Pre-processing the data means transforming and cleansing the data before loading it into Salesforce. This can reduce the errors and conflicts that might occur during the data load. Using Data Loader with SOAP API to upsert with zip compression enabled can also improve the performance and efficiency of the data load by reducing the network bandwidth and avoiding duplication

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

Creating an external object Invoice_x with a Lookup relationship with Opportunity is the best option for modeling invoices, considering performance and data storage space. An external object allows the data to be stored in the ERP system and accessed via OData in Salesforce. This reduces the data storage consumption in Salesforce and improves the performance of queries and reports. A Lookup relationship allows the sales reps to view the invoice status and amount from the opportunity page. The other options would either consume more data storage space, require additional customization, or not provide real-time data access

 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

The correct answer is A, global unique customer number. A global unique customer number is a data element that can uniquely identify each customer across different systems. It can be used as a key to link customer records from different sources and enable reporting across these systems. Email address, phone number, and full name are not reliable or consistent identifiers for customers, as they can change over time or be shared by multiple customers.

The data classification feature allows customers to classify their data in Salesforce based on two policies:

Data sensitivity level: This policy defines how sensitive the data is and what level of protection it requires. For example, high sensitivity data may require encryption or masking.

Compliance categorization policy: This policy defines how the data is regulated by various laws and standards. For example, GDPR or PCI DSS.

The recommended solution to ensure long-term performance for the customer categorization process is to implement a new picklist custom field in the Account object with Gold, Silver, and Bronze values. A picklist field is a simple and efficient way to store a predefined set of values for a record. A picklist field has the following benefits:

It allows you to easily filter, sort, and group records by their category values, which can help you analyze and segment your customers.

It does not require creating any additional objects or relationships, which can reduce the data model complexity and maintenance overhead.

It supports large data volumes and can handle millions of records without affecting performance or scalability.

A required Lookup relationship ensures that the Shipment record must have a value for each of the three parent records, and also prevents the deletion of those parent records if they are referenced by a Shipment record. A Master-Detail relationship would not allow separate sharing models for each custom object, and a VLOOKUP formula field would not enforce the relationship or prevent deletion

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.

Creating a trigger on Contact and adding an error to the record with a custom label allows the architect to use the translation workbench to translate the error message based on the user’s language. Creating a validation rule and translating the error message with translation workbench also achieves the same result. The other options would either not provide translation functionality or not display an error message

To improve the performance of SOQL queries that retrieve data from the Order_c object, the data architect should work with Salesforce Support to enable Skinny Tables and make the queries more selective using indexed fields. Skinny Tables are custom tables that contain frequently used fields and are kept in sync with the base tables3. They can improve performance by reducing the number of table joins and using indexes. Making the queries more selective using indexed fields can also improve performance by reducing the query execution time and avoiding query timeouts4. The other options are not effective or recommended for improving SOQL performance.

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.

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.

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.

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

Making use of a third-party tool to help merge duplicate Contacts across Accounts and using Data.com to standardize Contact address information to help identify duplicates are two solutions that UC should implement to cleanse their data and have a single Contact across departments. A third-party tool, such as an app from the AppExchange, can provide advanced features and capabilities for finding and merging duplicate Contacts across different Accounts, based on various criteria and rules. Data.com can provide address verification and standardization services that can enhance the quality and consistency of Contact address information and make it easier to identify duplicates. The other options are not feasible or effective for cleansing the data, as they would either not work across different Accounts, not address the root cause of duplication, or not provide sufficient functionality for merging duplicates

The correct answer is B, use data classification metadata fields available in field definition. Data classification metadata fields are standard fields that allow you to classify the sensitivity level of your data based on your organization’s policies. You can use these fields to indicate whether a field contains confidential, restricted, or general data. These fields are available for both standard and custom objects in Salesforce. Using app exchange products, creating a custom picklist field, or building reports would not be as effective or consistent as using data classification metadata fields.

 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.

