Exam Data-Architect Preview - Reliable Data-Architect Exam Guide

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Salesforce Data-Architect Exam Syllabus Topics:

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

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Salesforce Certified Data Architect Sample Questions (Q104-Q109):

NEW QUESTION # 104
Which two best practices should be followed when using SOSL for searching?

Answer: B,D


NEW QUESTION # 105
UC needs to load a large volume of leads into salesforce on a weekly basis. During this process the validation rules are disabled.
What should a data architect recommend to ensure data quality is maintained in salesforce.

Answer: A


NEW QUESTION # 106
Northern Trail Outfitters (NTO) has a variety of customers that include householder, businesses, and individuals.
The following conditions exist within its system:
NTO has a total of five million customers.
Duplicate records exist, which is replicated across many systems, including Salesforce.
Given these conditions, there is a lack of consistent presentation and clear identification of a customer record.
Which three option should a data architect perform to resolve the issues with the customer data?

Answer: A,D,E


NEW QUESTION # 107
What 2 data management policies does the data classification feature allow customers to classify in salesforce? Choose 2 answers:

Answer: A,B


NEW QUESTION # 108
Universal Containers (CU) is in the process of implementing an enterprise data warehouse (EDW). UC needs to extract 100 million records from Salesforce for migration to the EDW.
What data extraction strategy should a data architect use for maximum performance?

Answer: D

Explanation:
According to the Salesforce documentation2, extracting large amounts of data from Salesforce can be challenging and time-consuming, as it can encounter performance issues, API limits, timeouts, etc. To extract 100 million records from Salesforce for migration to an enterprise data warehouse (EDW), a data extraction strategy that can provide maximum performance is:
Utilize PK Chunking with the Bulk API (option C). This means using a feature that allows splitting a large query into smaller batches based on the record IDs (primary keys) of the queried object. This can improve performance and avoid timeouts by processing each batch asynchronously and in parallel using the Bulk API3.
Installing a third-party AppExchange tool (option A) is not a good solution, as it can incur additional costs and dependencies. It may also not be able to handle such a large volume of data efficiently. Calling the REST API in successive queries (option B) is also not a good solution, as it can encounter API limits and performance issues when querying such a large volume of data. Using the Bulk API in parallel mode (option D) is also not a good solution, as it can still cause timeouts and errors when querying such a large volume of data without chunking.


NEW QUESTION # 109
......

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