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Salesforce Data-Architect Exam Syllabus Topics:

SectionWeightObjectives
Large Data Volume Considerations20%- Performance and scalability
  • 1. Avoiding and resolving data skew
  • 2. Governor limits and bulk processing
  • 3. Handling millions of records
- Storage and optimization
  • 1. Archiving and off-platform storage
  • 2. Performance tuning techniques
  • 3. Storage limits and management
Data Integration and Master Data Management10%- Master data management
  • 1. Data synchronization and consistency
  • 2. MDM strategy and implementation
  • 3. Single source of truth design
- Integration patterns and tools
  • 1. REST, SOAP, and Bulk API usage
  • 2. External data sources and objects
  • 3. Real-time vs batch integration
Data Modeling and Database Design25%- Indexing and data structure
  • 1. Standard and custom indexes
  • 2. Query optimization strategies
  • 3. Skinny tables and big objects
- Design scalable data models
  • 1. Custom and standard object design
  • 2. Object relationships and cardinality
  • 3. Data model optimization for performance
Data Governance and Compliance20%- Data lifecycle management
  • 1. Data classification and handling
  • 2. Data retention and archiving policies
  • 3. Data privacy and security
- Data governance framework
  • 1. Data stewardship and ownership
  • 2. Compliance and regulatory requirements
  • 3. Data quality standards
Salesforce Data Management25%- Sharing and visibility design
  • 1. Data access and security model
  • 2. Sharing rules, manual sharing, Apex sharing
  • 3. Organization-wide defaults, role hierarchy
- Data import and export strategies
  • 1. Data validation and cleansing
  • 2. Tools: Data Loader, Import Wizard, Bulk API
  • 3. Migration planning and execution

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Salesforce Certified Data Architect Sample Questions (Q111-Q116):

NEW QUESTION # 111
Northern Trail Outfitters (NTO) has the following systems:
Customer master-source of truth for customer information
Service cloud-customer support
Marketing cloud-marketing support
Enterprise data warehouse-business reporting
The customer data is duplicated across all these system and are not kept in sync. Customers are also complaining that they get repeated marketing emails and have to call into update their information.
NTO is planning to implement master data management (MDM) solution across the enterprise.
Which three data will an MDM tool solve?
Choose 3 answers

Answer: B,D,E


NEW QUESTION # 112
Universal Containers (UC) has 1,000 accounts and 50,000 opportunities. UC has an enterprise security requirement to export all sales data outside of Salesforce on a weekly basis. The security requirement also calls for exporting key operational data that includes events such as file downloads, logins, logouts, etc. Which two recommended approaches would address the above requirement?

Answer: C,D

Explanation:
Using Weekly Export to extract transactional data to on-premise systems and using Event Monitoring to extract event data to on-premise systems are two recommended approaches that would address the security requirement of UC. Weekly Export is a built-in feature that allows UC to download a zip file of their Salesforce data on a weekly basis. Event Monitoring is a tool that allows UC to access detailed information about various events that occur in their Salesforce org, such as logins, logouts, file downloads, etc.


NEW QUESTION # 113
UC has a variety of systems across its technology landscape, including SF, legacy enterprise resource planning (ERP) applications and homegrown CRM tools. UC has decided that they would like to consolidate all customer, opportunity and order data into Salesforce as part of its master data management (MDM) strategy.
What are the 3 key steps that a data architect should take when merging data from multiple systems into Salesforce? Choose 3 answers:

Answer: B,C,D

Explanation:
The three key steps that a data architect should take when merging data from multiple systems into Salesforce are:
Analyze each system's data model and perform gap analysis. This step involves understanding the structure and meaning of the data in each system, identifying the common and unique data elements, and mapping the data fields between the systems. This step also involves assessing the quality and consistency of the data, and identifying any data cleansing or transformation needs.
Utilize an ETL tool to merge, transform, and de-duplicate data. This step involves using an ETL tool to connect to the source systems, extract the data, apply any data transformations or validations, and load the data into Salesforce. This step also involves applying de-duplication rules or algorithms to avoid creating duplicate records in Salesforce.
Work with stakeholders to define record and field survivorship rules. This step involves collaborating with the business users and owners of the data to determine which records and fields should be retained or overwritten in case of conflicts or discrepancies. This step also involves defining the criteria and logic for record and field survivorship, and implementing them in the ETL tool or in Salesforce.
Creating new fields to store additional values from all the systems is not a key step, but rather a possible outcome of the gap analysis. It may not be necessary or desirable to create new fields for every value from every system, as it may result in redundant or irrelevant data. Installing a 3rd party AppExchange tool to handle the merger is not a key step, but rather a possible option for choosing an ETL tool. It may not be the best option depending on the requirements, budget, and preferences of the organization.


NEW QUESTION # 114
Northern Trail Outfitters (NTO) wants to capture a list of customers that have bought a particular product. The solution architect has recommended to create a custom object for product, and to create a lookup relationship between its customers and its products.
Products will be modeled as a custom object (NTO_ Product__ c) and customers are modeled as person accounts. Every NTO product may have millions of customers looking up a single product, resulting in a lookup skew.
What should a data architect suggest to mitigate Issues related to lookup skew?

Answer: B

Explanation:
creating multiple similar products and distributing the skew across those products can be a way to mitigate issues related to lookup skew. The article explains that lookup skew happens when a very large number of records are associated with a single record in the lookup object, and this can cause record locking and performance issues. The article suggests creating multiple copies of the same product record and assigning different child records to each copy, so that the number of child records per parent record is reduced.


NEW QUESTION # 115
UC has one SF org (Org A) and recently acquired a secondary company with its own Salesforce org (Org B). UC has decided to keep the orgs running separately but would like to bidirectionally share opportunities between the orgs in near-real time.
Which 3 options should a data architect recommend to share data between Org A and Org B?
Choose 3 answers.

Answer: B,D,E

Explanation:
Leveraging Heroku Connect and Heroku Postgres, middleware tools, or Salesforce Connect and the cross-org adapter are all viable options to share data between Org A and Org B in near-real time3. Installing a 3rd party AppExchange tool may not provide bidirectional sync or may have additional costs. Developing an Apex class that pushes opportunity data between orgs daily via the Apex schedule may not meet the near-real time requirement.


NEW QUESTION # 116
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