Deterministic versus probabilistic encryption for queried personal data
  • Audit and monitoring options for access to and changes on sensitive data
  • Keeping personal data out of sandboxes and test environments
  • Data residency requirements and org strategy for EU personal data
  • Data owner, data steward and data custodian responsibilities
  • Governance council and operating model across business units
  • Standards to define in an enterprise data management plan
  • Declarative Salesforce controls for enforcing governance standards
  • Measuring and monitoring data quality dimensions over time
  • Prioritizing and phasing a governance program rollout
  • Storage option selection by record volume, data location and usage
  • Selective filters for list views, reports and queries on LDV objects
  • Custom indexes for frequently filtered fields on large objects
  • When to use skinny tables on large objects
  • Denormalization to reduce joins and formula-field filters at scale
  • Pre-aggregated summary objects for reporting over millions of records
  • Search design and divisions for text lookups across large objects
  • Sharing model and relationship choices that limit recalculation at scale
  • Lookup skew under concurrent high-volume inserts and updates
  • Integration extraction patterns that avoid sharing overhead on LDV objects
  • Big object limitations and surfacing big object data to users
  • Archive versus purge versus backup in a data retention plan
  • Tiering active, warm, cold and expired data across storage options
  • Retention periods and purge criteria for regulated data
  • Selecting archive candidates and preserving record context
  • Big objects as an on-platform archive for historical records
  • External archive stores and on-demand access to archived records
  • Field history retention beyond the standard tracking window
  • Bulk API hard delete versus Recycle Bin soft delete for mass purges
  • Reducing file storage versus data storage consumption
  • Ongoing archiving to sustain performance as data volumes grow
  • Packaged archiving solutions versus custom-built archive pipelines
  • Virtualizing external data versus replicating it into Salesforce
  • Salesforce Connect adapter selection by source system
  • External object feature support compared with custom objects
  • Rate limits and query performance for external objects at high volume
  • Salesforce Connect versus request-reply callouts and UI mashups
  • Heroku Connect data proxy versus data replication
  • Source-to-target field mapping and org preparation before import
  • Staging-area profiling and cleansing versus post-load cleanup
  • Standardizing picklist, address and formatted values before load
  • External ID keys and upsert for repeatable, duplicate-safe loads
  • Validation rules and required fields as load-time quality controls
  • Matching and duplicate rules as a load-time quality gate
  • Preserving legacy audit fields and record ownership during migration
  • Test imports and post-load reconciliation of migrated data
  • Data Import Wizard versus Data Loader versus ETL tools for LDV loads
  • Bulk API versus SOAP and REST APIs for LDV migration loads
  • Bulk API 2.0 versus Bulk API 1.0 for migration jobs
  • Batch size tuning for load throughput, timeouts and retries
  • Insert, update and upsert choice for load speed
  • Loading lean: minimal data set and deferred relationships
  • Load sequencing and configuration kept in place during migration
  • Suspending triggers, validation rules and workflow during LDV loads
  • Deferring sharing calculation during LDV loads
  • Full copy sandbox load rehearsal and load-window planning
  • Data Export Service versus Data Loader for exporting org data
  • Data Export Service limits as a backup and recovery mechanism
  • Data Loader Export versus Export All for deleted and archived records
  • Data Loader export query scope and unsupported fields
  • Record visibility and protection of exported data
  • Bulk API versus SOAP and REST API for large data extracts
  • PK chunking versus filter-based chunking for very large extracts
  • Full, incremental, and partial backup strategies
  • Native export tools versus Salesforce Backup and AppExchange backup solutions

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