Data-Architect Salesforce Certified Data Architect (SP25) Questions and Answers

Enabling granular locking allows concurrent sharing rule calculations and group membership updates to run without locking each other1. This can help resolve the group membership locking issues that UC is experiencing.

Enabling “Set Audit Fields” allows the user loading the data to set the value of the standard CreatedDate field to match the original date from the legacy system. This is a one-time permission that can be revoked after the migration is completed. The other options would either not work or require additional customization

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.

To capture and report on any changes made to ten text fields on the Contact object for the past 3 months, the data architect should write an Apex trigger on Contact after insert and after update events and store the old values in another custom object, or turn on field Contact object history tracking for these ten fields and create reports on contact history. An Apex trigger can capture the old and new values of the fields, as well as the user and time of the change, and store them in a custom object that can be used for reporting. Field history tracking can also track the changes to the fields and store them in a history table that can be used for reporting. However, field history tracking only retains data for up to 18 months or 24 months with an extension, so it may not be suitable for longer-term reporting needs. The other options are not feasible or effective for capturing and reporting on field data changes.

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.

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.

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

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.

The recommended solution to address the requirements of Universal Containers (UC) is to roll-up discount request amount at the line-item-level and line-item-level summary discount at the opportunity level. This solution would allow UC to show all sums of discounts across line items at an opportunity level using standard roll-up summary fields. Since UC has a multi-level master-detail relationship for opportunities, a custom opportunity line item object, and a custom discount request object, they can use two levels of roll-up summary fields to aggregate the discount amounts from the child objects to the parent objects. For example, they can create a roll-up summary field on the custom line item object to sum up the discount request amounts from the custom discount request object. Then, they can create another roll-up summary field on the opportunity object to sum up the line-item-level summary discounts from the custom line item object.

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.

To provide the sales team with the required functionality, a data architect should use a mashup page that will present the billing system records within Salesforce. A mashup page is a web page that combines data from multiple sources into a single integrated view. A mashup page can be created using Visualforce or Lightning Web Components, and can use Salesforce Connect or custom integrations to access external data from the on-premise web-based billing and invoice system. This will allow the sales team to pull a customer record and view their account status, invoice history, and opportunities without navigating outside of Salesforce. Option A is incorrect because creating a custom object and migrating the last 12 months of invoice data into Salesforce so it can be displayed on the account layout will consume a lot of storage space and may not reflect the latest data from the billing system. Option B is incorrect because writing an Apex callout and populating a related list to display on the account record will require additional development effort and may not be scalable or performant for large volumes of data. Option D is incorrect because creating a visual force tab with the billing system encapsulated within an iframe will not allow the sales team to view the billing system records within the customer record, but rather in a separate tab.

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.

 Patients as Person Accounts, Payment providers as Accounts, & Doctors as Person Accounts is the optimal selection by the data architect to map the actor to Salesforce objects. This is because Person Accounts are a special type of accounts that can store both business and personal information for individual customers. Payment providers are organizations that pay for the care of patients, so they can be modeled as Accounts. Doctors are also individuals who provide care plans for patients and need access to patient information, so they can also be modeled as Person Accounts.

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.

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.

 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

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.

 Salesforce allows up to four objects in a single report type. This means that if an architect wants to create a report that compiles data from Accounts, Contacts, Opportunities, Orders, and Order Line Items, they will run into a difficulty because that would require five objects in a single report type. To overcome this limitation, the architect can either create two separate report types and join them in a joined report, or use an external reporting tool that can access data from multiple objects.

According to the Salesforce documentation, a relationship field is a field that allows linking one object to another object in Salesforce. There are different types of relationship fields that have different characteristics and behaviors, such as master-detail, lookup, indirect lookup, external lookup, etc.

To recommend a type of relationship field for this scenario, where legacy inventory data will be stored in a custom child object called Inventory__c, inventory data should be related to the standard Account object, the Inventory__c object should inherit the same sharing rules as the Account object, and anytime an Account record is deleted in Salesforce, the related Inventory__c record(s) should be deleted as well, a data architect should recommend:

Master-detail relationship field on Inventory__c, related to Account (option B). This means creating a field on the Inventory__c object that references the Account object as its parent. A master-detail relationship field establishes a parent-child relationship between two objects, where the parent object controls certain behaviors of the child object. For example, a master-detail relationship field can:

Inherit the sharing and security settings from the parent object to the child object. This means that the users who can access and edit the parent record can also access and edit the related child records.

