Data-Architect Salesforce Certified Platform Data Architect (Plat-Arch-201) Questions and Answers

Ensuring the lead data is preprocessed for quality before loading into Salesforce is the best way to maintain data quality2. Activating validation rules after the load or developing a custom Apex batch process may not catch all the errors or may require additional time and resources. Allowing validation rules to be activated during the load may cause failures or delays.

According to Trailhead2, one of the best practices to increase Salesforce adoption is to migrate existing data from legacy systems or spreadsheets into Salesforce, so that users can access all their data in one place and leverage the features and functionality of Salesforce. Option B is the correct answer because it suggests defining a standard mapping and training sales users to import opportunity data from Excel files into Salesforce, which can help them transition from their old process and increase their confidence and satisfaction with Salesforce. Option A is incorrect because using the Excel connector to Salesforce does not migrate the data into Salesforce, but only syncs it between Excel and Salesforce, which can cause data inconsistency and duplication issues. Option C is incorrect because loading data in an external database and providing access to it does not increase Salesforce adoption, but rather creates another system for users to manage and switch between. Option D is incorrect because creating a chatter group and uploading all Excel files to it does not migrate the data into Salesforce, but only stores it as attachments, which cannot be used for reporting or analysis purposes.

The issue that an architect will have when implementing the quarterly accounts receivable report in Salesforce is that Salesforce does not allow more than four objects in a single report type. A report type defines the set of records and fields available to a report based on the relationships between a primary object and up to four related objects. A report type has the following limitations:

It cannot include more than four objects in a single report type, which means that the report cannot compile data from five objects (Accounts, Contacts, Opportunities, Orders, and Order Line Items) at once.

It cannot include objects that are more than two relationships away from each other, which means that the report cannot access fields from Order Line Items through Opportunities and Orders.

It cannot include objects that have a many-to-many relationship with each other, which means that the report cannot access fields from Contacts and Opportunities through the junction object Opportunity Contact Role.

The best solution to store historical cases for 10 years for audit purpose is to use a custom Big object to store archived case data. Big objects are a type of custom object that can store massive amounts of data on the Salesforce platform without affecting performance or storage limits. They also support point-and-click tools, triggers, and Apex code. Big objects can be used for archiving historical data that needs to be retained for compliance or analytics purposes3. Archiving case data using Salesforce Archiving process is not a good option because it only supports archiving cases that are closed for more than one year, and it does not allow customizing the archival criteria or accessing the archived data via Apex or APIs4. Purchasing more data storage to support case object is expensive and may impact performance. Using a custom object to store archived case data is not scalable and may consume a lot of storage space.

Changing the opportunity amount field access to read only for sales users using field level security is the best way to address the overriding issue. This way, the sales users can still view the opportunity amount field but cannot edit it, and PPS can update it as the trusted source

To allow only the owner of a contact record to delete that contact, the data architect should create a “before delete” trigger to check if the current user is not the owner. The trigger can use the UserInfo.getUserId() method to get the current user’s ID and compare it with the OwnerId field of the contact record. If they are not equal, the trigger can add an error to the record and prevent it from being deleted. The other options are not suitable for meeting the requirements, as they would either restrict the edit access or delete access for all users, regardless of ownership.

The best solution to comply with the DR policy is to leverage a 3rd party tool that extract Salesforce data/metadata and stores the information in an external protected system. This solution can help create a backup of Salesforce data and metadata in case of a disaster or data loss event. It can also help restore data from the backup system to Salesforce if needed. There are various 3rd party tools available in the AppExchange or online that offer data backup and recovery services for Salesforce67. Leverage Salesforce weekly exports and store data in flat files on a protected system is not a good solution because it does not include metadata backup and it does not allow granular or automated data recovery. Utilize an ETL tool to migrate data to an on-premise archive solution is not a good solution because it does not include metadata backup and it may require complex data transformations and synchronizations. Write a custom batch job to extract data changes nightly and store in an external protected system is not a good solution because it does not include metadata backup and it may have performance or reliability issues.

 Engaging with executive sponsorship to assess enterprise data strategy and goals, and engaging with business units and IT to assess current operational systems and data models are two first steps that would be appropriate for UC to initiate an assessment of data architecture. These steps will help to understand the current state of data management, the business needs and expectations, and the gaps and opportunities for improvement. Engaging with IT program managers to assess current velocity of projects in the pipeline, and engaging with database administrators to assess current database performance metrics are not relevant steps for assessing data architecture, as they are more related to project management and technical performance.

