Neueste Salesforce Certified Platform Data Architect Prüfung pdf & Plat-Arch-201 Prüfung Torrent

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Salesforce Plat-Arch-201 Exam Syllabus Topics:

SectionWeightObjectives
Data Modeling/Database Design25%- Describe the considerations when designing a data model for Salesforce
- Describe the options and considerations for Large Data Volumes (LDV)
- Describe the implications of LDV on data modeling
- Explain the usage of external objects
Data Governance10%- Describe the considerations for data classification
- Describe the considerations for data quality
- Describe the elements of a data governance framework
Master Data Management5%- Describe the methods for aligning data across systems
- Describe the considerations for a Master Data Management strategy
Salesforce Data Management25%- Describe the considerations for data governance
- Describe the considerations for data migration
- Describe the implications of LDV on data management
- Describe the considerations for data archival
Large Data Volumes20%- Describe the considerations for Large Data Volumes (LDV)
- Describe the implications of LDV on data modeling
- Describe the features and best practices for LDV
Data Migration15%- Describe the tools and best practices for data migration
- Describe the considerations for data migration
- Describe the implications of data migration on data quality

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Plat-Arch-201 aktueller Test, Test VCE-Dumps für Salesforce Certified Platform Data Architect

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Salesforce Certified Platform Data Architect Plat-Arch-201 Prüfungsfragen mit Lösungen (Q250-Q255):

250. Frage
Universal Container (UC) has around 200,000 Customers (stored in Account object). They get 1 or 2 Orders every month from each Customer. Orders are stored in a custom object called "Order c"; this has about 50 fields. UC is expecting a growth of 10% year-over-year. What are two considerations an architect should consider to improve the performance of SOQL queries that retrieve data from the Order _c object? Choose 2 answer

Antwort: A,B

Begründung:
To improve the performance of SOQL queries that retrieve data from the Order_c object, the data architect should work with Salesforce Support to enable Skinny Tables and make the queries more selective using indexed fields. Skinny Tables are custom tables that contain frequently used fields and are kept in sync with the base tables. They can improve performance by reducing the number of table joins and using indexes. Making the queries more selective using indexed fields can also improve performance by reducing the query execution time and avoiding query timeouts.
The other options are not effective or recommended for improving SOQL performance.


251. Frage
Cloud Kicks currently has a Public Read/Write sharing model for the company's Contacts. Cloud Kicks management team requests that only the owner of a contact record be allowed to delete that contact. What should an Architect do to meet these requirements?

Antwort: C

Begründung:
Checking if the current user is NOT the owner by creating a "before delete" trigger can meet the requirement of allowing only the owner of a contact record to delete that contact. A trigger is a piece of Apex code that can execute before or after a record is inserted, updated, deleted, or undeleted. A "before delete" trigger can prevent the deletion of a record by using the addError() method.


252. Frage
What happens when a lookup relationship parent is deleted?

Antwort: C

Begründung:
Lookup relationships do NOT enforce cascade delete.


253. Frage
An architect has been asked by a client to develop a solution that will integrate data and resolve duplicates and discrepancies between Salesforce and one or more external systems. What two factors should the architect take into consideration when deciding whether or not to use a Master Data Management system to achieve this solution? Choose 2 answer

Antwort: A,B

Begründung:
Whether the system of record changes for different tables and the number of systems that are integrating with each other are two factors that the architect should take into consideration when deciding whether or not to use a Master Data Management system to achieve the solution of integrating data and resolving duplicates and discrepancies between Salesforce and one or more external systems. The system of record is the authoritative source of truth for a given entity or field in a given context. If different systems have different systems of record for different tables, then a Master Data Management system can help to manage and synchronize the data across systems and ensure data quality and consistency. The number of systems that are integrating with each other is another factor that affects the complexity and scalability of the integration solution. If there are many systems that need to integrate with each other, then a Master Data Management system can provide a centralized and standardized way of integrating data and resolving duplicates and discrepancies across systems. The other factors are not relevant or important for deciding whether or not to use a Master Data Management system, as they do not affect the data quality or integration challenges that a Master Data Management system can address.


254. Frage
Universal Containers (UC) is a major supplier of office supplies. Some products are produced by UC and some by other manufacturers. Recently, a number of customers have complained that product descriptions on the invoices do not match the descriptions in the online catalog and on some of the order confirmations (e.g., "ballpoint pen" in the catalog and "pen" on the invoice, and item color labels are inconsistent: "what vs. "White" or "blk" vs. "Black"). All product data is consolidated in the company data warehouse and pushed to Salesforce to generate quotes and invoices. The online catalog and webshop is a Salesforce Customer Community solution. What is a correct technique UC should use to solve the data inconsistency?

Antwort: B

Begründung:
A correct technique UC should use to solve the data inconsistency is to define a data taxonomy for product data and apply the taxonomy to the product data in the data warehouse. A data taxonomy is a hierarchical classification of data entities and attributes that defines their meaning, format, and relationships. A data taxonomy can help ensure consistency, accuracy, and completeness of product data across different systems and channels


255. Frage
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