Salesforce Certified Platform Data Architect Practice Exam

Salesforce Certified Platform Data Architect Practice Exam

• Data Modeling & Database Design
• Master Data Management
• Salesforce Data Management
• Data Governance
• Considerations for Large Data Volumes
• Data Migration

Who should take the exam?

• 2 to 3 years of Salesforce experience
• Over 5 years of experience supporting or implementing data-centric solutions

• Advanced Administrator
• Data Architect
• Technical/Solution Architect
• Advanced Platform Developer

Knowledge Requirement:

• Data modeling and Database Design
• Custom fields, master-detail relationships, lookup relationships
• Translating client requirements into database requirements
• Understanding the standard object structure for sales and service clouds
• Optimizing Salesforce standard and big objects
• Recognizing the association between standard objects and Salesforce license types
• Handling considerations for Large Data Volumes (LDV)
• Implementing indexing, LDV migrations, and optimizing performance
• Grasping Salesforce Platform declarative and programming concepts
• Scripting using tools such as Data Loader and ETL platforms
• Upholding Data Stewardship principles
• Possessing Data Quality Skills, emphasizing clean data practices

Exam Details

• Exam Name: Salesforce Certified Platform Data Architect
• Exam Languages: English
• Exam Questions: 60 Questions
• Time: 105 minutes
• Passing Score: 58%

Course Outline 

• Compare and contrast various techniques and considerations for designing a data model for the Customer 360 platform. (e.g. objects, fields & relationships, object features).
• 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 (e.g. business dictionary, data lineage, taxonomy, data classification).
• Compare and contrast the different reasons for implementing Big Objects vs Standard/Custom objects within a production instance, alongside the unique pros and cons of utilizing Big Objects in a Salesforce data model.
• Given a customer scenario, recommend approaches and techniques to avoid data skew (record locking, sharing calculation issues, and excessive child to parent relationships).

• Compare and contrast the various techniques, approaches and considerations for implementing Master Data Management Solutions (e.g. MDM implementation styles, harmonizing & consolidating data from multiple sources, establishing data survivorship rules, thresholds & weights, leveraging external reference data for enrichment, Canonical modeling techniques, hierarchy management.)
• 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. Discuss criteria and methodology for picking the winning attributes.
• Given a customer scenario, recommend appropriate approaches and techniques to capture and maintain customer reference & metadata to preserve traceability and establish a common context for business rules 

• Given a customer scenario, recommend appropriate combination of Salesforce license types to effectively leverage standard and custom objects to meet business needs.
• 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.

• Given a customer scenario, recommend an approach for designing a GDPR compliant data model. Discuss the various options to identify, classify and protect personal and sensitive information. 
• Compare and contrast various approaches and considerations for designing and implementing an enterprise data governance program. 

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

• 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 large data volumes into Salesforce.
• Compare and contrast various techniques and considerations for exporting data from Salesforce.