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| Section | Weight | Objectives |
|---|---|---|
| Master Data Management | 20% | - Identity resolution - Data cleansing techniques - Data duplication and matching rules |
| Data Architecture | 20% | - Archiving and data lifecycle management - Architecture patterns and design principles - Performance tuning and optimization - Governor limits and bulk processing |
| Data Integration | 15% | - Handling locks and parallelism during migration - ETL tools and data migration strategies - API-based integration (REST, SOAP, Bulk API) - Sharing rules impact on data integration - Real-time vs batch integration patterns |
| Data Governance | 20% | - Data retention and archiving policies - Data stewardship roles and responsibilities - Data quality standards and metrics - Compliance and regulatory considerations |
| Data Modeling | 25% | - Skinny tables for performance optimization - Indexing strategies - Large Data Volumes (LDV) design considerations - Normalization and denormalization strategies - Big Objects and external objects |
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NEW QUESTION # 74
Universal Containers (UC) has over 10 million accounts with an average of 20 opportunities with each account. A Sales Executive at UC needs to generate a daily report for all opportunities in a specific opportunity stage.
Which two key considerations should be made to make sure the performance of the report is not degraded due to large data volume?
Answer: C,D
Explanation:
The number of joins used in report query and the number of records returned by report query are two key considerations to make sure the performance of the report is not degraded due to large data volume. The number of joins used in report query affects the complexity and execution time of the query, especially when joining multiple large objects4. The number of records returned by report query affects the amount of data that needs to be processed and displayed by the report engine.
NEW QUESTION # 75
Universal Container (UC) has accumulated data over years and has never deleted data from its Salesforce org. UC is now exceeding the storage allocations in the org. UC is now looking for option to delete unused from the org.
Which three recommendations should a data architect make is order to reduce the number of records from the org?
Choose 3 answers
Answer: C,D,E
Explanation:
Using hard delete in Bulk API, identifying records that have not been modified or used in last 3 years, and archiving the records in EDW before deleting from Salesforce are all good recommendations to reduce the number of records from the org1. Using hard delete in batch Apex or Rest API may not be as efficient or scalable as Bulk API
NEW QUESTION # 76
UC has to built a B2C ecommerce site on Heroku that shares customer and order data with a Heroku Postgres database. UC is currently utilizing Postgres as the single source of truth for both customers and orders. UC has asked a data architect to replicate the data into salesforce so that salesforce can now act as the system of record.
What are the 3 considerations that data architect should weigh before implementing this requirement? Choose
23 answers:
Answer: B,C,E
Explanation:
Before replicating the data from Heroku Postgres to Salesforce, the data architect should consider the following factors:
Whether the data is a driver of key processes implemented within Salesforce. For example, if the data is used for workflows, triggers, or validation rules, it should be replicated to Salesforce.
Whether there is a tight relationship between order data and an enterprise resource planning (ERP) application. For example, if the order data needs to be synchronized with the ERP system, it should be replicated to Salesforce.
The selection of the tool required to replicate the data. For example, Heroku Connect can be used to bi-directionally sync data between Heroku Postgres and Salesforce
NEW QUESTION # 77
UC is migrating individual customers (B2C) data from legacy systems to SF. There are millions of customers stored as accounts and contacts in legacy database.
Which object model should a data architect configure within SF?
Answer: B
Explanation:
The best object model to configure within SF for migrating individual customers (B2C) data from legacy systems is to leverage person account object in Salesforce. Person accounts are a special type of accounts that store information about individual people by combining certain account and contact fields into a single record4. Person accounts are useful for B2C scenarios where there is no need to associate a company name with a contact. Person accounts also support standard Salesforce features and functionality, such as leads, campaigns, reports, dashboards, etc5. Leverage custom person account object in SF is not a good option because there is no such thing as a custom person account object. Leverage custom account and contact object in SF is not a good option because it would require creating and maintaining additional objects and fields that may not be necessary or compatible with standard Salesforce features. Leverage standard account and contact object in SF is not a good option because it would require filling in dummy values for the account name field, which is mandatory for standard accounts.
NEW QUESTION # 78
Northern Trail Outfitters (NTO) has implemented Salesforce for its sales users. The opportunity management in Saiesforce Is implemented as follows:
1. Sales users enter their opportunities in Salesforce for forecasting and reporting purposes.
2. NTO has a product pricing system (PPS) that is used to update the Opportunity Amount field on opportunities on a daily basis.
3. PPS is the trusted source within NTO for Opportunity Amount.
4. NTO uses Opportunity Forecast for its sales planning and management.
Sales users have noticed that their updates to the Opportunity Amount field are overwritten when PPS updates their opportunities.
How should a data architect address this overwriting issue?
Answer: A
Explanation:
Changing Opportunity Amount field access to Read Only for sales users field-level security (option C) is the best way to address the overwriting issue, as it prevents sales users from updating the field that is controlled by PPS, and ensures data consistency and accuracy. Creating a custom field for Opportunity amount that PSS updates separating the field sales user updates (option A) or creating a custom field for Opportunity amount that sales users update separating the field that PPS updates (option D) are not good solutions, as they may create confusion and inconsistency with the Opportunity Forecast feature. Changing PSS integration to update only Opportunity Amount field when the value is null (option B) is also not a good solution, as it may cause data loss or conflicts with the sales users' inputs.
NEW QUESTION # 79
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