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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Data Modeling | 25% | - Big Objects and external objects - Indexing strategies - Large Data Volumes (LDV) design considerations - Skinny tables for performance optimization - Normalization and denormalization strategies |
| Topic 2: Master Data Management | 20% | - Identity resolution - Data cleansing techniques - Data duplication and matching rules |
| Topic 3: Data Integration | 15% | - Real-time vs batch integration patterns - Handling locks and parallelism during migration - API-based integration (REST, SOAP, Bulk API) - ETL tools and data migration strategies - Sharing rules impact on data integration |
| Topic 4: Data Governance | 20% | - Data stewardship roles and responsibilities - Compliance and regulatory considerations - Data retention and archiving policies - Data quality standards and metrics |
| Topic 5: Data Architecture | 20% | - Architecture patterns and design principles - Performance tuning and optimization - Archiving and data lifecycle management - Governor limits and bulk processing |
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NEW QUESTION # 180
DreamHouse Realty has a Salesforce org that is used to manage Contacts.
What are two things an Architect should consider using to maintain data quality in this situation? (Choose two.)
Answer: A,C
Explanation:
Using Salesforce duplicate management and using validation rules on new record create and edit are two things that an architect should consider using to maintain data quality for managing Contacts. Salesforce duplicate management allows the architect to create matching rules and duplicate rules to identify, prevent, or allow duplicate records based on various criteria. Validation rules allow the architect to enforce data quality standards and business logic by displaying error messages when users try to save invalid data. The other options are not relevant or helpful for maintaining data quality
NEW QUESTION # 181
Universal Containers has multiple systems all containing and maintaining customer dat a. Although point-to-point integrations are in place, customers are complaining about consistency in the data.
What solution should the data architect recommend?
Answer: D
NEW QUESTION # 182
NTO processes orders from its website via an order management system (OMS). The OMS stores over 2 million historical records and is currently not integrated with SF. The Sales team at NTO using Sales cloud and would like visibility into related customer orders yet they do not want to persist millions of records directly in Salesforce. NTO has asked the data architect to evaluate SF connect and the concept of data verification. Which 3 considerations are needed prior to a SF Connect implementation?
Choose 3 answers:
Answer: A,B,E
NEW QUESTION # 183
Universal Containers (UC) owns a complex Salesforce org with many Apex classes, triggers, and automated processes that will modify records if available. UC has identified that, in its current development state, UC runs change of encountering race condition on the same record.
What should a data architect recommend to guarantee that records are not being updated at the same time?
Answer: D
Explanation:
According to the How to avoid row lock or race condition in Apex blog post, one of the ways to prevent race condition in Apex is to use the FOR UPDATE keyword in SOQL statements. The blog post states that "We need to lock the records on which we are working such that other batches or threads will not be having any effect on them. How can we lock a record, then? We need to make use of FOR UPDATE keyword in the SOQL query." Therefore, a data architect should recommend this solution to guarantee that records are not being updated at the same time by different processes.
NEW QUESTION # 184
Universal Containers (UC) has multi -level account hierarchies that represent departments within their major Accounts. Users are creating duplicate Contacts across multiple departments. UC wants to clean the data so as to have a single Contact across departments. What two solutions should UC implement to cleanse their data?
Choose 2 answers
Answer: B,D
Explanation:
Making use of a third-party tool to help merge duplicate Contacts across Accounts and using Data.com to standardize Contact address information to help identify duplicates are two solutions that UC should implement to cleanse their data and have a single Contact across departments. A third-party tool, such as an app from the AppExchange, can provide advanced features and capabilities for finding and merging duplicate Contacts across different Accounts, based on various criteria and rules. Data.com can provide address verification and standardization services that can enhance the quality and consistency of Contact address information and make it easier to identify duplicates. The other options are not feasible or effective for cleansing the data, as they would either not work across different Accounts, not address the root cause of duplication, or not provide sufficient functionality for merging duplicates
NEW QUESTION # 185
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