According to the Salesforce documentation1, the query plan tool is a tool that can be enabled and used in the developer console to analyze the performance of a SOQL query. The query plan tool shows the cost, cardinality, sObject type, and relative cost of each query plan that Salesforce considers for a query. The relative cost indicates how expensive a query plan is compared to the Force.com query optimizer threshold. A query plan with a relative cost above 1.0 is likely to cause a time-out error.

To identify why the query is taking too long, a data architect should use the query plan tool in the developer console (option B). This way, the data architect can see which query plan is chosen by Salesforce and how it affects the performance of the query. The data architect can also use the query plan tool to optimize the query by adding indexes, filters, or limits to reduce the cost and improve the efficiency of the query.

Using Splunk to query the system logs looking for transaction time and CPU usage (option A) is not a good solution, as it can be complex, costly, and difficult to integrate with Salesforce. It may also not provide enough information or insights to identify and optimize the query performance. Using Salesforce’s query optimizer to analyze the query in the developer console (option C) is also not a good solution, as it is not a separate tool that can be used in the developer console. The query optimizer is a feature that runs automatically when a SOQL query is executed and chooses the best query plan based on various factors2. Opening a ticket with Salesforce support to retrieve transaction logs to be analyzed for processing time (option D) is also not a good solution, as it can be time-consuming, dependent, and inefficient. It may also not provide enough information or insights to identify and optimize the query performance.

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.

The Individual object is a standard object that lets you store details about data privacy and protection preferences for person accounts, contacts, users, and leads. The Individual object can be used to implement a standard communication preference management model for NTO, with relationships to the other objects.

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.

The two solutions that a data architect should recommend to preserve the date timestamps and ownership on records being migrated are:

Enable update records with Inactive Owners Permission: This permission allows users to update record owner and sharing-based records with inactive owners. This can help preserve the original ownership of records that belong to users who are no longer active in Salesforce8.

Enable Set Audit fields upon Record Creation Permission: This permission allows users to set audit fields (such as Created By or Last Modified By) when they create a record via API importing tools like Data Loader. This can help preserve the original date timestamps of records that were created or modified in another system9.

Log a case with SF to update these fields is not a good solution because it is not necessary or feasible to ask Salesforce support to update these fields manually or programmatically. Enable modify all and view all permission is not a good solution because it does not affect the ability to preserve the date timestamps and ownership on records, but rather grants users access to all records regardless of sharing settings

Persistence is the data pattern that will allow a data architect to satisfy the requirements, while also keeping limits in mind. Persistence means storing data from external sources in Salesforce objects, either standard or custom. This allows you to access the data within Salesforce and use it for reporting, analytics, workflows, and other features. Persistence also helps you avoid hitting API limits or performance issues when accessing large volumes of data from external systems. You can use various tools such as Data Loader, Bulk API, or Platform Events to persist IoT data from connected devices to a cloud database in Salesforce.

 According to this article, big objects can store and manage massive amounts of data on the Salesforce platform. This can help UC to archive records from other objects or bring massive datasets from outside systems into a big object. According to this article, Einstein Analytics can be used to create dashboards and lenses to analyze big object data and get insights from advanced AI-driven analytics.

The two best practices that should be followed when using SOSL for searching are: Keep searches specific and avoid wildcards where possible. Use Find in “ALL FIELDS” for faster searches. These best practices are helpful because they optimize the search performance and relevance. For example, keeping searches specific and avoiding wildcards where possible can reduce the number of results returned and improve the search accuracy. Using Find in “ALL FIELDS” for faster searches can leverage Salesforce’s pre-defined indexes on common fields and return results more quickly than specifying individual fields.

Before replicating the data from Heroku Postgres to Salesforce, the data architect should consider the following factors:

Whether the data is a driver of key processes implemented within Salesforce. For example, if the data is used for workflows, triggers, or validation rules, it should be replicated to Salesforce.

Whether there is a tight relationship between order data and an enterprise resource planning (ERP) application. For example, if the order data needs to be synchronized with the ERP system, it should be replicated to Salesforce.

The selection of the tool required to replicate the data. For example, Heroku Connect can be used to bi-directionally sync data between Heroku Postgres and Salesforce

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.