The setup audit trail tracks changes made in your org’s setup area for the past 180 days1. You can export the setup audit trail and find the fields in question by filtering by action type, user, or date2.

Grouping batches by the AccountId field can improve the performance and avoid locking issues when loading Opportunity records using the Bulk API in parallel mode1. This is because the AccountId field is indexed and can be used to distribute the records evenly across batches

Loading new records via the Insert operation and existing records via the Update operation will allow using the external ID field as a unique identifier and avoid any duplication or overwriting of records. This is faster and safer than deleting all existing records or using the Upsert operation, which might cause conflicts or errors .

 Creating a duplicate rule for account, lead, and contact, using standard matching rules for these objects, and setting the action to report and alert for both creates and edits can help UC fix the issue immediately by prompting users about a record that possibly exists in Salesforce. This can also generate a report regarding duplicate records that can be used for further analysis and resolution

 Specify the legacy CRM as the system of record during transition until it is removed from operation and fully replaced by Salesforce. This is a good technique to ensure data consistency and avoid conflicts between the two systems. Work with stakeholders to establish a Master Data Management plan for the system of record for specific objects, records, and fields. This is another good technique to define the data governance and stewardship policies and processes for managing data quality and integrity across multiple systems

 According to the Salesforce documentation1, the “UNABLE _TO_LOCK_ROW” error occurs when a record is being updated or created, and another operation tries to access or update the same record at the same time. This can cause lock contention and timeout issues. To resolve the error, some of the recommended solutions are:

Perform a batch job in serial mode and reduce the batch size (option B). This means running the batch job one at a time and processing fewer records per batch. This can reduce the chances of concurrent updates and lock contention on the same records.

Use the FOR UPDATE keyword to lock records in Apex code or API calls. This means explicitly locking the records that are being accessed or updated by a transaction, and preventing other transactions from modifying them until the lock is released. This can avoid conflicts and errors between concurrent operations on the same records2.

Defer rollup summary field calculation during data migration (option A). This means disabling the automatic calculation of rollup summary fields on the parent object when child records are inserted or updated. This can improve performance and avoid locking issues on the parent records. However, this option is only available for custom objects, not standard objects3.

Performing a batch job in parallel mode and reducing the batch size (option C) is not a good solution, as it can still cause lock contention and errors if multiple batches try to access or update the same records at the same time. Leveraging Data Loader’s platform API to load data (option D) is also not a good solution, as it can still encounter locking issues if other operations are modifying the same records at the same time.

The correct answer is C, create a geolocation custom field to maintain this requirement. A geolocation custom field is a compound field that can store both latitude and longitude information in a single field. It also supports geolocation functions and distance calculations. Creating formula fields or custom fields for latitude and longitude separately would be inefficient and redundant. Recommending app exchange packages would not be a direct solution to the requirement.

Using leading % wildcards, performing large loads and deletions, and using NOT and != operators are three options that prevent a query from being selective. These options can cause the query to scan a large number of records or indexes, which can negatively impact the performance and scalability of the query5. Using a custom index on a deterministic formula field, on the other hand, can improve the query selectivity by allowing the query to use the index instead of scanning all the records6.

Whether the system of record changes for different tables and the number of systems that are integrating with each other are two factors that the architect should take into consideration when deciding whether or not to use a Master Data Management system to achieve the solution of integrating data and resolving duplicates and discrepancies between Salesforce and one or more external systems. The system of record is the authoritative source of truth for a given entity or field in a given context. If different systems have different systems of record for different tables, then a Master Data Management system can help to manage and synchronize the data across systems and ensure data quality and consistency. The number of systems that are integrating with each other is another factor that affects the complexity and scalability of the integration solution. If there are many systems that need to integrate with each other, then a Master Data Management system can provide a centralized and standardized way of integrating data and resolving duplicates and discrepancies across systems. The other factors are not relevant or important for deciding whether or not to use a Master Data Management system, as they do not affect the data quality or integration challenges that a Master Data Management system can address.

 Identifying a location to store archived data and using scheduled batch jobs to migrate and purge the aged data on a nightly basis can be a way to meet the requirements for an archive solution. The article provides a use case of how to use Heroku Connect, Postgres, and Salesforce Connect to archive old data, free up space in the org, and still retain the option to unarchive the data if needed. The article also explains how this solution can improve Salesforce performance and meet data retention policies.

