Universal Containers has implemented Salesforce for its operations. In order for customers to be created in their MDM solution, the customer record needs to have the following attributes:
1. First Name
2. Last Name
3. Email
Which option should the data architect recommend to mandate this when customers are created in Salesforce?
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.
Northern Trail Outfitters (NTO) wants to capture a list of customers that have bought a particular product. The solution architect has recommended to create a custom object for product, and to create a lookup relationship between its customers and its products.
Products will be modeled as a custom object (NTO_ Product__ c) and customers are modeled
as person accounts. Every NTO product may have millions of customers looking up a single product, resulting in a lookup skew.
What should a data architect suggest to mitigate Issues related to lookup skew?
creating multiple similar products and distributing the skew across those products can be a way to mitigate issues related to lookup skew. The article explains that lookup skew happens when a very large number of records are associated with a single record in the lookup object, and this can cause record locking and performance issues. The article suggests creating multiple copies of the same product record and assigning different child records to each copy, so that the number of child records per parent record is reduced.
Universal Containers has implemented Salesforce for its operations. In order for customers to be created in their MDM solution, the customer record needs to have the following attributes:
1. First Name
2. Last Name
3. Email
Which option should the data architect recommend to mandate this when customers are created in Salesforce?
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.
Northern Trail Outfitters (NTO) wants to capture a list of customers that have bought a particular product. The solution architect has recommended to create a custom object for product, and to create a lookup relationship between its customers and its products.
Products will be modeled as a custom object (NTO_ Product__ c) and customers are modeled
as person accounts. Every NTO product may have millions of customers looking up a single product, resulting in a lookup skew.
What should a data architect suggest to mitigate Issues related to lookup skew?
creating multiple similar products and distributing the skew across those products can be a way to mitigate issues related to lookup skew. The article explains that lookup skew happens when a very large number of records are associated with a single record in the lookup object, and this can cause record locking and performance issues. The article suggests creating multiple copies of the same product record and assigning different child records to each copy, so that the number of child records per parent record is reduced.
Which API should a data architect use if exporting 1million records from Salesforce?
Using Bulk API to export 1 million records from Salesforce is the best option. Bulk API is a RESTful API that allows you to perform asynchronous operations on large sets of data. You can use Bulk API to create, update, delete, or query millions of records in batches. Bulk API is optimized for performance and scalability, and it can handle complex data loading scenarios.
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