Implementing a Master Data Management strategy to reconcile Leads, Accounts, and Contacts is the approach that the architect should suggest so that the requirements are met for UC. A Master Data Management strategy can provide a consistent and unified view of data across multiple systems, by defining and enforcing rules for data quality, governance, and integration. A Master Data Management strategy can also handle complex scenarios where different systems have different systems of record for different entities or fields, and where data can be modified in different systems with different values. A Master Data Management strategy can also provide a consolidated data store that links referenced records across systems and enables cross-system reporting and analysis. The other options are not suitable or sufficient for meeting the requirements, as they would either not provide a consolidated data store, not handle different systems of record, or not allow data modification in different system

Reviewing data quality reports and dashboards and installing and running data quality analysis dashboard app are two solutions that can help monitor and measure data quality. Data quality reports and dashboards can provide insights into the completeness, accuracy, and consistency of the data. Data quality analysis dashboard app is a free app from AppExchange that can help analyze and improve data quality by identifying duplicate, incomplete, or inaccurate records.

Option A is correct because integrating Salesforce with the ERP environment is a technique to maintain data synchronization between systems1. Option C is correct because utilizing an MDM strategy to outline a single source of truth is another technique to ensure data quality and consistency across systems2. Option B is not correct because utilizing workbench to update files within systems is not a technique to maintain data synchronization, but a tool to perform data manipulation tasks3. Option D is not correct because building synchronization reports and dashboards is not a technique to maintain data synchronization, but a way to monitor and analyze data4.

 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

 Changing the master-detail relationship to a lookup relationship is the best solution for allowing the creation of generic discount requests without the custom product object as its master record. A lookup relationship allows you to create child records without requiring a parent record. It also gives you more flexibility in defining the sharing and security settings for each object

The Sforce-Enable-PKChunking header enables you to extract large data sets from Salesforce by using a technique called primary key (PK) chunking. PK chunking splits your query into multiple queries based on the record IDs of the queried object. This reduces the query time and avoids the query timeout issues that can occur with large data volumes

Using a declarative approach by installing and configuring Data.com Clean to monitor Account data quality can help UC meet their requirement. Data.com Clean can enrich and cleanse data on an ongoing basis by comparing Salesforce records with Data.com records and providing suggestions for updates. Data.com Clean can also provide dashboards and reports to monitor key data quality metrics such as match rate, field fill rate, and record completeness

 According to this article, migrating the legacy data leveraging an ETL tool to transform data types and load data into Salesforce can be a way to ensure successful data migrations. The article states that ETL tools can help with data cleansing, mapping, transformation, and loading, and that they can handle different data types and formats. The article also provides some best practices for data migration, such as identifying the data to migrate, creating templates for the data, preparing the destination org, and validating the data.

 Creating the users, uploading all data, and then deploying the sharing rules will reduce the number of sharing recalculations that occur during the data load. Deferring sharing calculations until the data has finished uploading will also improve the performance by postponing the sharing rule evaluation. These are the recommended best practices for loading large data sets into Salesforce

According to the Salesforce documentation2, duplicate leads are leads that have the same or similar information as other leads in Salesforce. Duplicate leads can cause poor customer experience, higher costs, and inaccurate reporting. To mitigate the issues caused by duplicate leads, some of the recommended practices are:

Standardize lead information across all channels (option B). This means using consistent formats, values, and fields for capturing lead data from different sources, such as web forms, email campaigns, or third-party vendors. This can help reduce data quality issues and make it easier to identify and prevent duplicate leads.

Implement a third-party solution to clean and enrich lead data (option D). This means using an external service or tool that can validate, correct, update, and enhance lead data before or after importing it into Salesforce. This can help improve data quality and accuracy, and reduce duplicate leads.

Implement a de-duplication strategy to prevent duplicate leads (option E). This means using Salesforce features or custom solutions that can detect and block duplicate leads from being created or imported into Salesforce. For example, using Data.com Duplicate Management3, which allows defining matching rules and duplicate rules for leads and other objects.

