According to the Salesforce documentation, an opportunity is a standard object that represents a potential sale or deal with an account or contact. An opportunity can have products and prices associated with it using price books. A price book is a standard object that contains a list of products and their prices for different regions, currencies, segments, etc. A price book can be shared with different users or groups based on their visibility and access settings.
To model the requirement of implementing a distributor management application in Salesforce, where distributors create sales orders based on product prices applicable to their region, and sales orders are closed once they are fulfilled, a data architect should:
Configure price books for each region and share with distributors (option B). This means creating different price books for different regions with the appropriate products and prices, and sharing them with the distributors who belong to those regions. This way, distributors can create sales orders (opportunities) using the price books that are relevant to their region.
Creating a lookup to Custom Price object and sharing with distributors (option A) is not a good solution, as it can introduce unnecessary complexity and redundancy to the data model. It is better to use standard objects and features that are designed for managing products and prices in Salesforce. Manually updating opportunities with prices applicable to distributors (option C) is also not a good solution, as it can be time-consuming, error-prone, and inefficient. It is better to use automation tools or features that can update prices based on predefined criteria or logic. Adding custom fields in opportunity and using triggers to update prices (option D) is also not a good solution, as it can be complex, costly, and difficult to maintain. It is better to use standard fields and features that can handle prices more effectively.

According to the Salesforce documentation3, 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 address the issues with data quality that affect the reliability of executive dashboards, a data architect should recommend:
Building a sales data warehouse with purpose-built data marts for dashboards and senior management reporting (option B). This means creating a separate database or system that stores and organizes sales data from Salesforce and other sources for analytical purposes. A data warehouse can provide a single source of truth for sales data and enable faster and more accurate reporting and analysis. A data mart is a subset of a data warehouse that focuses on a specific subject or business area, such as sales performance, customer segmentation, product profitability, etc. A data mart can provide tailored and relevant data for different users or groups based on their needs and interests.
Exploring third-party data providers to enrich and augment information entered in Salesforce (option C). This means using external services or tools that can validate, correct, update, and enhance the data that is entered or imported into Salesforce. This can help improve data quality and accuracy, and reduce data duplication and incompleteness.
Leveraging Salesforce features, such as validation rules, to avoid incomplete and incorrect records (option D). 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.
Periodically exporting data to cleanse data and import them back into Salesforce for executive reports (option A) is not a good solution, as it can be time-consuming, error-prone, and inefficient. It may also cause data inconsistency and synchronization issues between Salesforce and other systems. Designing and implementing data-quality dashboard to monitor and act on records that are incomplete or incorrect (option E) is also not a good solution, as it can be complex, costly, and difficult to maintain. It may also not address the root causes of data quality issues or prevent them from occurring in the first place.

Patients as Person Accounts, Payment providers as Accounts, & Doctors as Person Accounts is the optimal selection by the data architect to map the actor to Salesforce objects. This is because Person Accounts are a special type of accounts that can store both business and personal information for individual customers.
Payment providers are organizations that pay for the care of patients, so they can be modeled as Accounts.
Doctors are also individuals who provide care plans for patients and need access to patient information, so they can also be modeled as Person Accounts.