Removing custom indexes on the data being loaded will prevent unnecessary index maintenance and improve the data load speed. Deferring sharing calculations of the Salesforce Org will avoid frequent sharing rule evaluations and reduce the load time. Deactivating approval processes and workflow rules will prevent triggering any automation logic that might slow down or fail the data load.

Leveraging a “tier”-based approach to classify the record storage need is a true statement with respect to a data archiving strategy for UC. This approach involves defining different tiers of data based on their usage, value, and retention policies, and then applying appropriate storage and archiving solutions for each tier.

Leveraging Heroku Connect and Heroku Postgres, middleware tools, or Salesforce Connect and the cross-org adapter are all viable options to share data between Org A and Org B in near-real time3. Installing a 3rd party AppExchange tool may not provide bidirectional sync or may have additional costs. Developing an Apex class that pushes opportunity data between orgs daily via the Apex schedule may not meet the near-real time requirement.

To build a custom big object to support storing more than 100 million records in Salesforce, a data architect should use Metadata API or Big Object in setup. Metadata API is an API that allows developers to create, retrieve, update, or delete metadata components in Salesforce programmatically. Big Object in setup is a user interface that allows admins to create big objects declaratively without writing code. Both tools can be used to define custom big objects and their fields, indexes, and relationships in Salesforce. Option A is incorrect because DX (Developer Experience) is a set of tools that allows developers to create and manage applications on Salesforce Platform, but it does not support creating big objects directly. Option D is incorrect because Object Manager in setup is a user interface that allows admins to create and manage standard and custom objects in Salesforce, but it does not support creating big objects declaratively.

 Integrate SAP with Salesforce, SAP to legacy CRM but not legacy CRM to Salesforce. This is a good recommendation because it allows both Salesforce and the legacy CRM to interact with the ERP system, which can be the source of truth for some data entities. However, it avoids creating a direct connection between Salesforce and the legacy CRM, which can cause data duplication and synchronization issues. Suggest MDM solution and link MDM to salesforce and SAP. This is another good recommendation because it can help manage the data lifecycle and quality across multiple systems, as well as provide a single view of the customer data

A correct technique UC should use to solve the data inconsistency is to define a data taxonomy for product data and apply the taxonomy to the product data in the data warehouse. A data taxonomy is a hierarchical classification of data entities and attributes that defines their meaning, format, and relationships. A data taxonomy can help ensure consistency, accuracy, and completeness of product data across different systems and channels

The correct answer is A, B, and D. To maintain the relationship hierarchy during migration, a data architect should use data loader to export the data from source org and then import or upsert into the target org in sequential order, create an external ID field for each object in the target org and map source record IDs to this field, and replace source record IDs with new record IDs from the target org in the import file. These steps will ensure that the records are linked correctly and the relationships are preserved. Option C is incorrect because redefining the master detail relationship fields to lookup relationship fields in the target org will change the behavior and security of the data model. Option E is incorrect because keeping the relationship fields populated with the source record IDs in the import file will cause errors and prevent the records from being imported.

Enabling Account de-duplication by creating matching rules in Salesforce, which will mass merge duplicate Accounts, is what UC should do to ensure proper data quality for their Account records. Matching rules allow UC to define how Salesforce identifies duplicate Accounts based on various criteria, such as name, address, phone number, etc. Mass merge allows UC to merge up to 200 duplicate Accounts at a time, based on the matching rules. This simplifies and automates the process of de-duplicating Accounts and improves data quality. The other options are either more time-consuming, costly, or error-prone for ensuring proper data quality.

According to the Get Started with Big Objects unit on Trailhead, one of the use cases for custom big objects is to store and manage loyalty program data for customers. The unit states that “From loyalty programs to transactions, order, and billing information, use a custom big object to keep track of every detail.” Therefore, a custom big object can be used to capture the reward program data and display it on the contact record. Additionally, according to the Big Objects Implementation Guide, big objects can handle massive amounts of data (up to billions of records) and can be updated nightly from external systems using Bulk API or batch Apex. Therefore, a custom big object can meet the requirements of NTO’s loyalty program scenario.

 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 best data archiving strategies for UC are to use Big objects and Heroku with external objects. Big objects allow storing large amounts of data on the Salesforce platform without affecting performance or storage limits. They also support point-and-click tools, triggers, and Apex code. Heroku is a cloud platform that can host external databases and integrate with Salesforce using external objects. External objects enable on-demand access to external data sources via standard Salesforce APIs and user interfaces. Using bulk API to hard delete cases from Salesforce will free up storage space and improve performance.