Building a process to manually search and merge duplicates (option A) is not a good practice, as it can be time-consuming, error-prone, and inefficient. Building a custom solution to identify and merge duplicate leads (option C) is also not a good practice, as it can be complex, costly, and difficult to maintain. It is better to use existing Salesforce features or third-party solutions that can handle duplicate leads more effectively.

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 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.

The data architect should choose to implement duplicate rules in SF (Salesforce) to validate duplicates during the account creation process. Duplicate rules are a feature in Salesforce that allow users to define criteria and actions for detecting and preventing duplicate records. By creating duplicate rules for accounts, the data architect can ensure that any leads from the website or customers from the retail stores that match existing accounts in Salesforce are flagged or blocked before they are created as new accounts. This will help avoid duplicate accounts in Salesforce and maintain data quality. Option B is incorrect because implementing a MDM (Master Data Management) solution to validate the customer information before creating accounts in SF will require additional infrastructure cost and maintenance effort. Option C is incorrect because building custom search based on fields on accounts which can be matched with customer when they visit the store will require additional development effort and may not be accurate or user-friendly. Option D is incorrect because customizing account creation process to search if customer exists before creating an account will require additional configuration effort and may not be consistent or scalable.

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.

According to the Salesforce documentation3, 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 address the issues with data quality that affect the reliability of executive dashboards, a data architect should recommend:

Building a sales data warehouse with purpose-built data marts for dashboards and senior management reporting (option B). This means creating a separate database or system that stores and organizes sales data from Salesforce and other sources for analytical purposes. A data warehouse can provide a single source of truth for sales data and enable faster and more accurate reporting and analysis. A data mart is a subset of a data warehouse that focuses on a specific subject or business area, such as sales performance, customer segmentation, product profitability, etc. A data mart can provide tailored and relevant data for different users or groups based on their needs and interests.

Exploring third-party data providers to enrich and augment information entered in Salesforce (option C). This means using external services or tools that can validate, correct, update, and enhance the data that is entered or imported into Salesforce. This can help improve data quality and accuracy, and reduce data duplication and incompleteness.

Leveraging Salesforce features, such as validation rules, to avoid incomplete and incorrect records (option D). 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.

Periodically exporting data to cleanse data and import them back into Salesforce for executive reports (option A) is not a good solution, as it can be time-consuming, error-prone, and inefficient. It may also cause data inconsistency and synchronization issues between Salesforce and other systems. Designing and implementing data-quality dashboard to monitor and act on records that are incomplete or incorrect (option E) is also not a good solution, as it can be complex, costly, and difficult to maintain. It may also not address the root causes of data quality issues or prevent them from occurring in the first place.

 According to the Salesforce documentation1, ownership skew occurs when a large number of records (more than 10,000) are owned by a single user or queue. This can cause performance issues and lock contention when multiple users try to access or update those records. To prevent ownership skew, some of the recommended practices are:

Assign a default user as the owner of the records and do not assign any role to the default user (option C). This way, the records will not be visible to other users in the role hierarchy and will not cause sharing recalculations.

Keep users out of public groups that can be used as the source for sharing rules (option B). Sharing rules based on public groups can cause excessive sharing calculations and lock contention when many records are owned by a single user or queue.

Assign a default user as the owner of the records and assign the top most role in the hierarchy to the default user (option E). This way, the records will be visible to all users in the role hierarchy, but will not cause sharing recalculations or lock contention.

Assigning a default user as the owner of the records and assigning a role in the hierarchy (option A) is not a good practice, as it can cause sharing recalculations and lock contention when the role is updated or moved. Assigning “view all” permission on profile to give access to the records (option D) is also not a good practice, as it can bypass the security and sharing model and expose sensitive data to unauthorized users.

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.

