Masked, seeded and synthetic test data strategy
How to give every environment representative test data without exposing regulated data: classify fields, mask production copies with Data Mask & Seed, seed related subsets with Seeding by Own from Salesforce, and use synthetic or curated datasets where production data is never needed.
Given a customer scenario, describe and recommend a unified test data strategy that utilizes representative data in a secure manner throughout the development lifecycle.
~8 min read · 13 practice questions ·
What the exam expects
- Recommend one test data standard across scratch orgs, Developer sandboxes, Partial Copy and Full sandboxes that keeps regulated data inside production.
- Configure masking in a current org with Data Mask & Seed: policies, locale, value distribution, scheduling, and migration from the legacy managed package.
- Use data classification metadata to drive what gets masked.
- Choose a Seeding by Own from Salesforce template, source and method, and handle automation that fires during a seed.
- Decide which test layers need baseline data and whether a dataset is small (tree commands) or large (bulk commands).
Key terms: masking (replacing sensitive values in a sandbox with realistic fake values), seeding (copying a related subset of records from a source org into a destination), synthetic data (generated records never derived from production), data classification (field-level sensitivity and compliance metadata), masking policy (the Data Mask & Seed definition of which fields to mask and how).
Key facts
- Detail
- Support ended 2026-07-09; read-only from 2026-12-31
- Detail
- Core app, no installation, available to all customers since Summer '26 (the Spring '26 release note says Beta)
- Detail
- New policies replace configurations; custom libraries shared
- Detail
- Masking jobs under 20M records
- Detail
- Run manually or scheduled
- Detail
- Owner, usage, sensitivity, compliance categorization
- Detail
- Production org or a specific sandbox
- Detail
- No source; 100 synthetic records per object
- Detail
- Tree under about 3,000 records; bulk for millions
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- Partial Copy and Full only
Sources for this section (7)
- Retirementhelp.salesforce.com (opens in a new tab)
- Data Mask and Seed release notehelp.salesforce.com (opens in a new tab)
- Classify Sensitive Datahelp.salesforce.com (opens in a new tab)
- Seedinghelp.salesforce.com (opens in a new tab)
- Seeding Templates and Jobstrailhead.salesforce.com (opens in a new tab)
- Work with Datadeveloper.salesforce.com (opens in a new tab)
- Sandbox Types and Templateshelp.salesforce.com (opens in a new tab)
- Why classify fields before building masking policies?
- When should a seeding job disable Salesforce automations?
- Which tests in a scratch org usually need no baseline data?
- Why can a Generate template feed an environment that must never hold production data?
The questions are free to read. Answers with their sources and self-grading are included with the pack.
Keep reading with the Platform Development Lifecycle and Deployment Architect pack
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The pack includes:
- All 26 lessons of the Platform Development Lifecycle and Deployment Architect guide, with reading progress
- Interactive decision walks and step-by-step sequence steppers
- Inline practice questions and one-click topic practice (13 questions for this lesson)
- An official source cited for every claim
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