Salesforce Data-Architect Exam Dumps

Changing Opportunity Amount field access to Read Only for sales users field-level security (option C) is the best way to address the overwriting issue, as it prevents sales users from updating the field that is controlled by PPS, and ensures data consistency and accuracy. Creating a custom field for Opportunity amount that PSS updates separating the field sales user updates (option A) or creating a custom field for Opportunity amount that sales users update separating the field that PPS updates (option D) are not good solutions, as they may create confusion and inconsistency with the Opportunity Forecast feature. Changing PSS integration to update only Opportunity Amount field when the value is null (option B) is also not a good solution, as it may cause data loss or conflicts with the sales users' inputs.

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.

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.

The correct answer is A. To migrate the data to Salesforce, the data architect should create custom objects to store agent relationships. This will allow the data architect to replicate the look-up table structure from the legacy CRM system and maintain the relationship data in Salesforce. Option B is incorrect because migrating to Salesforce without a record owner will cause errors and prevent the data from being imported. Option C is incorrect because assigning record owner based on relationship will not preserve the agent relationships from the legacy CRM system. Option D is incorrect because migrating the data and assigning to a non-person system user will not allow the users to access and modify the data.

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.