Having each system pull the record changes from Salesforce using change data capture (option D) is the correct solution for ensuring all systems using customer data are kept up to date, as it allows the systems to subscribe to real-time events from Salesforce and receive notifications when customer records are created, updated, deleted, or undeleted. Sending customer data nightly to the system of truth in a scheduled batch job (option A) or sending customer record changes from Salesforce to each system in a nightly batch job (option B) are not good solutions, as they may cause data latency and inconsistency, and they do not provide real-time updates. Sending customer record changes from Salesforce to the system of truth in real time (option C) is also not a good solution, as it does not address how the other systems will receive the updates from the system of truth.

Cleaning data before importing to Salesforce and using Salesforce matching and duplicate rules are two design considerations that an architect should take to minimize data duplication when migrating legacy account data from two aging systems into Salesforce. Cleaning data before importing involves removing or correcting any inaccurate, incomplete, or inconsistent data from the source systems, as well as identifying and resolving any potential duplicates. This ensures that only high-quality and unique data is imported to Salesforce. Using Salesforce matching and duplicate rules allows the architect to define how Salesforce identifies duplicate records during import and how users can handle them. This prevents or reduces the creation of duplicate records in Salesforce and improves data quality. The other options are not effective or recommended for minimizing data duplication.

 Option C is the best answer because reviewing projected data storage needs is an important step in designing a field-level audit and recovery plan1. You need to estimate how much data storage you will need in the future and plan accordingly. Option A is not correct because reducing data storage by purging old data may not be sufficient or desirable for backup purposes. Option B is not correct because implementing an AppExchange package may not be customized or compatible with your org’s requirements. Option D is not correct because scheduling a weekly export file may not be frequent or granular enough for field-level audit and recovery.

According to the Salesforce documentation2, extracting large amounts of data from Salesforce can be challenging and time-consuming, as it can encounter performance issues, API limits, timeouts, etc. To extract 100 million records from Salesforce for migration to an enterprise data warehouse (EDW), a data extraction strategy that can provide maximum performance is:

Utilize PK Chunking with the Bulk API (option C). This means using a feature that allows splitting a large query into smaller batches based on the record IDs (primary keys) of the queried object. This can improve performance and avoid timeouts by processing each batch asynchronously and in parallel using the Bulk API3.

Installing a third-party AppExchange tool (option A) is not a good solution, as it can incur additional costs and dependencies. It may also not be able to handle such a large volume of data efficiently. Calling the REST API in successive queries (option B) is also not a good solution, as it can encounter API limits and performance issues when querying such a large volume of data. Using the Bulk API in parallel mode (option D) is also not a good solution, as it can still cause timeouts and errors when querying such a large volume of data without chunking.

Integrating with a third-party database or service for address validation and enrichment is a scalable strategy that can improve the quality of the Account addresses by comparing them with a reliable source of data1. Leveraging Data.com Clean to clean up Account address fields with the D&B database is another scalable strategy that can automatically update and enrich Account records with verified information from Data.com2.

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

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.

 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

 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.

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

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.

The best solution to make sure decrypted values are exported during data export is to create another field to copy data from encrypted field and use this field in export. This is because classic encryption does not support exporting decrypted values of encrypted fields. The view encrypted data permission only allows users to view decrypted values in the user interface, but not in reports or data exports. Therefore, a workaround is to create a formula field or a workflow field update that copies the value of the encrypted field to another field, and use that field for data export. However, this solution has some drawbacks, such as exposing sensitive data in plain text and consuming extra storage space. A better solution would be to use Shield Platform Encryption, which supports exporting decrypted values of encrypted fields with the Export Encrypted Data permission

Defining data duplication standards and rules, measuring data timeliness and consistency, and measuring data completeness and accuracy are three key factors that a data architect should consider while defining data quality standards. Defining data duplication standards and rules can help prevent or reduce duplicate records in the org by specifying criteria and actions for identifying and merging duplicates. Measuring data timeliness and consistency can help ensure that the data is up-to-date, reliable, and synchronized across different sources. Measuring data completeness and accuracy can help ensure that the data is sufficient, relevant, and correct for the intended purposes.

