Free DATA-ARCHITECT Practice Test Questions and Answers (2026)

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

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.