Free Salesforce Data-Architect Practice Exam with Questions & Answers | Set: 3

The recommended solution to address the requirements of Universal Containers (UC) is to roll-up discount request amount at the line-item-level and line-item-level summary discount at the opportunity level. This solution would allow UC to show all sums of discounts across line items at an opportunity level using standard roll-up summary fields. Since UC has a multi-level master-detail relationship for opportunities, a custom opportunity line item object, and a custom discount request object, they can use two levels of roll-up summary fields to aggregate the discount amounts from the child objects to the parent objects. For example, they can create a roll-up summary field on the custom line item object to sum up the discount request amounts from the custom discount request object. Then, they can create another roll-up summary field on the opportunity object to sum up the line-item-level summary discounts from the custom line item object.

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.

 According to this article, big objects can store and manage massive amounts of data on the Salesforce platform. This can help UC to archive records from other objects or bring massive datasets from outside systems into a big object. According to this article, Einstein Analytics can be used to create dashboards and lenses to analyze big object data and get insights from advanced AI-driven analytics.

 According to the Salesforce Connect Reporting blog post, one of the considerations for using Salesforce Connect for reporting is the OData callout limits per day. The blog post states that “Salesforce Connect has a limit of 100,000 callouts per day. This limit is shared across all external data sources in your org. If you exceed this limit, you will receive an error message and no more callouts will be allowed until the next day.” Therefore, a data architect should consider this limit before implementing Salesforce Connect for reporting.

Lightning Platform Plus is the license type that best fits UC’s requirements, as it allows up to 50 custom objects and 40,000 API calls per user per 24-hour period4. Service Cloud does not provide enough custom objects or API calls. Lightning Platform Start only allows up to 10 custom objects and 5,000 API calls per user per 24-hour period. Lightning External Apps Start is for external users and does not provide enough API calls.

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.

Evaluating a third-party AppExchange app, such as OwnBackup or Spanning, etc., for backup and archival purposes is the best approach to address the requirement of having a one-day SLA for any data loss. These apps can provide automated backups, granular restores, and compliance features that can help UC recover from any data loss or corruption scenarios.

Having each system pull the record changes from Salesforce using change data capture (option D) is the correct solution for ensuring all systems using customer data are kept up to date, as it allows the systems to subscribe to real-time events from Salesforce and receive notifications when customer records are created, updated, deleted, or undeleted. Sending customer data nightly to the system of truth in a scheduled batch job (option A) or sending customer record changes from Salesforce to each system in a nightly batch job (option B) are not good solutions, as they may cause data latency and inconsistency, and they do not provide real-time updates. Sending customer record changes from Salesforce to the system of truth in real time (option C) is also not a good solution, as it does not address how the other systems will receive the updates from the system of truth.

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.

According to this article, Einstein Analytics for multi-org can help UC to report on accounts and opportunities from each org in one place. It can also provide advanced AI-driven analytics on the dataset using Einstein Discovery.