Salesforce Data Architect practice exam

Prepare for Your Salesforce Data Architect Certification with Challenging Mock Exams and Detailed Explanations.

Ace Your Salesforce Certified Integration Architect Exam - On Your First Try!

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With our up-to-date, verified practice questions and detailed explanations, you will gain the confidence and skills to pass with ease.

Here is what you will get:

• No duplicates — fresh, relevant, Salesforce-release-ready questions.

• Real exam simulation — so you walk into the test feeling prepared, not surprised.

• Full coverage of every certification section.

• Instant scoring & feedback — see your strengths and target your weak spots.

• In-depth explanations with Salesforce documentation links for deeper learning.

Why choose this practice test?
Because it is not just about answering questions - it is about understanding the "why" behind the right answers, so you can tackle even the trickiest scenarios with confidence.

Do not wait until test day to find out you were not ready.

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Salesforce Data Architect exam sections

Below are all the sections, which will be tested during the exam.

A candidate should have a hands-on experience and deep understanding of the following areas.

Data modeling/Database Design: 25%

• 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).

Master Data Management: 5%

• 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.

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 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 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 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 large data volumes into Salesforce.

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