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

Utilizing a Validation Rule with a REGEX operator on Postal Code and utilizing a Validation Rule with a CONTAINS operator on address fields are two actions that Cloud Kicks should take to improve data quality for their Invoice records. A Validation Rule with a REGEX operator can check if the Postal Code field matches a specific pattern or format, such as a five-digit number or a combination of letters and numbers. A Validation Rule with a CONTAINS operator can check if the address fields contain certain values, such as valid state abbreviations or country names. These Validation Rules can prevent users from saving invalid or incomplete data and display error messages to guide them to correct the data. The other options are not effective or recommended for improving data quality, as they would either require additional customization, not enforce data standards, or not address the specific issues of missing states and incorrectly formatted postal codes

Salesforce Connect is a feature that allows users to access data from external sources and multiple Salesforce orgs, using either clicks or code. Salesforce Connect can provide read-only access to customer records across all NTO orgs, without replicating or storing the data in Salesforce.

The number of joins used in report query and the number of records returned by report query are two key considerations to make sure the performance of the report is not degraded due to large data volume. The number of joins used in report query affects the complexity and execution time of the query, especially when joining multiple large objects4. The number of records returned by report query affects the amount of data that needs to be processed and displayed by the report engine.

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

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

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.

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

Exporting in multiple batches and using PK Chunking are two steps that can help correct the issues with exporting large volumes of Order records using Informatica Cloud Loader. Exporting in multiple batches can reduce the load on the system and avoid timeouts or errors. Using PK Chunking can split a large data set into smaller chunks based on the record IDs and enable parallel processing of each chunk.

 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.

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

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

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.

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.

Consolidating the data from each org into a centralized datastore is the best suggestion to achieve a 360-degree view of the customer. This way, UC can have a single source of truth for all customer data and avoid data silos and inconsistencies. The other options are not feasible because they either require complex integration, additional cost, or data duplication.

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

The best license to provide these features to the franchisees is the Partner Community license. Partner Community licenses are designed for external users who collaborate with your sales team on deals, such as resellers, distributors, or brokers. Partner Community users can access standard CRM objects, such as accounts, contacts, leads, opportunities, campaigns, and reports. They can also access custom objects and run dashboards12. Salesforce Sales Cloud license is not a good option because it is intended for internal users who need full access to standard CRM and custom apps. Lightning Platform license is not a good option because it is intended for users who need access to custom apps but not to standard CRM functionality. Customer Community license is not a good option because it is intended for external users who need access to customer support features, such as cases and knowledge articles, but not to sales features

 Partner community license type is the best option for building a channel sales portal, as it allows external resellers to access lead, opportunity, and order data, as well as run reports and dashboards. Customer community and customer community plus license types are more suitable for customer service portals, while lightning external apps starter license type does not support reports and dashboards

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.

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

The 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

 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.

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.

Option B is the most appropriate approach for data archiving in this scenario1. By aggregating order amount data at a summary level and storing it in a custom object, you can reduce data storage and still provide visibility to the Sales Reps on how much money each customer generates year-over-year. Option A is not correct because storing data in a zip file does not make it available in Salesforce. Option C is not correct because exporting and deleting orders and order line items may lose important details that are needed for analysis or reporting. Option D is not correct because deleting orders and order line items without exporting them may cause data loss or inconsistency if the customer does not have order information in another system.

 According to the How to avoid row lock or race condition in Apex blog post, one of the ways to prevent race condition in Apex is to use the FOR UPDATE keyword in SOQL statements. The blog post states that “We need to lock the records on which we are working such that other batches or threads will not be having any effect on them. How can we lock a record, then? We need to make use of FOR UPDATE keyword in the SOQL query.” Therefore, a data architect should recommend this solution to guarantee that records are not being updated at the same time by different processes.

 To improve data quality and avoid data skew, the data architect should create a picklist field with values sorted alphabetically for tracking shipping status on opportunity. A picklist field ensures that only valid values are entered and prevents typos or variations in spelling. Sorting the values alphabetically makes it easier for users to find and select the correct value. Data skew occurs when a large number of records are owned by a single user or have a single value for a field. Creating a picklist field with a limited number of values does not cause data skew, as long as the distribution of values is balanced and not skewed towards one value.

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.

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

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.

 The best license type to fulfil US’s requirements is the Service Cloud license. Service Cloud licenses are designed for users who need access to customer service features, such as cases, solutions, knowledge articles, entitlements, service contracts, and service console. Service Cloud users can also access standard CRM objects, such as accounts, contacts, leads, opportunities, campaigns, and reports3. Lightning Platform Starter license is not a good option because it is intended for users who need access to one custom app and a limited set of standard objects. Salesforce license is not a specific license type, but rather a generic term for any license that grants access to the Salesforce platform. Sales Cloud license is not a good option because it is intended for users who need access to sales features, such as products, price books, quotes, orders, and forecasts.

Exploring external data sources in Salesforce to build 360 view of customer is the best strategy to satisfy the requirement, as it allows real-time access to data from other systems without storing it in Salesforce3. Using a customer data mart may not provide real-time information or may require additional integration efforts. Migrating customer activities from all 4 systems into Salesforce may exceed the storage limits or cause data quality issues. Periodically uploading summary information in Salesforce may not provide current or detailed information.

A master data management (MDM) strategy for customer data can help UC ensure that all systems have access to the same customer information, without loading or duplicating data in all systems. An MDM strategy can also help UC avoid data conflicts and inconsistencies that may arise from having multiple systems as owners of data.

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.

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

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.

 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 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.

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

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

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

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

single-select restricted picklist with defined choices can be a way to configure customer type. The article states that picklists are fields that allow users to select one or more predefined values from a list, and restricted picklists ensure that users can only select from the defined values. This can help to limit the choices for customer type and ensure data quality.

