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Salesforce Data-Architect Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Data Modeling and Database Design	25%	- Design scalable data models 1. Object relationships and cardinality 2. Custom and standard object design 3. Data model optimization for performance - Indexing and data structure 1. Skinny tables and big objects 2. Standard and custom indexes 3. Query optimization strategies
Topic 2: Data Governance and Compliance	20%	- Data governance framework 1. Data stewardship and ownership 2. Compliance and regulatory requirements 3. Data quality standards - Data lifecycle management 1. Data privacy and security 2. Data classification and handling 3. Data retention and archiving policies
Topic 3: Data Integration and Master Data Management	10%	- Integration patterns and tools 1. Real-time vs batch integration 2. External data sources and objects 3. REST, SOAP, and Bulk API usage - Master data management 1. MDM strategy and implementation 2. Single source of truth design 3. Data synchronization and consistency
Topic 4: Salesforce Data Management	25%	- Sharing and visibility design 1. Sharing rules, manual sharing, Apex sharing 2. Data access and security model 3. Organization-wide defaults, role hierarchy - Data import and export strategies 1. Migration planning and execution 2. Tools: Data Loader, Import Wizard, Bulk API 3. Data validation and cleansing
Topic 5: Large Data Volume Considerations	20%	- Storage and optimization 1. Storage limits and management 2. Archiving and off-platform storage 3. Performance tuning techniques - Performance and scalability 1. Governor limits and bulk processing 2. Handling millions of records 3. Avoiding and resolving data skew

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Salesforce Certified Data Architect Sample Questions (Q51-Q56):

NEW QUESTION # 51
Universal Containers (UC) is a business that works directly with individual consumers (B2C). They are moving from a current home-grown CRM system to Salesforce. UC has about one million consumer records.
What should the architect recommend for optimal use of Salesforce functionality and also to avoid data loading issues?

A. Create one Account and load individual consumers as Contacts linked to that one Account.
B. Load all individual consumers as Account records and avoid using the Contact object.
C. Load one Account record and one Contact record for each individual consumer.
D. Create a Custom Object Individual Consumer c to load all individual consumers.

Answer: A

Explanation:
According to the exam guide, one of the objectives is to " describe best practices for implementing a single- org strategy in a B2C scenario " 1 . This implies that option D is the best practice for loading individual consumers as contacts in Salesforce. This approach avoids creating unnecessary accounts and reduces data duplication. Option C is not correct because it creates one account per contact, which increases data volume and complexity. Options A and B are not correct because they do not leverage the standard contact object, which provides native functionality and integration with other Salesforce features.

NEW QUESTION # 52
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:

A. Install a 3rd party AppExchange tool to handle the merger
B. Work with Stakeholders to define record and field survivorship rules
C. Analyze each system's data model and perform gap analysis
D. Utilize an ETL tool to merge, transform and de-duplicate data.
E. Create new fields to store additional values from all the systems.

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 # 53
What 2 data management policies does the data classification feature allow customers to classify in salesforce?
Choose 2 answers:

A. Data sensitivity level
B. Reference data policy.
C. Data governance policy.
D. Compliance categorization policy.

Answer: A,D

Explanation:
The data classification feature allows customers to classify their data in Salesforce based on two policies:
Data sensitivity level: This policy defines how sensitive the data is and what level of protection it requires. For example, high sensitivity data may require encryption or masking.
Compliance categorization policy: This policy defines how the data is regulated by various laws and standards. For example, GDPR or PCI DSS.

NEW QUESTION # 54
Universal Containers (UC) has implemented a master data management strategy, which uses a central system of truth, to ensure the entire company has the same customer information in all systems. UC customer data changes need to be accurate at all times in all of the systems. Salesforce is the identified system of record for this information.
What is the correct solution for ensuring all systems using customer data are kept up to date?

A. Have each system pull the record changes from Salesforce using change data capture.
B. Send customer data nightly to the system of truth in a scheduled batch job.
C. Send customer record changes from Salesforce to the system of truth in real time.
D. Send customer record changes from Salesforce to each system in a nightly batch job.

Answer: A

Explanation:
Having each system pull the record changes from Salesforce using change data capture (option D) is the correct solution for ensuring all systems using customer data are kept up to date, as it allows the systems to subscribe to real-time events from Salesforce and receive notifications when customer records are created, updated, deleted, or undeleted. Sending customer data nightly to the system of truth in a scheduled batch job (option A) or sending customer record changes from Salesforce to each system in a nightly batch job (option B) are not good solutions, as they may cause data latency and inconsistency, and they do not provide real-time updates. Sending customer record changes from Salesforce to the system of truth in real time (option C) is also not a good solution, as it does not address how the other systems will receive the updates from the system of truth.

NEW QUESTION # 55
UC is rolling out Sales App globally to bring sales teams together on one platform. UC expects millions of opportunities and accounts to be creates and is concerned about the performance of the application.
Which 3 recommendations should the data architect make to avoid the data skew? Choose 3 answers.

A. Use picklist fields rather than lookup to custom object.
B. Limit associating 10000 records looking up to same records.
C. Limit assigning one user 10000 records ownership.
D. Limit associating 10000 opportunities to one account.
E. Assign 10000 opportunities to one account.

Answer: B,C,D

Explanation:
Data skew occurs when a large number of child records are associated with a single parent record, or when a single user owns a large number of records. This can cause performance issues and lock contention. To avoid data skew, the data architect should limit assigning one user 10,000 records ownership, limit associating 10,000 opportunities to one account, and limit associating 10,000 records looking up to the same record

NEW QUESTION # 56
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