Salesforce Data-Architect Salesforce Certified Data Architect (SP25) Exam Practice Test

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.

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

The data architect should recommend building a batch job to move two-year-old records off platform, and delete records from Salesforce as an archiving solution. A batch job is a process that runs in the background and performs operations on large volumes of data in Salesforce. By building a batch job that moves two-year-old records off platform to an external storage system, such as Amazon S3 or Google Cloud Storage, and deletes them from Salesforce, the data architect can reduce the storage consumption and improve the performance of Salesforce org. Option A is incorrect because using a third-party backup solution to backup all data off platform will not free up any storage space in Salesforce, unless the data is also deleted from Salesforce after backup. Option B is incorrect because building a batch job to move all records off platform, and delete all records from Salesforce will result in losing all the current data in Salesforce, which may not be desirable or feasible. Option D is incorrect because building a batch job to move all restore off platform, and delete old records from Salesforce does not make sense, as restore implies restoring data back to Salesforce, not moving it off platform.

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

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

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

 Async SOQL with a custom object (option B) and Einstein Analytics (option D) are the two native tools that can be used to report on big object data. Async SOQL allows querying big object data and storing the results in a custom object, which can then be used for reporting. Einstein Analytics can connect to big object data sources and provide advanced analytics and visualization features. Standard reports and dashboards (option A) and standard SOQL queries (option C) cannot be used to report on big object data, as they do not support big object fields

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

According to the Data Modeling unit on Trailhead, some of the key actions that should be done when data modeling in Salesforce are identifying data elements, mapping legacy data, and implementing legacy data model. The unit states that “Before you start creating objects and fields in Salesforce, you need to identify the data elements that you want to store and work with. … Next, you need to map your legacy data to Salesforce objects and fields. … Finally, you need to implement your data model in Salesforce by creating custom objects and fields using declarative tools or Metadata API.” Therefore, these are the correct actions for migrating a legacy client server app to Salesforce.

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.

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

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

The recommended course of action to appropriately integrate the new Opportunity engagement system with Salesforce is to bring the stakeholders together to discuss the appropriate data strategy moving forward. This is because there may be valuable data in both systems that need to be reconciled and harmonized, and the Master Data Management (MDM) strategy may need to be revised or updated to accommodate the new system. The other options are not recommended, as they may result in data loss, inconsistency, or duplication

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

Setting up a Validation Rule to prevent blank values and marking the field required in setup at the field level are two approaches that the Architect should take to ensure that the field always contains data coming from the source system. A Validation Rule can display an error message when a record is created or edited with a blank value for the field, and prevent it from being saved. Marking the field required in setup at the field level can enforce the requirement for all records, regardless of the page layout or the source system. The other options are not effective or recommended for ensuring data quality, as they would either not prevent blank values, not apply to all records, or not address the root cause of the problem

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.

Using Bulk API to export 1 million records from Salesforce is the best option. Bulk API is a RESTful API that allows you to perform asynchronous operations on large sets of data. You can use Bulk API to create, update, delete, or query millions of records in batches. Bulk API is optimized for performance and scalability, and it can handle complex data loading scenarios.

Pre-go-live automated page-load testing against the Salesforce Full sandbox can help identify and resolve any performance bottlenecks or issues before deploying the solution to production. The Full sandbox is an ideal environment for performance testing as it replicates the production org in terms of data, metadata, and integrations. Stress testing against the web services hosted by the integration middleware can also help evaluate the scalability and reliability of the integration solution under high load conditions.

Using Duplicate Management and Data.com Clean are two recommended approaches for UC to avoid duplicate data and increase the level of data quality. Duplicate Management can prevent or alert users when they try to create or edit records that are duplicates of existing records. Data.com Clean can compare Salesforce records with Data.com records and provide suggestions for updates or removals of duplicate records.

According to Trailhead2, Customer 360 Data Manager is a feature that allows administrators to connect, reconcile, and share customer data across Salesforce orgs and external systems. Customer 360 Data Manager can sync product and pricing information from product master database to Salesforce using predefined mappings and rules, ensuring data quality and consistency. Option D is the correct answer because it suggests using Customer 360 Data Manager to sync product and pricing information from product master database to Salesforce. Option A is incorrect because building a custom integration for two-way sync of product and pricing information between product master and Salesforce can introduce data conflicts and duplication issues, as both systems can update the same data independently. Option B is incorrect because building a custom integration for one-way sync of product and pricing information from product master to Salesforce can be costly and time-consuming, as it requires custom development and maintenance. Option C is incorrect because implementing a manual process to update the products from an extract from the products master on a weekly basis can result in data latency and errors, as the products information may change more frequently than once a week.

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

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.

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

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.

Leveraging duplicate rules in Salesforce to validate duplicates during the account creation process (option A) is the best option to implement to avoid duplicates, as it allows the sales associates to identify and merge duplicate accounts before they are saved. Developing an Apex class that searches for duplicates and removes them nightly (option B) is not a good option, as it may cause data loss or conflicts, and it does not prevent duplicates from being created in the first place. Implementing an MDM solution to validate the customer information before creating Salesforce (option C) is also not a good option, as it may introduce additional complexity and cost, and it does not address the issue of customers interacting with both the website and the store. Building a custom search functionality that allows sales associates to search for customer in real time upon visiting their retail stores (option D) is also not a good option, as it may not be reliable or user-friendly, and it does not leverage the existing Global Search feature.