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.

Data integrity and data usability are two aspects of data that an Enterprise data governance program aims to improve. Data integrity refers to the accuracy, consistency, and validity of the data across the enterprise2. Data usability refers to the ease of access, analysis, and interpretation of the data by the end users

Contacting Salesforce to enable Defer Sharing Rules is the feature that would ensure that performance, when moving Sales Reps between regions, remains adequate while meeting existing requirements. Defer Sharing Rules allows you to defer sharing rule recalculation when you make changes to public groups or roles that are used in sharing rules. This can improve performance and avoid locking issues when you move users between groups or roles2. Implementing data archiving for old Shipment records will not help with the performance issue related to sharing rules. Contacting Salesforce to create Skinny tables on Shipment will not help with the performance issue related to sharing rules. Configuring shipment OWD to Public Read/Write will not meet the existing requirements of having a Private sharing model.

Data Loader uses external Id fields to match records in the CSV file with records in Salesforce during an upsert operation. If the CSV file contains duplicate external Id values within the same file, Data Loader will throw an error saying “Duplicate Id Specified” and will not process those records. Therefore, it is important to ensure that the CSV file does not have any duplicate external Id values before using Data Loader to upsert records.

Leveraging duplicate rules 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 an Apex 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 the customer 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.

According to the Salesforce documentation, an opportunity is a standard object that represents a potential sale or deal with an account or contact. An opportunity can have products and prices associated with it using price books. A price book is a standard object that contains a list of products and their prices for different regions, currencies, segments, etc. A price book can be shared with different users or groups based on their visibility and access settings.

To model the requirement of implementing a distributor management application in Salesforce, where distributors create sales orders based on product prices applicable to their region, and sales orders are closed once they are fulfilled, a data architect should:

Configure price books for each region and share with distributors (option B). This means creating different price books for different regions with the appropriate products and prices, and sharing them with the distributors who belong to those regions. This way, distributors can create sales orders (opportunities) using the price books that are relevant to their region.

Creating a lookup to Custom Price object and sharing with distributors (option A) is not a good solution, as it can introduce unnecessary complexity and redundancy to the data model. It is better to use standard objects and features that are designed for managing products and prices in Salesforce. Manually updating opportunities with prices applicable to distributors (option C) is also not a good solution, as it can be time-consuming, error-prone, and inefficient. It is better to use automation tools or features that can update prices based on predefined criteria or logic. Adding custom fields in opportunity and using triggers to update prices (option D) is also not a good solution, as it can be complex, costly, and difficult to maintain. It is better to use standard fields and features that can handle prices more effectively.

Integrating Salesforce with ERP and making ERP the system of truth (option B) and implementing a de-dupe solution and establishing account ownership in Salesforce (option D) are the two solutions that a data architect should recommend to resolve the complaints. Option B ensures that account data is consistent and accurate across both systems, while option D prevents duplicate records and clarifies who owns each account in Salesforce. Building a nightly batch job to de-dupe data and merge account records (option A) is not a good solution, as it does not address the root cause of the duplication and may result in data loss or conflicts. Building a nightly sync job from ERP to Salesforce (option C) is also not sufficient, as it does not prevent duplication or establish ownership in Salesforce.

Implementing a Contact Conference junction object with master detail relationship to both contact and conference_c is the correct way to model the relationship between the contact and conference objects, as it allows a many-to-many relationship between them. This means that each contact can attend multiple conferences, and each conference can be related to multiple contacts. Creating a master detail relationship field on either the contact or the conference object would create a one-to-many relationship, which does not meet the requirements. Creating a lookup relationship field on contact object would also create a one-to-many relationship, and would not enforce referential integrity.

The correct answer is A. To provide additional guidance for users when they enter information in a standard field, a data architect should provide custom help text under field properties. This will display a help icon next to the field label that users can hover over to see the help text. Option B is incorrect because creating a custom page with help text for user guidance will require additional development effort and may not be easily accessible by users. Option C is incorrect because adding custom help text in default value for the field will overwrite the actual default value of the field and may confuse users. Option D is incorrect because adding a label field with help text adjacent to the custom field will clutter the page layout and may not be visible to users.

