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SALESFORCE

Salesforce Certified Data Architect

DATA-ARCHITECTSalesforce Data Architect

The Salesforce Certified Data Architect certification validates your ability to design and implement scalable, secure, and high-performing data models on the Salesforce platform. It is for experienced architects who translate business requirements into robust data strategies, including data migration, integration, and governance. Earning it proves you can architect data solutions that drive business success.

Practice Free Questions

507 practice questions · Updated 2026-07-30

9Domains
21Objectives
144Concepts
507Questions

DATA-ARCHITECT Curriculum

Every domain, objective, and concept the DATA-ARCHITECT exam measures.

Compare and contrast various techniques and considerations for designing Lightning Platform data model (e.g., objects, fields & relationships, object features).

6 concepts · 15 questions
  1. Objects in Lightning Platform
  2. Fields and Data Types
  3. Relationships
  4. Object Features
  5. Data Modeling Techniques
  6. Considerations for Data Model Design

Given a customer scenario, design a data model that is scalable, supports all business processes with appropriate level of customizations (click vs. code), while considering performance for large data volumes.

4 concepts · 14 questions
  1. Scalable Data Model Design
  2. Business Process Support
  3. Customization Level (Click vs. Code)
  4. Performance for Large Data Volumes

Given a customer scenario, identify the issues impacting data quality along key dimensions (such as data duplication, completeness, accuracy, integrity, etc.).

6 concepts · 20 questions
  1. Data Quality Dimensions
  2. Data Duplication Analysis
  3. Data Completeness Assessment
  4. Data Accuracy Evaluation
  5. Data Integrity Verification
  6. Scenario-Based Issue Identification

Given a customer scenario, recommend approaches for improving data quality along key dimensions (e.g., data duplication, completeness, accuracy, integrity etc.) using various techniques (e.g., data assessment/profiling, cleansing, standardization/normalization, matching & merging, declarative components) in Salesforce (e.g., validation rules, visual indicators, dependent pick lists, etc.).

10 concepts · 30 questions
  1. Data Quality Dimensions
  2. Data Assessment and Profiling
  3. Data Cleansing Techniques
  4. Data Standardization and Normalization
  5. Matching and Merging Strategies
  6. Declarative Components for Data Quality
  7. Validation Rules
  8. Visual Indicators
  9. Dependent Picklists
  10. Improving Data Quality in Scenarios

Given a customer scenario, recommend appropriate techniques and tools to monitor data quality on an ongoing basis (e.g., KPIs, data quality reports, dashboards, metrics, third-party solutions).

6 concepts · 25 questions
  1. Data Quality KPIs
  2. Data Quality Reports
  3. Data Quality Dashboards
  4. Data Quality Metrics
  5. Third-Party Data Quality Tools
  6. Monitoring Strategy Selection

Given a customer scenario, recommend appropriate techniques and methods for ensuring high data quality at the point of entry, such as client/server side validation rules, workflows, approvals, data.com clean, duplicate prevention.

7 concepts · 22 questions
  1. Client-side validation rules
  2. Server-side validation rules
  3. Workflow rules for data quality
  4. Approval processes for data quality
  5. Data.com Clean
  6. Duplicate prevention techniques
  7. Selecting appropriate techniques

Given a customer scenario, recommend and use techniques for establishing a "golden source of truth"/"system of record" for the customer domain.

8 concepts · 32 questions
  1. Define Golden Source of Truth
  2. Identify Customer Data Sources
  3. Assess Data Quality Issues
  4. Select System of Record
  5. Design Data Governance Policies
  6. Implement Data Deduplication and Matching
  7. Establish Data Integration and Synchronization
  8. Monitor and Maintain Data Quality

Given a customer scenario, recommend approaches and techniques for consolidating data attributes from multiple sources. Discuss criteria and methodology for picking the winning attributes (data survivorship rules).

7 concepts · 27 questions
  1. Data Consolidation Approaches
  2. Source System Analysis
  3. Data Survivorship Rules Definition
  4. Survivorship Criteria Selection
  5. Conflict Resolution Techniques
  6. Data Governance Alignment
  7. Impact Assessment

Compare and contrast various techniques, approaches and considerations for capturing and managing business and technical metadata (e.g., business dictionary, data lineage, taxonomy, data classification).

8 concepts · 35 questions
  1. Business metadata vs technical metadata
  2. Business dictionary and glossary
  3. Data lineage
  4. Taxonomy
  5. Data classification
  6. Techniques for capturing metadata
  7. Approaches for managing metadata
  8. Considerations for metadata management

Given a customer scenario, recommend appropriate approaches and techniques to capture and maintain customer metadata to preserve traceability and establish a common context for business rules (e.g., applying weights to attributes used in matching process by their importance, data classification, global/local attributes, field audit trails, etc.).

