A casino is implementing Salesforce and is planning to build a customer 360 degree view for a customer who visits its resorts. The casino currently maintains the following systems that record customer activity: L Point-of-sale system: All purchases for a customer
2. Salesforce; All customer service activity and sales activities for a customer
3. Mobile app: All bookings, preferences, and browser activity for a customer
4. Marketing: All email, SMS, and social campaigns for a customer
Customer service agents using Salesforee would like to view the activities from all four systems to provide support to customers. The information has to be current and real time.
What strategy should the data architect implement to satisfy this requirement?

• A.Migrate customer activities fro, all four system into Salesforce.
• B.Periodically upload summary information in Salesforce to build a 360 degree view.
• C.Use a customer data mart to create the 360 degree view of the customer.
• D.Explore external data sources in Salesforce to build a 360 degree view of the customer.

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Universal Containers has a legacy system that captures Conferences and Venues. These Conferences can occur at any Venue. They create hundreds of thousands of Conferences per year. Historically, they have only used
20 Venues. Which two things should the data architect consider when de-normalizing this data model into a single Conference object with a Venue picklist? Choose 2 answers

• A.Bulk API limitations on picklist fields.
• B.Org data storage limitations.
• C.Limitations on master -detail relationships.
• D.Standard list view in-line editing.

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Universal Containers has millions of cases and are running out storage. Some user groups need to have access to historical cases for up to seven years.
Which two solutions should a data architect recommend in order to minimize performance and storage issues?
(Choose two.)

• A.Leverage big object to archive case data and Lightning components to show archived data
• B.Export data out of Salesforce and store in flat files on external system
• C.Create a custom object to store case history and run reports on it
• D.Leverage on-premise data archival and build integration to view archived data

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NTO has implemented salesforce for its sales users. The opportunity management in salesforce 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 opportunity amount field on opportunities on a daily basis.
3. PPS is the trusted source within the 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 overriding issue?

• A.Create a custom field for opportunity amount that PPS updates separating the field that sales user updates.
• B.Change opportunity amount field access to read only for sales users using field level security.
• C.Change PPS integration to update only opportunity amount fields when values is NULL.
• D.Create a custom field for opportunity amount that sales users update separating the fields that PPS updates.

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To address different compliance requirements, such as general data protection regulation (GDPR), personally identifiable information (PII), of health insurance Portability and Accountability Act (HIPPA) and others, a SF customer decided to categorize each data element in SF with the following:
Data owner
Security Level, such as confidential
Compliance types such as GDPR, PII, HIPPA
A compliance audit would require SF admins to generate reports to manage compliance.
What should a data architect recommend to address this requirement?

• A.Build reports for field information, then export the information to classify and report for Audits.
• B.Create a custom object and field to capture necessary compliance information and build custom reports.
• C.Use metadata API, to extract field attribute information and use the extract to classify and build reports
• D.Use field metadata attributes for compliance categorization, data owner, and data sensitivity level.

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