Storing the data in a staging database and de-duplicating identical records (option D) is the best solution for a successful migration to mitigate the pain point, as it allows the data architect to identify and merge duplicate customers before they are imported into Salesforce. Hiring an intern manually de-duplicate the records after migrating to Salesforce (option A) is not a good solution, as it may be time-consuming and error-prone, and it does not prevent duplicate records from being created in Salesforce. Migrating the data as is and using Salesforce’s de-duplicating feature (option B) is also not a good solution, as it may cause data quality issues and conflicts, and it does not address the root cause of the duplication. Having the users manually clean the data in the old system prior to migration (option C) is also not a good solution, as it may be unrealistic and impractical, and it does not leverage any automated tools or processes.

  Master data management (MDM) is a solution that helps organizations manage their master data across multiple systems and ensure consistency and quality. An MDM solution can act as the customer master, with centralized integrations to other systems, to avoid data duplication and inconsistency.

Option D is correct because data flows should be reviewed with the business users to determine the system of record per object or field1. The system of record is the authoritative source of data for a given entity or attribute2. It may vary depending on the business context and requirements. Option A is not correct because account and opportunity data originates in Salesforce, but it may not be the system of record for all attributes or objects if they are updated by other systems2. Option B is not correct because whatever system updates the attribute or object may not be the system of record for that field or object if there are conflicting or overlapping updates from other systems2. Option C is not correct because whatever integration data flow runs last may not determine which system is the system of record if there are different business rules or logic applied by different systems2.

According to the Salesforce documentation, a hub-and-spoke model is an integration architecture pattern that allows connecting multiple Salesforce orgs using a central org (hub) and one or more secondary orgs (spokes). The hub org acts as the master data source and orchestrates the data flow between the spoke orgs. The spoke orgs act as the consumers or producers of the data and communicate with the hub org.

To orchestrate data between the hub org and spoke orgs, a data architect should use:

A middleware solution that extracts and distributes data across both the hub and spokes (option A). This means using an external service or tool that can connect to multiple Salesforce orgs using APIs or connectors, and perform data extraction, transformation, and distribution operations between the hub and spoke orgs. This can provide a scalable, flexible, and reliable way to orchestrate data across multiple orgs.

Developing custom APIs to poll the hub org for change data and push into the spoke orgs (option B) is not a good solution, as it can be complex, costly, and difficult to maintain. It may also not be able to handle large volumes of data or complex transformations efficiently. Developing custom APIs to poll the spoke orgs for change data and push into the hub org (option C) is also not a good solution, as it can have the same drawbacks as option B. It may also not be able to handle conflicts or errors effectively. Using a backup and archive solution that extracts and restores data across orgs (option D) is also not a good solution, as it can incur additional costs and dependencies. It may also not be able to handle real-time or near-real-time data orchestration requirements.

To recommend a clean migration, the data architect should set up a staging database, and define external IDs to merge, clean duplicate data, and load into Salesforce. This will allow the data architect to consolidate and deduplicate the data from two legacy systems before importing it into Salesforce using external IDs as unique identifiers. Option A is incorrect because defining external IDs for an object, migrating second database to first database, and loading into Salesforce will not ensure that the data is clean and duplicate-free. Option B is incorrect because defining duplicate rules in Salesforce, and loading data into Salesforce from both databases will not prevent the duplicate data from being imported into Salesforce. Option D is incorrect because defining external IDs for an object, inserting data from one database, and using upsert for a second database will not handle the duplicate data from both databases.

According to the Using Mashups article on Salesforce Developers, one of the techniques for deploying large data volumes is to use mashups to display historical records in Salesforce. The article states that “Mashups are a way to display data from an external system within a Salesforce page without copying or synchronizing the data. Mashups use a combination of Visualforce, Apex callouts, and JavaScript code that runs in the browser. Mashups are useful when you want to display large amounts of read-only data that is stored outside of Salesforce.” Therefore, a data architect should recommend this solution to meet the business requirement of UC.

The correct answer is A. To migrate the data to Salesforce, the data architect should create custom objects to store agent relationships. This will allow the data architect to replicate the look-up table structure from the legacy CRM system and maintain the relationship data in Salesforce. Option B is incorrect because migrating to Salesforce without a record owner will cause errors and prevent the data from being imported. Option C is incorrect because assigning record owner based on relationship will not preserve the agent relationships from the legacy CRM system. Option D is incorrect because migrating the data and assigning to a non-person system user will not allow the users to access and modify the data.