Cascade delete from the parent object to the child object. This means that when a parent record is deleted, all the related child records are also deleted.

Roll up summary fields from the child object to the parent object. This means that the parent object can display aggregated information from the child records, such as count, sum, min, max, or average.

Master-detail relationship field on Account, related to Inventory__c (option A) is not a good solution, as it reverses the direction of the relationship. This means creating a field on the Account object that references the Inventory__c object as its parent. This is not possible, as a standard object cannot be on the detail side of a master-detail relationship. Indirect lookup relationship field on Account, related to Inventory__c (option C) is also not a good solution, as it is a special type of relationship field that allows linking a custom object to a standard object on an external system using an indirect reference. This is not applicable for this scenario, as both objects are in Salesforce and do not need an external reference. Lookup relationship field on Inventory__c related to Account (option D) is also not a good solution, as it establishes a looser relationship between two objects than a master-detail relationship. A lookup relationship field does not inherit sharing and security settings from the parent object to the child object, does not cascade delete from the parent object to the child object, and does not roll up summary fields from the child object to the parent object.

The data architect should recommend Lighting platform plus license type for the finance team that best meets the requirements. This license type allows users to access custom objects and run reports and dashboards on them, without accessing any sales objects. Option A is incorrect because Service Cloud license type is more suitable for customer service users who need to access cases, knowledge, and other service objects. Option B is incorrect because Sales Cloud license type is more suitable for sales users who need to access accounts, contacts, opportunities, and other sales objects. Option C is incorrect because Light Platform Start license type does not allow users to run reports and dashboards on custom objects.

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 .

The best solution to assign opportunities based on zip code and sales area is to configure territory management feature to support opportunity assignment. Territory management is a feature that allows you to organize your sales team into territories based on criteria such as geography, industry, product line, or customer segment. You can assign accounts and opportunities to territories using assignment rules or manual sharing. You can also define forecast managers and roll up forecasts by territory45. Assign opportunities using list views using zip code is not a good solution because it is inefficient and does not support reporting by sales area. Add custom fields in opportunities for zip code and use assignment rules is not a good solution because it requires creating additional fields and does not support reporting by sales area. Allow sales users to manually assign opportunity ownership based on zip code is not a good solution because it is prone to errors and does not support reporting by sales area.

A performance test plan is required for any Force.com application that has highly customized code or large volumes of data. You need to create and submit a performance test plan to Salesforce customer support before conducting any performance testing in your sandbox or production org2. Applications with highly customized code or large volumes of data should be performance tested to ensure they meet the expected response time and throughput

According to Trailhead2, record types are a feature that allows administrators to create different business processes, page layouts, and picklist values for different types of records within an object. Record types can be used to maintain different dealer types in Salesforce by creating record types for each of the dealer types and assigning them appropriate attributes, processes, and rules. Option A is the correct answer because it suggests using Accounts for dealers, and creating record types for each of the dealer types. Option B is incorrect because creating dealers as Accounts, and building custom views for each of the dealer types does not allow CRM users to enter only attributes related to the customer types, nor does it enable different processes and business rules for each of the customer types. Option C is incorrect because using Accounts for dealers and custom picklist field for each of the dealer types does not allow CRM users to enter only attributes related to the customer types, nor does it enable different processes and business rules for each of the customer types. Option D is incorrect because creating Custom objects for each dealer types and custom fields for dealer attributes can be unnecessary and complex, as it requires creating multiple objects and relationships instead of using the standard Account object.

 According to the Salesforce Connect Reporting blog post, one of the considerations for using Salesforce Connect for reporting is the OData callout limits per day. The blog post states that “Salesforce Connect has a limit of 100,000 callouts per day. This limit is shared across all external data sources in your org. If you exceed this limit, you will receive an error message and no more callouts will be allowed until the next day.” Therefore, a data architect should consider this limit before implementing Salesforce Connect for reporting.

