How much Data-Architecture-And-Management-Designer Exam Cost

The price of the Salesforce Data-Architecture-And-Management-Designer exam is $400 USD and exam retake fee is $200 USD.

Reference: https://trailhead.salesforce.com/en/credentials/dataarchitectureandmanagementdesigner#

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Data-Architecture-And-Management-Designer Exam topics

Candidates must know the exam topics before they start of preparation. Because it will really help them in hitting the core. Our Salesforce Data-Architecture-And-Management-Designer exam dumps will include the following topics:

1. Data Modeling/ Database Design 20%

• 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 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).
• 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 various techniques and considerations for designing a data model for the Customer 360 platform. (e.g. objects, fields & relationships, object features).

2. Master Data Management: 5%

• 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
• 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 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

3. Salesforce Data Management: 25%

• 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 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 customer scenario, recommend a design to effectively consolidate and/or leverage data from multiple Salesforce instances.

4. Data Governance: 10%

• 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.

5. Large Data Volume considerations: 20%

• 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, design a data model that scales considering large data volume and solution performance.

6. Data Migration: 15%

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

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