The correct answer is A, D, and E. The data architect should consider these three factors when evaluating the need for Salesforce Connect: You want real-time access to the latest data from other systems, you have a large amount of data that you don’t want to copy into your Salesforce org, and you need to small amounts of external data at any one time. These factors indicate that Salesforce Connect is a suitable solution for creating a connected experience in Salesforce without importing inventory data from a cloud database. Salesforce Connect allows Salesforce to access external data via OData or custom adapters without storing it in Salesforce, which reduces storage costs and ensures data freshness. Salesforce Connect also supports pagination and caching to optimize performance when accessing small amounts of external data at any one time. Option B is incorrect because if you have a large amount of data and would like to copy subsets of it into Salesforce, you may not need Salesforce Connect but rather use other tools such as Data Loader or API integration. Option C is incorrect because if you need to expose data via a virtual private connection, you may not need Salesforce Connect but rather use other tools such as VPN or VPC peering.

According to this article, person accounts are designed to store information about individual people by combining certain account and contact fields into a single record. This is suitable for a large B2C retailer that needs to model the consumer account structure in Salesforce.

 Using a validation rule is the best way to ensure that all customers provide either a phone number or an email address when registering for an account, as it allows defining a formula that evaluates whether a record meets certain criteria. Using required fields may not work if both phone number and email address are required. Using an Apex class or a Process Builder may be more complex or cumbersome than using a validation rule.

Virtualizing the remaining set of data with salesforce connect and external objects is the best way to meet the requirements with minimal effort, as it allows salesforce to access data stored in Heroku Postgres without storing it in salesforce. This reduces the storage limitations and avoids data duplication. Heroku connect can bi-directionally sync data between systems, but it requires more configuration and maintenance. Making callouts to Heroku Postgres and persisting the data in salesforce may not be feasible for 1 billion records. Migrating the data to big objects may incur additional costs and require custom code to use async SOQL

A master-detail field on VTOTime__c to Project__c is the data relationship that the data architect should use to support the requirement when creating the custom VTOTime__c object. A master-detail relationship creates a parent-child relationship between two objects, where the master record controls certain behaviors of the detail record, such as security, ownership, deletion, and roll-up summary fields. By using a master-detail field on VTOTime__c to Project__c, you can ensure that the ability to view or edit the VTOTime__c object is the same as the Project__c record, and that managers can see the total hours volunteered while viewing the Project__c record using a roll-up summary field.

Checking if the current user is NOT the owner by creating a “before delete” trigger can meet the requirement of allowing only the owner of a contact record to delete that contact. A trigger is a piece of Apex code that can execute before or after a record is inserted, updated, deleted, or undeleted. A “before delete” trigger can prevent the deletion of a record by using the addError() method.

Importing Account records first followed by order records after sorting orders by AccountID is the recommended way to avoid locking issues during import. This can reduce the number of lock contention errors by minimizing the number of parent records that are concurrently processed by multiple batches. Sorting orders by AccountID can also group the child records by their parent records and avoid updating the same parent record in different batches3. Importing Account records first followed by order records after sorting order by OrderID will not help with avoiding locking issues because it does not group the child records by their parent records. Changing the relationship to Lookup and updating the relationship to master-detail after import will not work because changing a relationship from Lookup to master-detail requires that all child records have a parent record, which may not be the case after import. Importing Order records and Account records separately and populating AccountID in orders using batch Apex will not help with avoiding locking issues because it still requires updating the parent records in batches.

The data architect should recommend using field metadata attributes for compliance categorization, data owner, and data sensitivity level. This will allow the SF admins to generate reports to manage compliance based on the field metadata attributes that are defined for each data element in SF. Option A is incorrect because using metadata API to extract field attribute information and use the extract to classify and build reports will require additional development effort and may not be up-to-date with the latest changes in SF. Option C is incorrect because creating a custom object and field to capture necessary compliance information and build custom reports will require additional configuration effort and may not be consistent with the actual data elements in SF. Option D is incorrect because building reports for field information, then exporting the information to classify and report for audits will require additional manual effort and may not be accurate or timely.

Ordering batches by Auto-number field will ensure that the records are processed in a sequential order and avoid any locking issues that might occur when loading related records in parallel mode. Creating indexes, grouping batches by AccountId, or sorting batches by Name field values are not necessary or beneficial for loading Opportunity records using the Bulk API.

The correct answer is C, migrate Notes and Attachment to Enhanced Notes and Files using a migration tool. Enhanced Notes and Files are the new features in Lightning Experience that replace the classic Notes and Attachments. They offer more functionality and security than the classic version. To access them in Lightning Experience, you need to migrate your existing Notes and Attachments using a migration tool provided by Salesforce. Adding Notes and Attachments Related List, manually uploading Notes or Attachments, or doing nothing are not valid solutions, as they will not enable you to use the enhanced features in Lightning Experience.

