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Salesforce Data-Architect Exam Syllabus Topics:

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

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更新したData-Architect受験料 & 資格試験のリーダー & 最新Data-Architect: Salesforce Certified Data Architect

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Salesforce Certified Data Architect 認定 Data-Architect 試験問題 (Q154-Q159):

質問 # 154
Universal Containers keeps its Account data in Salesforce and its Invoice data in a third -party ERP system. They have connected the Invoice data through a Salesforce external object. They want data from both Accounts and Invoices visible in one report in one place. What two approaches should an architect suggest for achieving this solution? Choose 2 answers

正解:B、C


質問 # 155
Universal container (UC) would like to build a Human resources application on Salesforce to manage employee details, payroll, and hiring efforts. To adequately and store the relevant data, the application will need to leverage 45 custom objects. In addition to this, UC expects roughly 20,00 API calls into Salesfoce from an n-premises application daily.
Which license type should a data architect recommend that best fits these requirements?

正解:A


質問 # 156
Cloud Kicks is launching a Partner Community, which will allow users to register shipment requests that are then processed by Cloud Kicks employees. Shipment requests contain header information, and then a list of no more than 5 items being shipped.
First, Cloud Kicks will introduce its community to 6,000 customers in North America, and then to 24,000 customers worldwide within the next two years. Cloud Kicks expects 12 shipment requests per week per customer, on average, and wants customers to be able to view up to three years of shipment requests and use Salesforce reports.
What is the recommended solution for the Cloud Kicks Data Architect to address the requirements?

正解:A

解説:
The recommended solution for the Cloud Kicks Data Architect to address the requirements is to create a custom object to track shipment requests and a child custom object to track shipment items. Implement an archiving process that moves data off-platform after three years. This solution would allow Cloud Kicks to store and manage their shipment data on Salesforce, and use Salesforce reports to analyze it. However, since Cloud Kicks expects a large volume of data over time, they should implement an archiving process that moves data off-platform after three years to avoid hitting the Org data storage limit and maintain optimal performance3. External objects are not a good option for this scenario, because they are stored off-platform in an external system, such as Heroku's Postgres database, and they have limited functionality and performance compared to custom objects


質問 # 157
Universal Containers has two systems. Salesforce and an on -premise ERP system. An architect has been tasked with copying Opportunity records to the ERP once they reach a Closed/Won Stage. The Opportunity record in the ERP system will be read-only for all fields copied in from Salesforce. What is the optimal real-time approach that achieves this solution?

正解:D

解説:
Implementing a workflow rule that sends Opportunity data through Outbound Messaging is the optimal real-time approach that achieves the solution of copying Opportunity records to the ERP once they reach a Closed/Won Stage. A workflow rule can trigger an Outbound Message when an Opportunity record meets certain criteria, such as having a Closed/Won Stage. An Outbound Message can send data from Salesforce to an external system via SOAP API, without requiring any code. The external system can then process the data and create a read-only record in the ERP system. The other options are not optimal or real-time, as they would either require additional systems or tools, not provide real-time data synchronization, or not meet the frequency requirement


質問 # 158
Northern Trail Outfitter has implemented Salesforce for its associates nationwide, Senior management is concerned that the executive dashboard is not reliable for their real-time decision-making. On analysis, the team the following issues with data entered in Salesforce.
Information in certain records is incomplete.
Incorrect entry in certain fields causes records to be excluded in report fitters.
Duplicate entries cause incorrect counts.
Which three steps should a data architect recommend to address the issues?

正解:A、B、E

解説:
According to the Salesforce documentation3, data quality is the measure of how well the data in Salesforce meets the expectations and requirements of the users and stakeholders. Data quality can be affected by various factors, such as data entry errors, data duplication, data inconsistency, data incompleteness, data timeliness, etc. To address the issues with data quality that affect the reliability of executive dashboards, a data architect should recommend:
Building a sales data warehouse with purpose-built data marts for dashboards and senior management reporting (option B). This means creating a separate database or system that stores and organizes sales data from Salesforce and other sources for analytical purposes. A data warehouse can provide a single source of truth for sales data and enable faster and more accurate reporting and analysis. A data mart is a subset of a data warehouse that focuses on a specific subject or business area, such as sales performance, customer segmentation, product profitability, etc. A data mart can provide tailored and relevant data for different users or groups based on their needs and interests.
Exploring third-party data providers to enrich and augment information entered in Salesforce (option C). This means using external services or tools that can validate, correct, update, and enhance the data that is entered or imported into Salesforce. This can help improve data quality and accuracy, and reduce data duplication and incompleteness.
Leveraging Salesforce features, such as validation rules, to avoid incomplete and incorrect records (option D). This means using features that allow defining rules and criteria to validate the data that is entered or updated by the users or integrations. This can help prevent invalid or incorrect data from being saved, and trigger actions or alerts to correct or improve the data.
Periodically exporting data to cleanse data and import them back into Salesforce for executive reports (option A) is not a good solution, as it can be time-consuming, error-prone, and inefficient. It may also cause data inconsistency and synchronization issues between Salesforce and other systems. Designing and implementing data-quality dashboard to monitor and act on records that are incomplete or incorrect (option E) is also not a good solution, as it can be complex, costly, and difficult to maintain. It may also not address the root causes of data quality issues or prevent them from occurring in the first place.


質問 # 159
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