According to the exam guide, one of the objectives is to " describe the use cases and considerations for deferring sharing calculations " 1 . This implies that option A is the correct way to load large amounts of data into Salesforce without affecting performance and data integrity.  Deferring sharing calculations allows the data to be loaded first and then the sharing rules to be applied later 2 .  Option B is not correct because workflows are not recommended for calculating summary values, as they can cause performance issues and data skew 3 . Option C is not correct because triggers are also not recommended for calculating summary values, as they can cause governor limit errors and data inconsistency. Option D is not correct because external IDs are used to link records from different systems, not to improve data loading performance.

Creating an account plan object with a lookup relationship to Account with validation rules to enforce the Account association can help UC meet their requirement. A lookup relationship allows different accessibility requirements for the account plan object and the account object, as well as different ownership and sharing settings. A validation rule can ensure that each account plan has an account associated with it.

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.

Scheduling a weekly dashboard displaying records that are missing information to be sent to managers for review and using Workflow, Validation Rules, and Force.com code (Apex) to enforce critical business processes are two approaches that an architect should recommend to enforce the new Data Quality Plan for UC’s Salesforce data. Scheduling a weekly dashboard can provide a regular and vis ual way of monitoring the data quality and identifying any gaps or issues that need to be addressed by the managers or users. Using Workflow, Validation Rules, and Apex can provide various ways of enforcing data quality standards and business logic by automating actions, displaying error messages, or executing custom code when users create or edit records.  The other options are not suitable or helpful for enforcing the Data Quality Plan, as they would either not provide real-time feedback, not prevent data quality issues, or not leverage the capabilities of Salesforce

Using external objects to maintain Sales Order in Salesforce is the best recommendation for maintaining Sales Orders in Salesforce, as it allows users to access large volumes of data stored outside Salesforce without copying or synchronizing it. Using custom objects, custom big objects, or standard order object may not be feasible or optimal for storing more than 150 million records that will not be updated in Salesforce.

The data architect should recommend using AppExchange package for backup and restore. AppExchange is a marketplace for Salesforce apps and solutions that can be installed and configured in Salesforce orgs. There are several AppExchange packages that provide backup and restore functionality for Salesforce data, such as OwnBackup, Odaseva, or Spanning. These packages can perform daily backups of Salesforce data to a secure cloud storage, and provide restore capability to avoid any data loss in case of outage. Option B is incorrect because using ETL (Extract, Transform, Load) for backup and restore from EDW (Enterprise Data Warehouse) will require additional development effort and may not be reliable or secure. Option C is incorrect because using Bulk API to extract data on daily basis to EDW and REST API for restore will require additional integration effort and may not be scalable or performant. Option D is incorrect because changing weekly backup process to daily backup, and implementing a custom restore solution will require additional configuration effort and may not be robust or compliant.

Analytics/BI Owners and Data Domain Stewards are appropriate roles as members of a team that makes decisions on enterprise-wide data governance. Analytics/BI Owners are responsible for defining the business requirements and metrics for data analysis and reporting, and Data Domain Stewards are responsible for defining and enforcing the data quality standards and rules for specific data domains. Salesforce Administrators and Operational Data Users are not suitable roles for this team, as they are more focused on the operational aspects of data management, such as configuration, maintenance, and usage.

Using an ETL tool to orchestrate the migration is the best strategy for this scenario, as it can handle the data model changes, the large volume of records, and the tight timeline. Writing a script to use the Bulk API (option C) is also possible, but it would require more coding and testing effort. Using Data Loader and Data Import Wizard (option B) is not suitable for migrating three million records, as they have limitations on the batch size and the number of records per operation.  Using Salesforce CLI (option D) is also not recommended for large data migration, as it is mainly designed for development and testing purposes

The data architect should recommend building a batch job to move two-year-old records off platform, and delete records from Salesforce as an archiving solution. A batch job is a process that runs in the background and performs operations on large volumes of data in Salesforce. By building a batch job that moves two-year-old records off platform to an external storage system, such as Amazon S3 or Google Cloud Storage, and deletes them from Salesforce, the data architect can reduce the storage consumption and improve the performance of Salesforce org. Option A is incorrect because using a third-party backup solution to backup all data off platform will not free up any storage space in Salesforce, unless the data is also deleted from Salesforce after backup. Option B is incorrect because building a batch job to move all records off platform, and delete all records from Salesforce will result in losing all the current data in Salesforce, which may not be desirable or feasible. Option D is incorrect because building a batch job to move all restore off platform, and delete old records from Salesforce does not make sense, as restore implies restoring data back to Salesforce, not moving it off platform.

 Making sure the query does not contain NULL in any filter criteria can avoid full table scans and leverage indexes more efficiently. Queries with NULL filters are not selective and can cause performance issues. Creating custom indexes on the fields used in the filter criteria can also enhance the query performance by reducing the number of records to scan.