Creating a custom lookup field on the standard Product to reference the parent record in the hierarchy is the correct way to modify the standard Product object model to support a hierarchical data structure. This allows UC to create a self-relationship on the Product object and define parent-child relationships among products.

Utilizing an app from the AppExchange to create data-quality dashboards is the approach that the architect should recommend for UC to implement a data-quality process to monitor the data that users are manually entering into the system through the Salesforce UI. An app from the AppExchange can provide ready-made or customizable dashboards that can display various metrics and indicators of data quality, such as completeness, accuracy, consistency, timeliness, etc. These dashboards can help UC to measure and evaluate their data quality performance and identify any areas that need improvement or attention. The other options are not relevant or effective for implementing a data-quality process, as they would either not address the issue of manual data entry, not provide data-quality monitoring, or not leverage the benefits of Salesforce.

Lightning Platform Plus is the license type that best fits UC’s requirements, as it allows up to 50 custom objects and 40,000 API calls per user per 24-hour period4. Service Cloud does not provide enough custom objects or API calls. Lightning Platform Start only allows up to 10 custom objects and 5,000 API calls per user per 24-hour period. Lightning External Apps Start is for external users and does not provide enough API calls.

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

According to the Salesforce documentation1, reporting performance can be affected by various factors, such as the volume and complexity of data, the design and configuration of reports and dashboards, the number and type of users accessing the reports, etc. To improve reporting performance, some of the recommended solutions are:

Move data off of the platform and run reporting outside Salesforce, and give access to reports (option C). This means using an external service or tool that can extract, transform, and load (ETL) data from Salesforce to another system or database, such as a data warehouse or a business intelligence platform. This can improve reporting performance by reducing the load and latency on Salesforce, and enabling faster and more flexible reporting and analysis on the external system. Users can access the reports from the external system using a link or an embedded component in Salesforce.

Contact Salesforce support to enable skinny table for cases (option B). This means requesting Salesforce to create a custom table that contains a subset of fields from the Case object that are frequently used or queried. A skinny table can improve reporting performance by avoiding joins between standard and custom fields, omitting soft-deleted records, and leveraging indexes on the fields2.

Create a custom object to store aggregate data and run reports (option A). This means creating a custom object that contains summary or calculated data from the Case object, such as counts, sums, averages, etc. A custom object can improve reporting performance by reducing the number of records and fields that need to be queried and displayed.

Build reports using custom Lightning components (option D). This means creating custom components that use Lightning Web Components or Aura Components frameworks to display report data in Salesforce. A custom component can improve reporting performance by using client-side caching, pagination, lazy loading, or other techniques to optimize data rendering and interaction.

 Exporting the setup audit trail can provide information about who is creating, changing, or deleting certain fields within the past four months. The setup audit trail tracks the recent setup changes that administrators and other users have made to the organization. The setup audit trail history shows up to 20 most recent changes in the Setup area, but administrators can download a report (in CSV format) of up to six months of setup history.

Evaluating a third-party AppExchange app, such as OwnBackup or Spanning, etc., for backup and archival purposes is the best approach to address the requirement of having a one-day SLA for any data loss. These apps can provide automated backups, granular restores, and compliance features that can help UC recover from any data loss or corruption scenarios.

According to the official Salesforce guide1, big objects are designed to store and manage massive data volumes within Salesforce without affecting performance. They can be queried by using Async SOQL or standard SOQL, and they can be accessed by using Apex, Visualforce, Lightning components, or APIs. Big objects are ideal for storing data that is used for analytics or reporting purposes, such as the reward program data. Option A is the correct answer because it allows NTO to create a custom big object in Salesforce that can store the reward program data and make it available for consumption by Einstein Analytics and Discovery. Option B is incorrect because Einstein cannot connect directly to the point of sales system to capture the reward program data. Option C is incorrect because Einstein Analytics does not support creating big objects. Option D is incorrect because custom objects are not suitable for storing large volumes of data.