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.

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

Custom metadata records are deployable using packages, which makes them easier to migrate from one environment to another. Custom settings records are not deployable using packages, and they are copied from production to sandbox. Custom metadata types are not available for reporting, and custom metadata records are not editable in Apex.

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

Building a customer view in Service Console with components that show website data and marketing data as mashup is the best option to fulfill the requirement with minimum development effort in Salesforce. A mashup is a technique that combines data from different sources into a single user interface. You can use Visualforce pages or Lightning components to create mashups that display data from external systems such as your website and marketing system. This way, you can provide your contact center agents with a comprehensive view of the customer information they need.

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 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.

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.

Using an AppExchange package for Data Quality is a good way to match Salesforce data against a trusted source, such as an MDM system. You can use tools like Cloudingo, DupeCatcher, or DemandTools to identify and merge duplicate records, standardize data formats, and enrich data with external sources

Doing client-side validation of phone number and email field formats and preferring picklist form fields over free text fields, where possible, are two techniques that will help maintain a high level of data quality when designing forms that create Lead records in Salesforce using the REST API. Client-side validation can ensure that the input data is in the correct format and prevent errors or invalid values from being submitted. Picklist form fields can provide a predefined set of values for the users to choose from and avoid typos or inconsistencies in the data.

According to this article, Einstein Analytics for multi-org can help UC to report on accounts and opportunities from each org in one place. It can also provide advanced AI-driven analytics on the dataset using Einstein Discovery.

 Creating a custom object related to the Invoice object with a picklist field for the Type allows the architect to categorize the attachments and report on them by Type. The standard Attachment object does not have a ContentType picklist field, and adding a custom picklist field to it would not be best practice.

  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.

 The best solution to make sure lead data has both profile and location information is to leverage external data providers to populate company profile and location data. This is because external data providers can enrich lead data with additional information from third-party sources, such as Dun & Bradstreet, ZoomInfo, or Clearbit. This can help improve lead quality, segmentation, and conversion. Salesforce supports integrating with external data providers using Data.com Clean or other AppExchange solutions2. Asking sales people to search for populating company profile and location data is inefficient and prone to errors. Running reports to identify records which do not have company profile and location data is useful, but does not solve the problem of how to populate the missing data. Exporting data out of Salesforce and sending to another team to populate company profile and location data is cumbersome and time-consuming.

The three considerations needed prior to a SF Connect implementation are to develop an object relationship strategy, identify the external tables to sync into external objects, and assess whether the external data source is reachable via an ODATA endpoint. SF Connect is a feature that allows integrating external data sources with Salesforce using external objects. External objects are similar to custom objects, but they store metadata only and not data. They enable on-demand access to external data via standard Salesforce APIs and user interfaces. To implement SF Connect, a data architect needs to consider how the external objects will relate to other objects in Salesforce, which external tables will be exposed as external objects, and whether the external data source supports ODATA protocol for data access.

 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

Using Weekly Export to extract transactional data to on-premise systems and using Event Monitoring to extract event data to on-premise systems are two recommended approaches that would address the security requirement of UC. Weekly Export is a built-in feature that allows UC to download a zip file of their Salesforce data on a weekly basis. Event Monitoring is a tool that allows UC to access detailed information about various events that occur in their Salesforce org, such as logins, logouts, file downloads, etc.

Enabling field history tracking on the Case object is the first step to implement a reporting solution to measure the support reps case closure rates. Field history tracking allows you to track changes to certain fields on the Case object, such as Status, Owner, or Priority. You can then create reports based on the field history data to analyze how long it took to close a case, how many times the case owner changed, or how many cases were escalated.

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.

