According to the Get Started with Big Objects unit on Trailhead, one of the use cases for custom big objects is to store and manage loyalty program data for customers. The unit states that "From loyalty programs to transactions, order, and billing information, use a custom big object to keep track of every detail." Therefore, a custom big object can be used to capture the reward program data and display it on the contact record.
Additionally, according to the Big Objects Implementation Guide, big objects can handle massive amounts of data (up to billions of records) and can be updated nightly from external systems using Bulk API or batch Apex. Therefore, a custom big object can meet the requirements of NTO's loyalty program scenario.

Defining data duplication standards and rules, measuring data timeliness and consistency, and measuring data completeness and accuracy are three key factors that a data architect should consider while defining data quality standards. Defining data duplication standards and rules can help prevent or reduce duplicate records in the org by specifying criteria and actions for identifying and merging duplicates. Measuring data timeliness and consistency can help ensure that the data is up-to-date, reliable, and synchronized across different sources.
Measuring data completeness and accuracy can help ensure that the data is sufficient, relevant, and correct for the intended purposes.

Partner community license type is the best option for building a channel sales portal, as it allows external resellers to access lead, opportunity, and order data, as well as run reports and dashboards. Customer community and customer community plus license types are more suitable for customer service portals, while lightning external apps starter license type does not support reports and dashboards

Enabling Account de-duplication by creating matching rules in Salesforce, which will mass merge duplicate Accounts, is what UC should do to ensure proper data quality for their Account records. Matching rules allow UC to define how Salesforce identifies duplicate Accounts based on various criteria, such as name, address, phone number, etc. Mass merge allows UC to merge up to 200 duplicate Accounts at a time, based on the matching rules. This simplifies and automates the process of de-duplicating Accounts and improves data quality. The other options are either more time-consuming, costly, or error-prone for ensuring proper data quality.

Creating a unique global customer ID for each customer and storing that in all systems for referential identity (option A), investing in a data duplicate tool to de-dupe and merge duplicate records across all systems (option C), and creating a customer master database external to Salesforce as a system of truth and syncing the customer data with all systems (option E) are the three options that a data architect should perform to resolve the issues with the customer data. Option A ensures that each customer can be uniquely identified across different systems, option C eliminates duplicate records and improves data quality, and option E provides a consistent and reliable source of customer data for all systems. Using Salesforce CDC to sync customer data across all systems (option B) is not a good option, as it does not address the duplication or inconsistency issues. Duplicating customer records across the system and providing a two-way sync of data between the systems (option D) is also not a good option, as it may create more confusion and conflicts with customer data.