Creating validation rules to check if the required attributes are entered is the best option to mandate this when customers are created in Salesforce. Validation rules allow you to specify criteria that must be met before a record can be saved. You can use validation rules to ensure that customers have a first name, last name, and email when they are created in Salesforce. This way, you can prevent incomplete or invalid data from being sent to your MDM solution.

Creating the users, uploading all data, and then deploying the sharing rules will reduce the number of sharing recalculations that occur during the data load. Deferring sharing calculations until the data has finished uploading will also improve the performance by postponing the sharing rule evaluation. These are the recommended best practices for loading large data sets into Salesforce

According to the official Salesforce guide, using Bulk API serial mode for data migration can help avoid locking issues by processing batches in a single thread. This mode ensures that batches are processed in the order they are received and that only one batch is processed at a time. This reduces the risk of lock contention and deadlocks, especially when updating parent and child records in a lookup relationship. Option B is the correct answer because it suggests using Bulk API serial mode for data migration.

According to Trailhead, Customer 360 Data Manager is a feature that allows administrators to connect, reconcile, and share customer data across Salesforce orgs and external systems.
Customer 360 Data Manager can sync product and pricing information from product master database to Salesforce using predefined mappings and rules, ensuring data quality and consistency. Option D is the correct answer because it suggests using Customer 360 Data Manager to sync product and pricing information from product master database to Salesforce.

According to the What is Master Data Management (MDM)? article, some of the data challenges that an MDM tool can solve are data duplication, data accuracy and quality, and data standardization. The article states that "MDM solutions comprise a broad range of data cleansing, transformation, and integration practices. As data sources are added to the system, MDM initiates processes to identify, collect, transform, and repair data. Once the data meets the quality thresholds, schemas and taxonomies are created to help maintain a high-quality master reference." Therefore, an MDM tool can help NTO eliminate data duplication across different systems, improve data accuracy and quality by removing errors and inconsistencies, and standardize data formats and definitions for better integration and analysis.