According to the exam guide, one of the objectives is to "describe the use cases and considerations for deferring sharing calculations"1. This implies that option A is the correct way to load large amounts of data into Salesforce without affecting performance and data integrity. Deferring sharing calculations allows the data to be loaded first and then the sharing rules to be applied later2. Option B is not correct because workflows are not recommended for calculating summary values, as they can cause performance issues and data skew3. Option C is not correct because triggers are also not recommended for calculating summary values, as they can cause governor limit errors and data inconsistency. Option D is not correct because external IDs are used to link records from different systems, not to improve data loading performance.

Using the Consent Management Feature to validate consent provided under the person Account and Contact that is provided by the customer (option C) is the best option to implement these requirements, as it allows UC to store and manage consent preferences for Person Accounts and Contacts using native Salesforce features and tools. Configuring custom fields in Person Account and Contact to store consent provided by customers and validate consent against the fields (option A) or building Custom object to store consent information in Person Account and Contact and validate against this object before contacting customers (option B) are not good options, as they may require more customization and maintenance effort, and they do not leverage the existing Data Protection and Privacy feature. Deleting contact information from customers who have declined consent to be contacted (option D) is also not a good option, as it may cause data loss or compliance issues, and it does not allow UC to track or update consent preferences.

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.

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.

 Using validation rules to mitigate this problem is the best option. Validation rules can help you prevent invalid data from being entered in your web order form. You can use validation rules to check for data quality, format, range, or consistency. For example, you can use validation rules to ensure that the order quantity is positive, the product code is valid, or the shipping address is complete.

According to the Salesforce documentation, SOQL is a query language that allows querying data from Salesforce objects and fields. SOQL queries have various clauses and operators that can be used to filter and sort the results. However, some clauses and operators can affect the performance of SOQL queries by increasing the cost or complexity of executing them.

To make this query more performant, a data architect should change the where clause to filter by a deterministic defined value (option D). This means using a filter condition that specifies a concrete value or range of values for a field, such as WhatId = ‘001xx000003DGg3’ or WhatId IN (‘001xx000003DGg3’, ‘001xx000003DGg4’). This can improve the performance of the query by reducing the number of records that need to be scanned and returned. A deterministic defined value can also leverage an index on the field, which can speed up the query execution.

Removing description from the requested field set (option A) is not a good solution, as it can affect the functionality or usability of the query. The description field may contain important or relevant information that is needed for the process. Changing the query to SOSL (option B) is also not a good solution, as SOSL is a different query language that allows searching text fields across multiple objects. SOSL queries have different syntax and limitations than SOQL queries, and may not return the same results or performance. Adding limit 100 to the query (option C) is also not a good solution, as it can affect the completeness or accuracy of the query. The limit clause specifies the maximum number of records that can be returned by the query, which may not include all the records that match the filter condition.

The correct answer is B. To make changes efficiently, a data architect should enable Defer Sharing Calculation prior to making sharing rule changes. This will allow the administrator to make multiple changes to sharing rules without recalculating them after each change, which can take a long time and cause failures. The sharing rules can be recalculated later when there are fewer users online or during off-peak hours. Option A is incorrect because logging a case with salesforce to make sharing rule changes will not speed up the process or prevent failures. Option C is incorrect because deleting old sharing rules and building new sharing rules will not reduce the processing time or failure rate. Option D is incorrect because logging out all users and making changes to sharing rules will disrupt the business operations and may not be feasible.

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 best solution to resolve the error of “Unable to lock a row” during the account load is to perform batch job in serial mode and reduce batch size. This is because roll-up summary fields are calculated synchronously when the parent record is updated, and asynchronously when the child record is updated. Therefore, updating many child records at once can cause locking issues on the parent record. To avoid this, it is recommended to use serial mode and smaller batch sizes when loading data using tools like Data Loader or Bulk API12. Leverage data loader platform API to load data is not a good option because it does not specify the mode or batch size. Perform batch job in parallel mode and reduce batch size is not a good option because parallel mode can still cause locking issues even with smaller batches. Defer roll-up summary fields calculation during data migration is not a good option because it is not possible to defer or disable roll-up summary fields calculation

Analytics/BI Owners and Data Domain Stewards are appropriate roles as members of a team that makes decisions on enterprise-wide data governance. Analytics/BI Owners are responsible for defining the business requirements and metrics for data analysis and reporting, and Data Domain Stewards are responsible for defining and enforcing the data quality standards and rules for specific data domains. Salesforce Administrators and Operational Data Users are not suitable roles for this team, as they are more focused on the operational aspects of data management, such as configuration, maintenance, and usage.

