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| Section | Objectives |
|---|---|
| Data Management | - Data governance principles - Master data management (MDM) concepts - Data quality management |
| Data Architecture and Design | - Conceptual and logical data modeling - Handling large data volume and scalability considerations - Data modeling best practices in Salesforce |
| Salesforce Data Model and Platform Capabilities | - Standard and custom objects - Relationships and schema design - Reporting and analytics considerations |
| Data Migration | - Data cleansing and transformation - Data migration strategies and planning |
| Data Integration | - ETL vs ELT strategies - API-based integration approaches - Integration patterns in Salesforce |
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NEW QUESTION # 99
Universal Container require all customers to provide either a phone number of an email address when registering for an account. What should the data architect use to ensure this requirement is met?
Answer: D
Explanation:
Using a validation rule is the best way to ensure that all customers provide either a phone number or an email address when registering for an account, as it allows defining a formula that evaluates whether a record meets certain criteria. Using required fields may not work if both phone number and email address are required. Using an Apex class or a Process Builder may be more complex or cumbersome than using a validation rule.
NEW QUESTION # 100
What makes Skinny tables fast? Choose three answer
Answer: A,D,E
Explanation:
Skinny tables are custom tables that contain frequently used fields from a standard or custom object. They are used to improve performance by reducing the number of database joins required for queries. Skinny tables have the following characteristics:
They do not include soft-deleted records, which means they only contain active records and save space.
They avoid resource intensive joins by storing data from multiple objects in one table, which reduces the query time and complexity.
Their tables are kept in sync with their source tables when the source tables are modified, which ensures data consistency and accuracy.
NEW QUESTION # 101
Which API is best for loading millions of records asynchronously?
Answer: B
Explanation:
Bulk API is optimized for large-scale data loads.
NEW QUESTION # 102
Universal Containers has defined a new Data Quality Plan for their Salesforce data and wants to know how they can enforce it throughout the organization. Which two approaches should an architect recommend to enforce this new plan? Choose 2 answer
Answer: B,C
Explanation:
Scheduling a weekly dashboard displaying records that are missing information to be sent to managers for review and using Workflow, Validation Rules, and Force.com code (Apex) to enforce critical business processes are two approaches that an architect should recommend to enforce the new Data Quality Plan for UC's Salesforce data. Scheduling a weekly dashboard can provide a regular and visual way of monitoring the data quality and identifying any gaps or issues that need to be addressed by the managers or users. Using Workflow, Validation Rules, and Apex can provide various ways of enforcing data quality standards and business logic by automating actions, displaying error messages, or executing custom code when users create or edit records. The other options are not suitable or helpful for enforcing the Data Quality Plan, as they would either not provide real-time feedback, not prevent data quality issues, or not leverage the capabilities of Salesforce
NEW QUESTION # 103
Universal Containers (UC) is migrating from a legacy system to Salesforce CRM, UC is concerned about the quality of data being entered by users and through external integrations.
Which two solutions should a data architect recommend to mitigate data quality issues?
Answer: C,D
Explanation:
According to the Salesforce documentation, 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 mitigate data quality issues, some of the recommended solutions are:
Leverage picklist and lookup fields where possible (option A). This means using fields that restrict the values or references that can be entered by the users or integrations. This can help reduce data entry errors, enforce data consistency, and improve data accuracy.
Leverage validation rules and workflows (option C). This means using features that allow defining ules 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.
NEW QUESTION # 104
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