Salesforce - Priscilla Hong

• Content: 60 multiple-choice/multiple-select questions and up to 5 non-scored questions
• Time allotted to complete the exam: 105 minutes
• Passing score: 58%
• Version: Exam questions align to the Spring '23 release
• Registration fee: US$400 plus applicable taxes as required per local law
• Retake fee: $200 plus applicable taxes as required per local law
• Delivery options: Proctored exam delivered onsite at a testing center or in an online proctored environment. Click here for information on scheduling an exam.
• References: No hard-copy or online materials may be referenced during the exam.
• Prerequisite: None

Exam Outline

Trailhead Exam Guide

Data Modeling/Database Design: 25%

• Compare and contrast various techniques and considerations for designing a data model for the Customer 360 Platform. (for example, 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 (for example, business dictionary, data lineage, taxonomy, data classification).
• 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.
• Given a customer scenario, recommend approaches and techniques to avoid data skew (record locking, sharing calculation issues, and excessive child to parent relationships).

Master Data Management: 5%

• Compare and contrast the various techniques, approaches and considerations for implementing master data management (MDM) Solutions (for example, MDM implementation styles; harmonizing and consolidating data from multiple sources; establishing data survivorship rules, thresholds and 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 and metadata to preserve traceability and establish a common context for business rules.

Salesforce Data Management: 25%

• 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.

Data Governance: 10%

• Given a customer scenario, recommend an approach for designing a General Data Protection Regulation (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.

Large Data Volume Considerations: 20%

• 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.

Data Migration: 15%

• 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.