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Leverage an External ID from source system orders to import related order lines. This is the recommended solution because it allows you to use the upsert operation to match records based on the External ID field, which is indexed and unique. This avoids the need to query the parent order IDs in Salesforce before importing the order line items, which can cause time-out errors or long delays1.

Runninga report that shows the last time the key fields were updated can help Get Cloudy Consulting identify stale or outdated data and prioritize data cleansing activities. Running a report that shows the percentage of blanks for the important fields can help Get Cloudy Consulting measure the completeness of their data and identify gaps or missing value

Leveraging duplicaterules in Salesforce to validate duplicates during the account creation process (option A) is the best option to implement to avoid duplicates, as it allows the sales associates to identify and merge duplicate accounts before they are saved. Developing anApex class that searches for duplicates and removes them nightly (option B) is not a good option, as it may cause data loss or conflicts, and it does not prevent duplicates from being created in the first place. Implementing an MDM solution to validate thecustomer information before creating Salesforce (option C) is also not a good option, as it may introduce additional complexity and cost, and it does not address the issue of customers interacting with both the website and the store. Building a custom search functionality that allows sales associates to search for customer in real time upon visiting their retail stores (option D) is also not a good option, as it may not be reliable or user-friendly, and it does not leverage the existing Global Search feature.

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 truthin 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.

 To improve data quality and avoid data skew, the data architect should create a picklist field with values sorted alphabetically for tracking shipping status on opportunity. A picklist field ensures that only valid values are entered and prevents typos or variations in spelling. Sorting the values alphabetically makes it easier for users to find and select the correct value. Data skew occurs when a large number of records are owned by a single user or have a single value for a field. Creating a picklist field with a limited number of values does not cause data skew, as long as the distribution of values is balanced and not skewed towards one value.

According to Trailhead2, Salesforce Connect is a feature that allows administrators to integrate external data sources with Salesforce without copying or synchronizing the data. Salesforce Connect can use external objects to display the external data in Salesforce as if it were stored in Salesforce objects, enabling seamless user experience and real-time access. Option B is the correct answer because it suggests using Salesforce Connect and an external object to display the order history in Salesforce. Option A is incorrect because using an Apex callout to populate a text area field for displaying the order history can be inefficient and unreliable, as it requires making an HTTP request for each customer record and storing the order history as unstructured text. Option C is incorrect because importing the order history into a custom Salesforce object, update nightly can consume a lot of storage space and cause data latency, as the order history may change more frequently than once a night. Option D is incorrect because embedding the ERP system in an iframe and displaying on a custom tab can degrade the user experience and security, as it requires switching between tabs and authenticating with another system.

Option B is the most appropriate approach for data archiving in this scenario1. By aggregating order amount data at a summary level and storing it in a custom object, you can reduce data storage and still provide visibility to the Sales Reps on how much money each customer generates year-over-year. Option A is not correct because storing data in a zip file does not make it available in Salesforce. Option C is not correct because exporting and deleting orders and order line items may lose important details that are needed for analysis or reporting. Option D is not correct because deleting orders and order line items without exporting them may cause data loss or inconsistency if the customer does not have order information in another system.

Using an AppExchange package for Data Quality is a good way to match Salesforce data against a trusted source, such as an MDM system. You can use tools like Cloudingo, DupeCatcher, or DemandTools to identify and merge duplicate records, standardize data formats, and enrich data with external sources

A required Lookup relationship ensures that the Shipment record must have a value for each of the three parent records, and also prevents the deletion of those parent records if they are referenced by a Shipment record. A Master-Detail relationship would not allow separate sharing models for each custom object, and a VLOOKUP formula field would not enforce the relationship or prevent deletion

 Async SOQL with a custom object (option B) and Einstein Analytics (option D) are the two native tools that can be used to report on big object data. Async SOQL allows querying big object data and storing the results in a custom object, which can then be used for reporting. Einstein Analytics can connect to big object data sources and provide advanced analytics and visualization features. Standard reports and dashboards (option A) and standard SOQL queries (option C) cannot be used to report on big object data, as they do not support big object fields