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.

To provide the sales team with the required functionality, a data architect should use a mashup page

that will present the billing system records within Salesforce. A mashup page is a web page that

combines data from multiple sources into a single integrated view. A mashup page can be created

using Visualforce or Lightning Web Components, and can use Salesforce Connect or custom

integrations to access external data from the on-premise web-based billing and invoice system. This

will allow the sales team to pull a customer record and view their account status, invoice history, and

opportunities without navigating outside of Salesforce. Option A is incorrect because creating a

custom object and migrating the last 12 months of invoice data into Salesforce so it can be displayed

on the account layout will consume a lot of storage space and may not reflect the latest data from

the billing system. Option B is incorrect because writing an Apex callout and populating a related list

to display on the account record will require additional development effort and may not be scalable

or performant for large volumes of data. Option D is incorrect because creating a visual force tab

with the billing system encapsulated within an iframe will not allow the sales team to view the billing

system records within the customer record, but rather in a separate tab.

Custom metadata records are deployable using packages, which makes them easier to migrate from

one environment to another. Custom settings records are not deployable using packages, and they

are copied from production to sandbox. Custom metadata types are not available for reporting, and

custom metadata records are not editable in Apex.

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 the official Salesforce guide1, using Bulk API serial mode for data migration can help

avoid locking issues by processing batches in a single thread. This mode ensures that batches are

processed in the order they are received and that only one batch is processed at a time. This reduces

the risk of lock contention and deadlocks, especially when updating parent and child records in a

lookup relationship. Option B is the correct answer because it suggests using Bulk API serial mode for

data migration. Option A is incorrect because using Bulk API parallel mode for data migration can

cause locking issues by processing batches in multiple threads. This mode does not guarantee the

order of batch processing and can result in concurrent updates to the same records, which can lead

to lock contention and deadlocks. Option C is incorrect because inserting the order in another

custom object and using Batch Apex to move the records to Service_Order__c object adds

unnecessary complexity and overhead to the data migration process. Option D is incorrect because

changing the lookup configuration to “Clear the value of this field” when lookup record is deleted

does not address the locking issues caused by data migration, but rather by record deletion.

The correct answer is B, data export wizard. The data export wizard is a tool that allows you to export

your data in CSV format, schedule the export process to run once per week, and configure the export

settings through the Salesforce UI. The data export wizard can handle up to 51 million records per

export. The bulk API, third-party ETL tools, and data loader are also tools that can export data, but

they are not as simple or user-friendly as the data export wizard.

Using a third-party data source to update contact information in Salesforce is the best solution to

reduce the amount of returned mails. This way, the data architect can ensure that the contact

information is accurate and up-to-date without relying on manual verification or deletion of contacts

When denormalizing a data model into a single object with a picklist field, the data architect should

consider the Bulk API limitations on picklist fields and the standard list view in-line editing. The Bulk

API has a limit of 1,000 distinct picklist values per file1, which could be an issue if there are more

than 1,000 venues in the future. The standard list view in-line editing allows users to edit multiple

records at once, which could introduce data quality issues if the venue picklist is not validated or

restricted2. The other options are not relevant to denormalizing a data model.

The three questions that should be considered when designing an appropriate archiving strategy are:

If reporting is necessary, can the information be aggregated into fewer, summary records? Will the

data being archived need to be reported on or accessed in any way in the future? Are there any

regulatory restrictions that will influence the archiving and purging plans? These questions are

important because they help determine the scope, frequency, and method of archiving and purging

data from Salesforce. For example, if reporting is necessary, then summary records or analytics

snapshots can be used to store aggregated data and reduce the number of records that need to be

archived1. If the data being archived needs to be accessed in the future, then offsite storage or

external objects can be used to retain the data and make it available on demand2. If there are any

regulatory restrictions, such as GDPR or HIPAA, then the archiving and purging plans need to comply

with them and ensure data security and privacy

Using Salesforce duplicate management and using validation rules on new record create and edit are

two things that an architect should consider using to maintain data quality for managing Contacts.

Salesforce duplicate management allows the architect to create matching rules and duplicate rules to

identify, prevent, or allow duplicate records based on various criteria. Validation rules allow the

architect to enforce data quality standards and business logic by displaying error messages when

users try to save invalid data. The other options are not relevant or helpful for maintaining data

quality