Indexing key fields used in report criteria can speed up the query execution and reduce the report run time. Indexes can be created by Salesforce automatically or manually by request. Creating one skinny table per report can also improve the reporting performance by storing frequently used fields in a separate table that does not include complex formulas or joins.

Implementing duplicate rules (option D) is the best solution to remediate the duplication issue, as it allows the data architect to identify and merge duplicate accounts in Salesforce using native features and tools. Developing an ETL process that utilizes the merge API to merge the duplicate records (option A) is not a good solution, as it may require more coding and testing effort, and it does not prevent duplicates from being created in Salesforce. Utilizing a data warehouse as the system of truth (option B) is also not a good solution, as it may introduce additional complexity and cost, and it does not address the duplication issue in Salesforce. Extracting the data using data loader and using excel to merge the duplicate records (option C) is also not a good solution, as it may be time-consuming and error-prone, and it does not prevent duplicates from being created in Salesforce.

According to the What is Master Data Management (MDM)? article, some of the data challenges that an MDM tool can solve are data duplication, data accuracy and quality, and data standardization. The article states that “MDM solutions comprise a broad range of data cleansing, transformation, and integration practices. As data sources are added to the system, MDM initiates processes to identify, collect, transform, and repair data. Once the data meets the quality thresholds, schemas and taxonomies are created to help maintain a high-quality master reference.” Therefore, an MDM tool can help NTO eliminate data duplication across different systems, improve data accuracy and quality by removing errors and inconsistencies, and standardize data formats and definitions for better integration and analysis.

The Authorization Form Consent object is a standard object that allows you to capture customer consent for marketing purposes. It has fields such as Consent Captured Date, Consent Captured Source, Consent Description, and Consent Status. You can use this object to create consent forms and track customer responses. This is the best option to fulfill the requirement, as it does not require any custom development or external solution.

Creating a Dashboard using Analytics Cloud that will allow the user to create ad-hoc lenses and drill down is a recommended solution for building data exploration dashboards in Salesforce. Analytics Cloud is a powerful data analysis tool that enables users to explore data using interactive dashboards, charts, graphs, and tables on any device. Users can also create lenses, which are ad-hoc data queries that can be saved and reused, and drill down into data details using filters and facets. Creating a Dashboard in an external reporting tool, exporting data to the tool, and adding link to the dashboard in Salesforce will not provide a seamless user experience and may require additional data integration and security considerations. Creating a Dashboard in an external reporting tool, exporting data to the tool, and embedding the dashboard in Salesforce using the Canval toolkit will not provide a native Salesforce solution and may require additional data integration and security considerations. Creating a standard Salesforce Dashboard and connecting it to reports with the appropriate filters will not allow the user to create ad-hoc lenses and drill down into data details on their mobile device.

To solve the issue of lack of governance in making changes to Salesforce, a data architect should recommend creating and managing a data dictionary and setting up a governance process for changes made to common objects. A data dictionary is a document that defines the metadata, structure, and relationship of each object and field in Salesforce. A governance process is a set of rules and procedures that govern how changes are proposed, reviewed, approved, and deployed in Salesforce. These measures will help NTO to maintain consistency, quality, and clarity of their data model and avoid confusion and errors among users. Option A is incorrect because adding description fields to explain why the field is used, and marking the field as required will not prevent unauthorized or unnecessary changes to Salesforce. Option C is incorrect because creating reports to identify which users are leaving blank, and using external data sources to augment the missing data will not address the root cause of the issue, which is the lack of governance in making changes to Salesforce. Option D is incorrect because creating validation rules with error messages to explain why the fields are used will not stop users from seeing new fields on their page that they are unsure of.