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Storing the data in a staging database and de-duplicating identical records (option D) is the best solution for a successful migration to mitigate the pain point, as it allows the data architect to identify and merge duplicate customers before they are imported into Salesforce. Hiring an intern manually de-duplicate the records after migrating to Salesforce (option A) is not a good solution, as it may be time-consuming and error-prone, and it does not prevent duplicate records from being created in Salesforce. Migrating the data as is and using Salesforce’s de-duplicating feature (option B) is also not a good solution, as it may cause data quality issues and conflicts, and it does not address the root cause of the duplication. Having the users manually clean the data in the old system prior to migration (option C) is also not a good solution, as it may be unrealistic and impractical, and it does not leverage any automated tools or processes.

Implementing a workflow rule that sends Opportunity data through Outbound Messaging is the optimal real-time approach that achieves the solution of copying Opportunity records to the ERP once they reach a Closed/Won Stage. A workflow rule can trigger an Outbound Message when an Opportunity record meets certain criteria, such as having a Closed/Won Stage. An Outbound Message can send data from Salesforce to an external system via SOAP API, without requiring any code. The external system can then process the data and create a read-only record in the ERP system. The other options are not optimal or real-time, as they would either require additional systems or tools, not provide real-time data synchronization, or not meet the frequency requirement

The correct answer is B, add custom indexes on frequently searched account and contact object fields. Custom indexes are a way to improve the performance of your queries and reports by creating indexes on specific fields that are often used in filters or joins. By adding custom indexes on frequently searched account and contact object fields, you can speed up the response time for reports and list views. Loading only the data that users are permitted to access, limiting data loading to the 2000 most recent records, or creating a skinny table are also possible solutions, but they are either not feasible, not scalable, or not supported by Salesforce.

According to the Salesforce documentation1, 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 rules 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.

Leveraging Apex to validate the format of data being entered via a mobile device (option B) is not a good solution, as it can be complex, costly, and difficult to maintain. It is better to use standard features or declarative tools that can handle data validation more effectively. Leveraging third-party AppExchange tools (option D) is also not a good solution, as it can incur additional costs and dependencies. It is better to use native Salesforce features or custom solutions that can handle data quality more efficiently.

 According to this article, big objects can store and manage massive amounts of data on the Salesforce platform. This can help UC to archive records from other objects or bring massive datasets from outside systems into a big object. According to this article, Einstein Analytics can be used to create dashboards and lenses to analyze big object data and get insights from advanced AI-driven analytics.

Utilizing a Validation Rule with a REGEX operator on Postal Code and utilizing a Validation Rule with a CONTAINS operator on address fields are two actions that Cloud Kicks should take to improve data quality for their Invoice records. A Validation Rule with a REGEX operator can check if the Postal Code field matches a specific pattern or format, such as a five-digit number or a combination of letters and numbers. A Validation Rule with a CONTAINS operator can check if the address fields contain certain values, such as valid state abbreviations or country names. These Validation Rules can prevent users from saving invalid or incomplete data and display error messages to guide them to correct the data. The other options are not effective or recommended for improving data quality, as they would either require additional customization, not enforce data standards, or not address the specific issues of missing states and incorrectly formatted postal codes

  IP restrictions can be used to prevent users from accessing Salesforce from outside the company network or VPN. According to this article, sharing settings can be used to control access to customer information based on record ownership. According to this article, custom Apex can be used to support customer requests for removal of their information from Salesforce.

Using a declarative approach by installing and configuring Data.com Clean to monitor Account data quality can help UC meet their requirement. Data.com Clean can enrich and cleanse data on an ongoing basis by comparing Salesforce records with Data.com records and providing suggestions for updates. Data.com Clean can also provide dashboards and reports to monitor key data quality metrics such as match rate, field fill rate, and record completeness

The best design pattern to satisfy the requirement of accessing order data from an on-premise database with REST API access without consuming SF storage limits is to use SF Connect to virtualize the data in SF and avoid storage limits. SF Connect is an integration tool that allows users to access and integrate data from external sources using external objects. External objects are similar to custom objects, except that the data resides in another system and is accessed in real time via web service callouts. SF Connect supports various adapters to connect to different types of external data sources, such as OData, cross-org, or Apex custom adapter11. Migrate and persist the data in SF to take advantage of native functionality is not a good option because it would consume SF storage limits and require data synchronization between systems. Develop a bidirectional integration between the on-premise system and Salesforce is not a good option because it would be complex and costly to implement and maintain, and it would also consume SF storage limits. Build a UI for the on-premise system and iframe it in Salesforce is not a good option because it would not provide a seamless user experience and it would not allow users to search, report, or perform actions on the external data.

Option B is correct because putting the business purpose in the Description of each field is a method that an architect can use to trace back configuration changes to the detailed requirements1. The Description of each field provides a brief explanation of what the field is used for and why it is needed2. Option D is correct because using the Force.com IDE to save the metadata files in source control is another method that an architect can use to trace back configuration changes to the detailed requirements1. The Force.com IDE is an integrated development environment that allows developers to work with Salesforce metadata files and Apex code3. Source control is a system that tracks and manages changes to code and configuration files4. Option A is not correct because reviewing the setup audit trail for configuration changes is not a method to trace back configuration changes to the detailed requirements, but a way to monitor and audit the changes made in the setup area. Option C is not correct because maintaining a data dictionary with the justification for each field is not a method to trace back configuration changes to the detailed requirements, but a document that provides information about the data entities and attributes in a system.