Using an indexed data field with bounded data filters can improve the report performance by making the query more selective and reducing the number of records to scan. Using formula fields to surface information from related entities on the report can also enhance the report performance by avoiding joins and complex calculations

The best design pattern to satisfy the requirement of accessing order data from an on-premise database with REST API access without consuming SF storage limits is to use SF Connect to virtualize the data in SF and avoid storage limits. SF Connect is an integration tool that allows users to access and integrate data from external sources using external objects. External objects are similar to custom objects, except that the data resides in another system and is accessed in real time via web service callouts. SF Connect supports various adapters to connect to different types of external data sources, such as OData, cross-org, or Apex custom adapter11. Migrate and persist the data in SF to take advantage of native functionality is not a good option because it would consume SF storage limits and require data synchronization between systems. Develop a bidirectional integration between the on-premise system and Salesforce is not a good option because it would be complex and costly to implement and maintain, and it would also consume SF storage limits. Build a UI for the on-premise system and iframe it in Salesforce is not a good option because it would not provide a seamless user experience and it would not allow users to search, report, or perform actions on the external data.

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

saving Apex errors in a custom object can be a way to report errors from an Apex job. The article provides an example of how to create a custom object called AsyncApexError__c and use a trigger to insert error records into it. The custom object can then be used for reporting or notification purposes.

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

According to the Get Started with Custom Metadata Types unit on Trailhead, one of the use cases for custom metadata types is to define custom charges for an accounting app. The unit states that “Say that your org uses a standard accounting app. You can create a custom metadata type that defines custom charges, like duties and VAT rates. Then you can write some Apex code that calculates the total amount due for each invoice by using the metadata from your custom metadata type.” Therefore, a similar approach can be used to maintain the pricing criteria for a custom pricing engine in Salesforce.

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.

Scheduling a weekly dashboard displaying records that are missing information to be sent to managers for review and using Workflow, Validation Rules, and Force.com code (Apex) to enforce critical business processes are two approaches that an architect should recommend to enforce the new Data Quality Plan for UC’s Salesforce data. Scheduling a weekly dashboard can provide a regular and visual way of monitoring the data quality and identifying any gaps or issues that need to be addressed by the managers or users. Using Workflow, Validation Rules, and Apex can provide various ways of enforcing data quality standards and business logic by automating actions, displaying error messages, or executing custom code when users create or edit records. The other options are not suitable or helpful for enforcing the Data Quality Plan, as they would either not provide real-time feedback, not prevent data quality issues, or not leverage the capabilities of Salesforce

Einstein Analytics can provide more analytical insights than native reports and dashboards by allowing users to explore data from multiple sources, create interactive visualizations, and apply AI-powered features5. AppExchange Apps can also provide more analytical insights by offering pre-built solutions or integrations with external tools that can enhance the reporting and analytics capabilities of Salesforce6.

PK Chunking is a feature of the Bulk API that allows splitting a large query into smaller batches based on the primary key of the object. This can improve the performance and avoid query timeouts when querying large data sets

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.

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.

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.

PK Chunking can resolve query timeout when running a bulk query on an object with more than 10 million records. PK Chunking is a feature of the Bulk API that splits a query into multiple batches based on the record IDs (primary keys) of the queried object. This can improve the query performance and avoid timeouts by reducing the number of records processed in each batch.

The correct answer is C, leverage Salesforce Connect to bring the order line item from the legacy system to Salesforce. Salesforce Connect is a feature that allows you to integrate external data sources with Salesforce and access them in real time without copying or synchronizing the data. This way, the sales team can view the order status from the ERP system without having access to it. Leveraging Canvas, building batch jobs, or building real-time integration are also possible solutions, but they are more complex and costly than using Salesforce Connect.

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.

 Loading all data after deferring sharing calculations can improve the performance and avoid locking issues when loading a large amount of time entry records into Salesforce.com. This is because deferring sharing calculations can temporarily suspend the calculation of sharing rules until all the data is loaded, and then recalculate them in one operation

 Ensuring validation rules, triggers and other automation tools are disabled is the best way to mitigate any unwanted results during the import, as it prevents any errors or conflicts that may occur due to the existing logic. Ensuring duplication and matching rules are defined may not be sufficient or relevant for preventing unwanted results. Importing the data in smaller batches over a 24-hour period may not be necessary or efficient. Bulkifying the trigger to handle import leads may not be possible or desirable if the triggers were not designed to run during a data load.