Creating customers as Contacts and relating them to Business and Residential Accounts using the Account Contact Relationships (option A) is the best option to recommend for a single view of these customers without creating multiple customer records, as it allows the data architect to model complex relationships between customers and accounts using native Salesforce features and tools. Creating customers as Person Accounts and relating them to Business and Residential Accounts using the Account Contact relationship (option B) is not a good option, as it may create data redundancy and inconsistency, and it does not leverage the existing Contact object. Creating customers as individual objects and relating them with Accounts for Business and Residence accounts (option C) is also not a good option, as it may require more customization and maintenance effort, and it does not leverage the existing Account and Contact objects. Creating customers as Accounts for Residence Account and using Parent Account to relate Business Account (option D) is also not a good option, as it may create confusion and complexity with the account hierarchy, and it does not leverage the existing Contact object.

According to this article, inserting 1 million orders distributed across a variety of accounts with potential lock exceptions is a scenario where using the Bulk API in serial mode can help to prevent the lock contention issue that can occur in parallel mode.

 Event Monitoring is a feature that allows you to track user actions, such as logins, logouts, downloads, etc., in your Salesforce org. You can use Event Monitoring to monitor performance, usage, security, and compliance

Creating a custom object to store a copy of the record when changed and creating a custom object to store the previous and new field values are two possible actions that an architect can perform to meet the requirement of tracking field history on 100 Opportunity fields. A custom object can store more fields and records than the standard field history tracking feature, which has a limit of 20 fields per object and 18 or 24 months of data retention. A custom object can also be used for reporting and analysis of field history data. The other options are not feasible or effective for meeting the requirement

Using an ETL tool to orchestrate the migration is the best strategy for this scenario, as it can handle the data model changes, the large volume of records, and the tight timeline. Writing a script to use the Bulk API (option C) is also possible, but it would require more coding and testing effort. Using Data Loader and Data Import Wizard (option B) is not suitable for migrating three million records, as they have limitations on the batch size and the number of records per operation. Using Salesforce CLI (option D) is also not recommended for large data migration, as it is mainly designed for development and testing purposes

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.

According to the Salesforce documentation1, data quality is the measure of how well the data in Salesforce meets the expectations and requirements of the users and stakeholders. Data quality can be affected by various factors, such as data entry errors, data duplication, data inconsistency, data incompleteness, data timeliness, etc. To mitigate data quality issues, some of the recommended solutions are:

Leverage picklist and lookup fields where possible (option A). This means using fields that restrict the values or references that can be entered by the users or integrations. This can help reduce data entry errors, enforce data consistency, and improve data accuracy.

Leverage validation rules and workflows (option C). This means using features that allow defining rules and criteria to validate the data that is entered or updated by the users or integrations. This can help prevent invalid or incorrect data from being saved, and trigger actions or alerts to correct or improve the data.

Leveraging Apex to validate the format of data being entered via a mobile device (option B) is not a good solution, as it can be complex, costly, and difficult to maintain. It is better to use standard features or declarative tools that can handle data validation more effectively. Leveraging third-party AppExchange tools (option D) is also not a good solution, as it can incur additional costs and dependencies. It is better to use native Salesforce features or custom solutions that can handle data quality more efficiently.

 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.

 A sound data archiving strategy is essential for ensuring scalability and performance of any application that deals with large volumes of data. You need to consider factors like data retention policies, storage limits, backup and recovery options, and data access requirements when designing a data archiving solution2. You can use tools like OwnBackup, Odaseva, or ArchiveIt to archive your data from Salesforce to external systems

Option B is correct because data model is a key artifact that an architect should use to document the data architecture for a multi-system, enterprise Salesforce implementation1. Data model describes the structure and relationship of data entities within an organization2. Option C is correct because integration specification is another key artifact that an architect should use to document the data architecture for a multi-system, enterprise Salesforce implementation1. Integration specification defines the scope, requirements, design, testing, and deployment of integration solutions between Salesforce and other systems3. Option A is not correct because user stories are not key artifacts for documenting the data architecture, but agile development tools that capture the features and functionalities that users want from a system4. Option D is not correct because non-functional requirements are not key artifacts for documenting the data architecture, but quality attributes that specify how well a system performs its functions.

Creating a report in an external system combining Salesforce Account data and Invoice data from the ERP, and creating a Visualforce page combining Salesforce Account data and Invoice external object data are two approaches that an architect can suggest for achieving this solution. Both of these approaches can display data from both Accounts and Invoices in one place, using either an external reporting tool or a custom web page. Creating a report combining data from the Account standard object and the Invoices external object is not possible because Salesforce does not support reporting on external objects. Creating a separate Salesforce report for Accounts and Invoices and combining them in a dashboard is not ideal because it will not show the data in one report, but rather in two separate components.