Question 31
Universal Containers (UC) uses Salesforce for tracking opportunities (Opportunity). UC uses an internal ERP system for tracking deliveries and invoicing. The ERP system supports SOAP API and OData for bi-directional integration between Salesforce and the ERP system. UC has about one million opportunities. For each opportunity, UC sends 12 invoices, one per month. UC sales reps have requirements to view current invoice status and invoice amount from the opportunity page. When creating an object to model invoices, what should the architect recommend, considering performance and data storage space?
Question 32
Universal Containers (UC) has implemented Sales Cloud and it has been noticed that Sales reps are not entering enough data to run insightful reports and dashboards. UC executives would like to monitor and measure data quality metrics. What solution addresses this requirement?
Question 33
Universal Containers has provided a web order form for its customer and has noticed invalid data coming in on orders.
What should be used to mitigate this problem?
What should be used to mitigate this problem?
Question 34
A customer needs a sales model that allows the following:
Opportunities need to be assigned to sales people based on the zip code.
Each sales person can be assigned to multiple zip codes.
Each zip code is assigned to a sales area definition. Sales is aggregated by sales area for reporting.
What should a data architect recommend?
Opportunities need to be assigned to sales people based on the zip code.
Each sales person can be assigned to multiple zip codes.
Each zip code is assigned to a sales area definition. Sales is aggregated by sales area for reporting.
What should a data architect recommend?
Question 35
Universal Containers (UC) has a Salesforce org with multiple automated processes defined for group membership processing, UC also has multiple admins on staff that perform manual adjustments to the role hierarchy. The automated tasks and manual tasks overlap daily, and UC is experiencing "lock errors" consistently.
What should a data architect recommend to mitigate these errors?
What should a data architect recommend to mitigate these errors?