The correct answer is C and D. To minimize performance and storage issues, a data architect should recommend leveraging on premise data archival and building integration to view archived data, and leveraging big object to archive case data and lightning components to show archived data. These solutions will allow some user groups to access historical cases for up to 7 years without consuming too much storage space or affecting the performance of queries and reports. Option A is incorrect because exporting data out of salesforce and storing it in flat files on external system will make it difficult to access and query the data. Option B is incorrect because creating a custom object to store case history and run reports on it will still consume a lot of storage space and impact the performance of queries and reports.

According to the 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.

Creating a unique global customer ID for each customer and storing that in all systems for referential identity (option A), investing in a data duplicate tool to de-dupe and merge duplicate records across all systems (option C), and creating a customer master database external to Salesforce as a system of truth and syncing the customer data with all systems (option E) are the three options that a data architect should perform to resolve the issues with the customer data. Option A ensures that each customer can be uniquely identified across different systems, option C eliminates duplicate records and improves data quality, and option E provides a consistent and reliable source of customer data for all systems. Using Salesforce CDC to sync customer data across all systems (option B) is not a good option, as it does not address the duplication or inconsistency issues. Duplicating customer records across the system and providing a two-way sync of data between the systems (option D) is also not a good option, as it may create more confusion and conflicts with customer data.

The three questions that should be considered when designing an appropriate archiving strategy are: If reporting is necessary, can the information be aggregated into fewer, summary records? Will the data being archived need to be reported on or accessed in any way in the future? Are there any regulatory restrictions that will influence the archiving and purging plans? These questions are important because they help determine the scope, frequency, and method of archiving and purging data from Salesforce. For example, if reporting is necessary, then summary records or analytics snapshots can be used to store aggregated data and reduce the number of records that need to be archived1. If the data being archived needs to be accessed in the future, then offsite storage or external objects can be used to retain the data and make it available on demand2. If there are any regulatory restrictions, such as GDPR or HIPAA, then the archiving and purging plans need to comply with them and ensure data security and privacy

The data architect should recommend using AppExchange package for backup and restore. AppExchange is a marketplace for Salesforce apps and solutions that can be installed and configured in Salesforce orgs. There are several AppExchange packages that provide backup and restore functionality for Salesforce data, such as OwnBackup, Odaseva, or Spanning. These packages can perform daily backups of Salesforce data to a secure cloud storage, and provide restore capability to avoid any data loss in case of outage. Option B is incorrect because using ETL (Extract, Transform, Load) for backup and restore from EDW (Enterprise Data Warehouse) will require additional development effort and may not be reliable or secure. Option C is incorrect because using Bulk API to extract data on daily basis to EDW and REST API for restore will require additional integration effort and may not be scalable or performant. Option D is incorrect because changing weekly backup process to daily backup, and implementing a custom restore solution will require additional configuration effort and may not be robust or compliant.

According to the official Salesforce guide1, data classification metadata is a feature that allows administrators to classify data fields based on their sensitivity level, such as confidential, restricted, or general. Data classification metadata can be used to filter and identify sensitive data fields and apply appropriate security measures, such as encryption, masking, or auditing. Option D is the correct answer because it suggests using data classification metadata to provide ways to filter and identify sensitive data. Option A is incorrect because data grouping metadata is not a feature in Salesforce. Option B is incorrect because field-level security is a feature that controls the visibility and editability of fields based on user profiles or permission sets, but it does not provide ways to filter and identify sensitive data. Option C is incorrect because creating a custom checkbox denoting sensitive data is not a scalable or reliable solution, as it requires manual maintenance and does not enforce any security measures.

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.

Both A and B are automated approaches to purge detailed transactional records from Salesforce and aggregate them at a summary level1. You can use a third-party integration tool (ETL) or schedulable batch Apex to perform these tasks. Option C is not correct because a third-party business intelligence system does not purge data from Salesforce, but only analyzes it. Option D is not correct because Apex triggers are not automated, but execute when a record is inserted, updated, deleted, or undeleted.

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.

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.

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.

Using Salesforce Bulk API in parallel mode can reduce the migration time by processing multiple batches of records simultaneously and leveraging the server resources more efficiently. The Bulk API is designed for loading large amounts of data into Salesforce