8 concepts · 24 questions
  1. Metadata capture approaches
  2. Metadata maintenance strategies
  3. Traceability preservation
  4. Common context for business rules
  5. Attribute weighting in matching
  6. Data classification
  7. Global vs local attributes
  8. Field audit trails

Given a customer scenario, recommend a data archiving and purging plan that is optimal for customer's data storage management needs (e.g., large data volumes, data storage governor limits, data archiving, restore options, linking archived data to core CRM, on-platform vs off-platform options, AppExchange enablers).

8 concepts · 28 questions
  1. Identify data storage limits and large data volume triggers
  2. Differentiate archiving vs purging
  3. Evaluate on-platform archiving options
  4. Evaluate off-platform archiving options
  5. Design restore and access strategies
  6. Link archived data to core CRM records
  7. Leverage AppExchange archiving solutions
  8. Create a data archiving and purging plan

Compare and contrast various approaches and considerations for designing and implementing an enterprise data governance program while taking into account framework for defining roles and responsibilities (e.g., stewardship, data custodian, etc.), policies and standards, ownership and accountability, data rules and definitions, monitoring, and measurement.

8 concepts · 28 questions
  1. Enterprise Data Governance Program Approaches
  2. Data Governance Implementation Considerations
  3. Data Governance Framework for Roles and Responsibilities
  4. Data Stewardship vs. Data Custodianship
  5. Data Ownership and Accountability
  6. Data Policies and Standards
  7. Data Rules and Definitions
  8. Data Governance Monitoring and Measurement

Given a customer scenario, recommend a data governance model in terms of roles and responsibilities, processes for establishing data standards, metrics and KPIs, classification of attributes by usage, identifying and prioritizing attributes to be used in match and merge, setting attribute scores and weights.

6 concepts · 25 questions
  1. Data Governance Roles and Responsibilities
  2. Data Governance Processes for Standards
  3. Data Governance Metrics and KPIs
  4. Classification of Attributes by Usage
  5. Identifying Attributes for Match and Merge
  6. Setting Attribute Scores and Weights

Compare and contrast approaches and techniques for creating analytical reports and dashboards, discuss Salesforce offerings such as Wave, options available on AppExchange for exposing data quality metrics, adoption metrics, etc.

7 concepts · 18 questions
  1. Analytical report vs. dashboard
  2. Report and dashboard techniques
  3. Salesforce reporting and analytics offerings
  4. CRM Analytics (Wave) features
  5. AppExchange solutions for data quality metrics
  6. AppExchange solutions for adoption metrics
  7. Choosing between native and AppExchange tools

Given a customer scenario, recommend approaches for leveraging Salesforce (and partner) analytics offerings to enhance/optimize customer's enterprise analytics capabilities as well as recommending techniques to maintain the desired performance level (SOQL queries, reports) given customer's large data volumes.

7 concepts · 20 questions
  1. Salesforce Analytics Offerings Overview
  2. Requirements Analysis for Analytics Solutions
  3. Architecture Design for Enterprise Analytics
  4. Performance Optimization for SOQL Queries
  5. Report and Dashboard Performance Tuning
  6. Data Volume Management Strategies
  7. Monitoring and Troubleshooting Performance

Compare and contrast various techniques and considerations for importing data into and exporting data out of Salesforce (e.g., serial load, parallel load, deferred sharing, managing locks, handling hierarchical relationships, Bulk load API limits).

8 concepts · 24 questions
  1. Serial load
  2. Parallel load
  3. Deferred sharing
  4. Managing locks
  5. Handling hierarchical relationships
  6. Bulk API limits
  7. Export techniques
  8. Import vs export considerations

Given a customer scenario, recommend an optimal data migration plan taking into account parallelism, managing locks, and handling sharing rules.

4 concepts · 17 questions
  1. Parallelism in Data Migration
  2. Managing Locks During Migration
  3. Handling Sharing Rules in Migration
  4. Migration Plan Design

Compare and contrast various techniques for improving performance when migrating large data volumes into Salesforce, generating reports and dashboards, and querying large data sets in Salesforce.

3 concepts · 16 questions
  1. Data Migration Performance Techniques
  2. Report and Dashboard Performance Optimization
  3. Query Performance Optimization for Large Data Sets
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Practice Free Questions

Percentages reflect share of the current practice bank, not official exam weightings — no structured per-skill weight is published for DATA-ARCHITECT, so none is invented.