UC is planning a massive SF implementation with large volumes of data. As part of the org's implementation, several roles, territories, groups, and sharing rules have been configured. The data architect has been tasked with loading all of the required data, including user data, in a timely manner.
What should a data architect do to minimize data load times due to system calculations?

• A.Leverage the Bulk API and concurrent processing with multiple batches
• B.Enable defer sharing calculations, and suspend sharing rule calculations
• C.Enable granular locking to avoid "UNABLE _TO_LOCK_ROW" error.
• D.Load the data through data loader, and turn on parallel processing.

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Cloud Kicks has a Salesforce instance with 12,000 Account records. Managers at the company have noticed similar, but not identical, Account names and addresses.
The Chief Technology Officer (CTO) at Cloud Kicks is concerned about proper data quality.
Which steps should the CTO take to address this issue?

• A.1. Run a report.
  2. Find Accounts whose name starts with the same five characters, and merge those Accounts.
• B.1. Use a service to standardize Account addresses.
  2. Use a 3rd-party tool to merge Accounts based on rules.
• C.1. Have the Account Owner clean their Accounts' addresses.
  2. Merge Accounts with the same address.
• D.1. Enable Account de-duplication by creating matching rules in Salesforce.
  2. The system will then mass merge duplicate Accounts.

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UC is migrating data from legacy system to SF. UC would like to preserve the following information on records being migrated:
Date time stamps for created date and last modified date.
Ownership of records belonging to inactive users being migrated to Salesforce.
Which 2 solutions should a data architect recommends to preserve the date timestamps and ownership on records? Choose 2 answers.

• A.Enable update records with Inactive Owners Permission
• B.Enable modify all and view all permission.
• C.Enable Set Audit fields upon Record Creation Permission
• D.Log a case with SF to update these fields

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A custom pricing engine for a Salesforce customer has to be decided by factors with the following hierarchy:
1. State in which the customer is located
2. City in which the customer is located if available
3. Zip code in which the customer is located if available
4. Changes to this information should have minimum code change
What should a data architect recommend to maintain this information for the custom pricing engine that is to be built in Salesforce?

• A.Assign the pricing criteria within custom pricing engine.
• B.Create a custom object to maintain the pricing criteria.
• C.Configure the pricing criteria in price books.
• D.Maintain required pricing criteria in custom metadata types.

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Universal Containers (UC) has several custom Visualforce applications have been developed in which users are able to edit Opportunity records. UC struggles with data completeness on their Opportunity records and has decided to make certain fields required that have not been in the past. The newly required fields are dependent on the Stage of the Opportunity, such that certain fields are only required once an Opportunity advances to later stages. There are two fields. What is the simplest approach to handle this new requirement?

• A.Update these Opportunity field definitions in Setup to be required.
• B.Write an Apex trigger that checks each field when records are saved.
• C.Use a validation rule for each field that takes the Stage into consideration.
• D.Update the Opportunity page layout to mark these fields as required.

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