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.

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

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

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

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

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

 Improving reporting performance by replacing the existing reports in Sales Cloud with new reports based on Analytics Cloud can avoid the time-out issues by leveraging the power and scalability of Analytics Cloud. Analytics Cloud can handle large volumes of data and provide faster and more interactive reports than Sales Cloud .

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

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

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

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

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

Enabling granular locking (option B) is the best option to prevent the error, as it allows finer control over how records are locked during automated or manual processes, and reduces the chances of lock contention or deadlock. Reducing number of users updated concurrently (option A) is not a good option, as it may limit the productivity and efficiency of the system admins, and it does not address the root cause of the error. Analyzing Splunk query to spot offending records (option C) is also not a good option, as it may require more time and effort, and it does not provide a permanent solution for the error. Increasing CPU for the Salesforce org (option D) is also not a good option, as it may introduce additional cost and complexity, and it does not solve the root cause of the error.

Using external objects to maintain Sales Order in Salesforce is the best recommendation for maintaining Sales Orders in Salesforce, as it allows users to access large volumes of data stored outside Salesforce without copying or synchronizing it. Using custom objects, custom big objects, or standard order object may not be feasible or optimal for storing more than 150 million records that will not be updated in Salesforce.

The two tasks required to define the data archive strategy are to identify how the archive data will be accessed and used, and identify the data retention requirements. Data archiving is the process of moving infrequently used or historical data from active storage to a separate storage location for long-term retention. Data archiving can improve performance, reduce storage costs, and comply with legal or regulatory obligations. To define a data archive strategy, a data architect needs to consider how the archived data will be accessed and used by different users or processes in Salesforce or outside Salesforce, and how long the archived data needs to be retained based on business or legal requirements.

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.

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.

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

Option A is correct because RecordType is a valid metadata type that should be included in documenting the data architecture of a Salesforce organization1. RecordType defines different business processes, picklist values, and page layouts for different users2. Option C is correct because CustomField is another valid metadata type that should be included in documenting the data architecture of a Salesforce organization1. CustomField defines custom attributes for standard or custom objects3. Option B is not correct because Document is not a valid metadata type, but a standard object that stores documents in folders4. Option D is not correct because SecuritySettings is not a valid metadata type, but a setup menu that allows administrators to configure various security features such as password policies, network access, session settings, etc.

Enforcing critical business processes by using Workflow, Validation Rules, and Apex code can help ensure data quality and consistency by applying rules and logic to the data entry and update3. Scheduling a weekly dashboard displaying records that are missing information to be sent to managers for review can help identify and fix data quality issues by providing visibility and accountability4.

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

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.

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

The 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.

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

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

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.

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.

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.

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

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

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

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

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

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

  IP restrictions can be used to prevent users from accessing Salesforce from outside the company network or VPN. According to this article, sharing settings can be used to control access to customer information based on record ownership. According to this article, custom Apex can be used to support customer requests for removal of their information from Salesforce.

Creating an aggregate custom object that summarizes the monthly and yearly values into the required format for the required reports (option D) is the fastest and most effective way for a data architect to solve the time-out issue, as it reduces the amount of data that needs to be queried and processed by the reports. Creating custom fields on opportunity and copying data from order into those custom fields (option A) is not a good solution, as it may create data redundancy and inconsistency, and it does not address the large volume of data. Extracting opportunity and order data from Salesforce and using a third-party reporting tool (option B) is also not a good solution, as it may introduce additional complexity and cost, and it does not leverage the native reporting features of Salesforce. Creating a skinny table in Salesforce and copying order and opportunity fields into it (option C) is also not a good solution, as it may not support all the reporting requirements, and it does not reduce the number of records.

 To help uniquely identify customer across multiple systems, a data architect should recommend creating a custom field as external ID to maintain the customer ID from the MDM solution. An external ID is a custom field that has the “External ID” attribute enabled, which means that it contains unique record identifiers from a system outside of Salesforce. By using the customer ID from the MDM solution as an external ID in Salesforce CRM, Contract Management system, and Marketing solution, the data architect can ensure that each customer can be easily identified and integrated across these systems. Option A is incorrect because storing the Salesforce ID in all the solutions to identify the customer will not work if the customer records are created or updated in other systems besides Salesforce CRM. Option B is incorrect because creating a custom object that will serve as a cross reference for the customer ID will require additional configuration effort and may not be consistent with the actual customer records in each system. Option C is incorrect because creating a customer database and using this ID in all systems will require additional infrastructure cost and maintenance effort.

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

To improve the dashboard performance, the data architect should use selective queries to reduce the amount of data being returned, de-normalize the data by reducing the number of joins, and reduce the amount of data queried by archiving unused opportunity records. These options will help optimize the query performance, reduce the query complexity, and free up storage space. Option C is incorrect because removing widgets from the dashboard to reduce the number of graphics loaded will not affect the dashboard performance significantly, and may reduce the usability and functionality of the dashboard. Option D is incorrect because running the dashboard for CEO and sending it via email will not improve the dashboard performance, but rather shift the burden to another user.

Exploring external data sources in Salesforce to build a 360 degree view of the customer (option A) is the best strategy to satisfy this requirement, as it allows customer service agents to view the activities from all four systems in real time without storing or replicating the data in Salesforce. Using a customer data mart to create the 360 degree view of the customer (option B) is not a good strategy, as it may introduce additional complexity and cost, and it does not leverage the native Salesforce features. Periodically uploading summary information in Salesforce to build a 360 degree view (option C) is also not a good strategy, as it may cause data latency and inconsistency, and it does not provide real-time information. Migrating customer activities from all four systems into Salesforce (option D) is also not a good strategy, as it may cause data redundancy and conflicts, and it does not scale well with large volumes of data.

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.

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.