Given a Customer Scenario, Recommend Approaches for Improving Data Quality Along Key Dimensions (e.g., Data Duplication, Completeness, Accuracy, Integrity Etc.) Using Various Techniques (e.g., Data Assessment/profiling, Cleansing, Standardization/normalization, Matching & Merging, Declarative Components) in Salesforce (e.g., Validation Rules, Visual Indicators, Dependent Pick Lists, Etc.). DATA-ARCHITECT Practice Questions (Page 1)

Questions 1–5

1. 1application · medium

   A multinational company uses Salesforce to manage customer data. They have noticed that phone numbers are stored in various formats (e.g., '+1 (555) 123-4567', '555.123.4567', '5551234567'). They want to standardize the phone number format to a single international format. Which approach should the architect recommend?

   • AUse a workflow rule to replace all phone numbers with a default value.
   • BCreate a validation rule that requires phone numbers to be in the format '+1 (555) 123-4567'.
   • CCreate a formula field that displays the phone number in a standardized format, but keep the original field unchanged.
   • DUse a data cleansing tool to normalize phone numbers to the E.164 format during a data import or update.
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2. 2foundation · easy

   Which declarative component is best suited to visually alert users to a data quality issue on a record, such as a missing critical field?

   • AVisual indicator
   • BValidation rule
   • CDependent picklist
   • DWorkflow rule
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4. 3application · medium

   A travel agency uses Salesforce to manage bookings. They want to ensure that when a booking is for a 'Cruise', the 'Cabin Type' field is required and only shows options like 'Interior', 'Ocean View', and 'Balcony'. Which declarative solution should the architect recommend?

   • ACreate a workflow rule that automatically sets 'Cabin Type' to 'Interior' when the booking type is 'Cruise'.
   • BCreate a dependent picklist on the Booking object where 'Cabin Type' is a dependent picklist of 'Booking Type', and add a validation rule that requires 'Cabin Type' when 'Booking Type' equals 'Cruise'.
   • CCreate a validation rule that requires 'Cabin Type' to be populated when 'Booking Type' equals 'Cruise', but do not restrict the available options.
   • DCreate a formula field that displays the valid cabin types based on the booking type.
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5. 4expert · hard

   A financial services company has two Salesforce orgs that are being merged into one. They have identified duplicate Account records across the orgs. The data architect needs to merge these duplicates while preserving the most accurate data and maintaining referential integrity to child records (e.g., Contacts, Opportunities). Which approach should the architect recommend?

   • ACreate a validation rule that prevents duplicate Account names, and then ask users to manually delete the duplicates.
   • BExport all duplicate Accounts to a CSV file, manually combine the data, and then re-import the consolidated records, deleting the originals.
   • CUse the standard Salesforce merge tool to merge duplicate Accounts, selecting the master record that has the most complete data, and review the merge preview for child record conflicts.
   • DUse a data cleansing tool to standardize Account names, and then rely on the duplicate reporting to identify potential matches, but do not merge them.
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6. 5application · medium

   A global manufacturing company has been importing Account records from multiple regional ERP systems. The same customer may appear with different names (e.g., 'Acme Corp', 'ACME Corporation', 'Acme Co.') and different address formats (e.g., '123 Main St' vs '123 Main Street'). The data architect needs to identify the extent of the problem before deciding on a cleanup approach. Which two-step approach should the architect recommend?

   • ARun a data profiling report to quantify duplicates and format inconsistencies, then standardize name and address fields using a cleansing tool or Data Import Wizard.
   • BCreate a custom formula field that displays a warning when the Account name contains common abbreviations, then ask users to manually correct records.
   • CUse Data Assessment to generate a report of all records with missing fields, then delete those records to improve data quality.
   • DImmediately merge all Accounts that appear to be duplicates using the standard merge tool, then create validation rules to prevent future duplicates.
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