Key stakeholders from both systems should collaborate with the Architect to determine which

system is the system of record on a field level is what the Architect should do to achieve this goal of

integrating two different systems with customer records into the Salesforce Account object using

Master Data Management. The system of record is the authoritative source of truth for a given entity

or field in a given context. Different systems may have different levels of accuracy, completeness,

timeliness, or relevance for different fields. Therefore, it is important to involve key stakeholders

from both systems who have knowledge and expertise about their data quality and business needs

to decide which system should be the system of record for each field. The Architect should facilitate

this collaboration and document the decisions and rationale for each field. The other options are not

correct or feasible, as they would either delegate or abdicate the responsibility of determining the

system of record, ignore or disregard the input from key stakeholders, or assume or impose a default

system of record without considering the data quality and business needs.

According to the Salesforce documentation, a relationship field is a field that allows linking one

object to another object in Salesforce. There are different types of relationship fields that have

different characteristics and behaviors, such as master-detail, lookup, indirect lookup, external

lookup, etc.

To recommend a type of relationship field for this scenario, where legacy inventory data will be

stored in a custom child object called Inventory__c, inventory data should be related to the standard

Account object, the Inventory__c object should inherit the same sharing rules as the Account object,

and anytime an Account record is deleted in Salesforce, the related Inventory__c record(s) should be

deleted as well, a data architect should recommend:

Master-detail relationship field on Inventory__c, related to Account (option B). This means creating a

field on the Inventory__c object that

the Account object as its parent. A master-detail

relationship field establishes a parent-child relationship between two objects, where the parent

object controls certain behaviors of the child object. For example, a master-detail relationship field

can:

Inherit the sharing and security settings from the parent object to the child object. This means that

the users who can access and edit the parent record can also access and edit the related child

records.

Cascade delete from the parent object to the child object. This means that when a parent record is

deleted, all the related child records are also deleted.

Roll up summary fields from the child object to the parent object. This means that the parent object

can display aggregated information from the child records, such as count, sum, min, max, or average.

Master-detail relationship field on Account, related to Inventory__c (option A) is not a good solution,

as it reverses the direction of the relationship. This means creating a field on the Account object that

references the Inventory__c object as its parent. This is not possible, as a standard object cannot be

on the detail side of a master-detail relationship. Indirect lookup relationship field on Account,

related to Inventory__c (option C) is also not a good solution, as it is a special type of relationship

field that allows linking a custom object to a standard object on an external system using an indirect

reference. This is not applicable for this scenario, as both objects are in Salesforce and do not need

an external reference. Lookup relationship field on Inventory__c related to Account (option D) is also

not a good solution, as it establishes a looser relationship between two objects than a master-detail

relationship. A lookup relationship field does not inherit sharing and security settings from the

parent object to the child object, does not cascade delete from the parent object to the child object,

and does not roll up summary fields from the child object to the parent object.

The correct answer is C and D. To minimize performance and storage issues, a data architect should

recommend leveraging on premise data archival and building integration to view archived data, and

leveraging big object to archive case data and lightning components to show archived data. These

solutions will allow some user groups to access historical cases for up to 7 years without consuming

too much storage space or affecting the performance of queries and reports. Option A is incorrect

because exporting data out of salesforce and storing it in flat files on external system will make it

difficult to access and query the data. Option B is incorrect because creating a custom object to store

case history and run reports on it will still consume a lot of storage space and impact the

performance of queries and reports.

According to the How to avoid row lock or race condition in Apex blog post, one of the ways to

prevent race condition in Apex is to use the FOR UPDATE keyword in SOQL statements. The blog post

states that “We need to lock the records on which we are working such that other batches or threads

will not be having any effect on them. How can we lock a record, then? We need to make use of FOR

UPDATE keyword in the SOQL query.” Therefore, a data architect should recommend this solution to

guarantee that records are not being updated at the same time by different processes.

The best object model to configure within SF for migrating individual customers (B2C) data from

legacy systems is to leverage person account object in Salesforce. Person accounts are a special type

of accounts that store information about individual people by combining certain account and contact

fields into a single record4. Person accounts are useful for B2C scenarios where there is no need to

associate a company name with a contact. Person accounts also support standard Salesforce features

and functionality, such as leads, campaigns, reports, dashboards, etc5. Leverage custom person

account object in SF is not a good option because there is no such thing as a custom person account

object. Leverage custom account and contact object in SF is not a good option because it would

require creating and maintaining additional objects and fields that may not be necessary or

compatible with standard Salesforce features. Leverage standard account and contact object in SF is

not a good option because it would require filling in dummy values for the account name field, which

is mandatory for standard accounts.

A data dictionary is a document that describes the structure, format, and meaning of data entities

and attributes. A common technique to build a data dictionary is to use an entity relationship

diagram (ERD), which shows the logical relationships between objects and fields in a graphical

way. Another technique is to use the Salesforce Metadata API, which allows you to retrieve and

deploy the metadata that defines your Salesforce org

Acquiring an AppExchange Lead de-duplication application and implementing Salesforce Matching

and Duplicate Rules are two approaches that the manager at Cloud Kicks should take to avoid

creating duplicate records when importing Leads into Salesforce. An AppExchange Lead de-

duplication application can provide additional features and functionality for finding and preventing

duplicate Leads during import, such as fuzzy matching, custom rules, mass merge, etc. Salesforce

Matching and Duplicate Rules can allow the manager to define how Salesforce identifies duplicate

Leads based on various criteria and how users can handle them during import, such as blocking,

allowing, or alerting them. The other options are not feasible or effective for avoiding duplicate

records, as they would either not work during import, not provide de-duplication capabilities, or

require additional customization.

The recommended solution for Universal Containers (UC) to implement its new Internet of Things

technology is to create a new Container Reading custom object with a master-detail relationship to

Container which is created when a new measure is received for a specific container. Implement an

archiving process that runs every hour. This solution would allow UC to store and access current and

historical temperature and humidity data for each container on Salesforce, and use reports and

dashboards to analyze it. However, since UC expects a large volume of data over time, they should

implement an archiving process that moves data off-platform after a certain period of time to avoid

hitting the Org data storage limit and maintain optimal performance. The other options are not

recommended, as they would either not store the historical data on Salesforce, or create too many

custom fields on the Container object that could impact performance and usability.

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.

Determining if any integration points create records in Salesforce, running key reports to determine

what fields should be required, and reviewing the sharing model to determine impact on duplicate

records are three areas of Salesforce that the architect should review before proposing any design

recommendation. These areas will help to identify the sources and quality of data, the business rules

and validations for data entry, and the access and visibility of data across users and roles. Reviewing

the metadata xml files for redundant fields to consolidate, and exporting the setup audit trail to

review what fields are being used are not necessary steps for optimizing a data stewardship

engagement, as they are more related to metadata management and audit tracking.