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Cert++

Resource Pack

Platform Data Architect

The Salesforce Certified Platform Data Architect credential is one of the harder architect-domain exams. It tests your ability to design data models that hold up under real enterprise conditions: large data volumes, multi-system master data management, GDPR compliance, and migration at scale. If you have been working with Salesforce for years and have wrestled with data skew, skinny tables, or bulk API tuning, this exam will feel familiar. If you have not, the official trailmix plus the Large Data Volumes module are your starting points.

3-Step Path to Passing

  1. 1

    Complete the Data Architecture Trailmix

    Salesforce's official trailmix covers the entire exam scope. The Large Data Volumes module is the most important one, so do not skip it. Work through the trailmix in order and you will cover around 70% of what the exam tests.
  2. 2

    Attempt Practice Exams

    I recommend my own practice exams, but I have linked other options in the Study Resources section below. This exam is scenario-heavy, so practicing with scenario questions is essential preparation.
  3. 3

    Schedule Your Exam

    Exams run every day, at all hours of the day. Schedule with short notice when you feel confident. I would suggest waiting until you are consistently scoring above 70% on practice exams before booking.

Core Resources

Exam Overview

Questions

65

60 scored + 5 unscored

Duration

105 min

1 hour 45 minutes

To Pass

65%

Minimum Score

Question Format

The exam tests data architecture judgment across six domains: data modeling for scale, master data management, Salesforce data management tools, data governance and GDPR, large data volume design, and data migration. The majority of questions are scenario-based and require selecting the best architectural approach given specific constraints.

Scored

92%

60questions

Unscored

8%

5questions

Exam Details

Pricing

$200 registration · $100 retake

Delivery

Online proctored or at a testing center

Experience

5+ years recommended

Prerequisites

No prerequisites required

Exam Topics

Each topic section shows the topic weight, learning objectives, and links to study resources.

Data Modeling/Database Design25%

Object relationships and their implications for sharing and cascade delete, junction objects, field types, formula fields vs. custom metadata types, big objects use cases and constraints, data skew types and mitigation, and schema design for multi-cloud orgs.

  • Compare and contrast various techniques and considerations for designing a data model for the Customer 360 Platform.
  • Given a scenario, recommend approaches and techniques to design a scalable data model that obeys the current security and sharing model.
  • Compare and contrast various techniques, approaches and considerations for capturing and managing business and technical metadata.
  • Compare and contrast the different reasons for implementing big objects versus standard/custom objects within a production instance.
  • Given a customer scenario, recommend approaches and techniques to avoid data skew.

Resources

Master Data Management5%

MDM implementation styles (registry, consolidation, coexistence, centralized hub), golden record survivorship rules, external ID strategy for cross-system identity, data quality and deduplication, and data enrichment from external reference data.

  • Compare and contrast the various techniques, approaches and considerations for implementing master data management (MDM) Solutions.
  • Given a customer scenario, recommend and use techniques for establishing a golden record or system of truth for the customer domain in a single org.
  • Given a customer scenario, recommend approaches and techniques for consolidating data attributes from multiple sources.
  • Given a customer scenario, recommend appropriate approaches and techniques to capture and maintain customer reference and metadata.

Resources

Salesforce Data Management25%

License type selection and object access, single view of the customer across systems, Salesforce Connect and external objects for data virtualization, data consistency patterns, and consolidating data across multiple Salesforce instances.

  • Given a customer scenario, recommend appropriate combination of Salesforce license types to effectively leverage standard and custom objects.
  • Given a customer scenario, recommend techniques to ensure data is persisted in a consistent manner.
  • Given a scenario with multiple systems of interaction, describe techniques to represent a single view of the customer on the Salesforce Platform.
  • Given a customer scenario, recommend a design to effectively consolidate and/or leverage data from multiple Salesforce instances.

Resources

Data Governance10%

GDPR compliance design (field encryption, right to erasure, data residency, anonymization vs. deletion), Shield Platform Encryption key management and trade-offs, enterprise data governance programs, data classification, California Consumer Privacy Act basics, and Privacy Center.

  • Given a customer scenario, recommend an approach for designing a GDPR compliant data model.
  • Compare and contrast various approaches and considerations for designing and implementing an enterprise data governance program.

Resources

Large Data Volume Considerations20%

LDV data model design, skinny table eligibility and provisioning, custom index eligibility and null-value limitations, SOQL selective query design, data archiving and purging strategies, big objects for historical data, virtualized data options, and asynchronous processing for large record sets.

  • Given a customer scenario, design a data model that scales considering LDV and solution performance.
  • Given a customer scenario, recommend a data archiving and purging plan that is optimal for the customer's data storage management needs.
  • Given a customer scenario, decide when to use virtualized data and describe virtualized data options.

Resources

Data Migration15%

Data quality at load time (field mapping, validation, deduplication), Bulk API 2.0 vs. SOAP API performance trade-offs, parallel vs. serial DML modes, batch size tuning, external IDs for referential integrity, and data export strategies.

  • Given a customer scenario, recommend appropriate techniques and methods for ensuring high data quality at load time.
  • Compare and contrast various techniques for improving performance when migrating LDVs into Salesforce.
  • Compare and contrast various techniques and considerations for exporting data from Salesforce.

Resources

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