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

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.

 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.

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

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

 Using hard delete in Bulk API, identifying records that have not been modified or used in last 3 years, and archiving the records in EDW before deleting from Salesforce are all good recommendations to reduce the number of records from the org1. Using hard delete in batch Apex or Rest API may not be as efficient or scalable as Bulk API

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.

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

 Clearing the role field in the generic user record and creating a role at the top of the role hierarchy and assigning it to the generic user are two measures that can help avoid performance issues while working with large number of order records. These measures can prevent data skew and lock contention that may occur when a single user owns or shares a large number of records

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

According to this article, the Apex Connector Framework enables developers to create custom adapters for Salesforce Connect to access data from external systems that are not OData enabled. This can meet UC’s requirements of exposing shipment details to the custom application and accessing the data in real time.

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.

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

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.

Option B is correct because putting the business purpose in the Description of each field is a method that an architect can use to trace back configuration changes to the detailed requirements1. The Description of each field provides a brief explanation of what the field is used for and why it is needed2. Option D is correct because using the Force.com IDE to save the metadata files in source control is another method that an architect can use to trace back configuration changes to the detailed requirements1. The Force.com IDE is an integrated development environment that allows developers to work with Salesforce metadata files and Apex code3. Source control is a system that tracks and manages changes to code and configuration files4. Option A is not correct because reviewing the setup audit trail for configuration changes is not a method to trace back configuration changes to the detailed requirements, but a way to monitor and audit the changes made in the setup area. Option C is not correct because maintaining a data dictionary with the justification for each field is not a method to trace back configuration changes to the detailed requirements, but a document that provides information about the data entities and attributes in a system.

 Setting Customer-Reference_c as an External ID (unique) is the recommended solution to ensure that support agents using global search and reports can quickly find shipment records. This is because external ID fields are indexed and can be used as filters in reports and list views4. Making the field unique also ensures that there are no duplicate values that could confuse the search results.

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

According to 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 best API for retrieving Salesforce org metadata programmatically is the Metadata API. The Metadata API provides access to the metadata that defines the structure and configuration of an org, such as custom objects, fields, workflows, security settings, etc. It also supports deploying, retrieving, creating, updating, and deleting metadata components. The Metadata API can be used with various tools, such as Ant, Workbench, or IDEs.

 Using a validation rule for each field that takes the Stage into consideration is the simplest approach to handle this new requirement. A validation rule can enforce the field requirements based on the logic and criteria that you define, and display an error message when users try to save a record that does not meet the requirements. Updating the Opportunity page layout to mark these fields as required will not work because page layouts do not support conditional field requirements. Updating these Opportunity field definitions in Setup to be required will not work because it will apply to all stages and records. Writing an Apex trigger that checks each field when records are saved is not the simplest approach because it requires coding and testing

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

Creating a 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.

Using third-party AppExchange tools to monitor and measure data quality can address the requirement of UC executives by providing features such as data cleansing, deduplication, validation, enrichment, and scoring. These tools can help improve the accuracy, completeness, and consistency of the data entered by sales reps .

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

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.

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.

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.

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

Shield platform encryption is the recommended solution for encrypting sensitive patient records stored in files, as it provides encryption at rest for files and attachments, as well as standard and custom fields. Classic encryption only supports text fields with a maximum length of 175 characters, and does not encrypt files. Third-party App Exchange apps may not provide the same level of security and compliance as shield platform encryption. Storing files outside of salesforce may introduce additional complexity and latency

The best solution to extract 50 million records from a custom object everyday from Salesforce org without facing query timeout issues is to use an ETL tool for extraction of records. ETL stands for extract, transform, and load, and it refers to a process of moving data from one system to another. An ETL tool is a software application that can connect to various data sources, perform data transformations, and load data into a target destination. ETL tools can handle large volumes of data efficiently and reliably, and they often provide features such as scheduling, monitoring, error handling, and logging5. Using a custom auto number and formula field and use that to chunk records while extracting data is a possible workaround, but it requires creating additional fields and writing complex queries. The REST API can extract data as it automatically chunks records by 200, but it has some limitations, such as a maximum of 50 million records per query job6. Asking SF support to increase the query timeout value is not feasible because query timeout values are not configurable

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 .

 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

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

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.

Implementing a workflow rule that sends Opportunity data through Outbound Messaging is the optimal real-time approach that achieves the solution of copying Opportunity records to the ERP once they reach a Closed/Won Stage. A workflow rule can trigger an Outbound Message when an Opportunity record meets certain criteria, such as having a Closed/Won Stage. An Outbound Message can send data from Salesforce to an external system via SOAP API, without requiring any code. The external system can then process the data and create a read-only record in the ERP system. The other options are not optimal or real-time, as they would either require additional systems or tools, not provide real-time data synchronization, or not meet the frequency requirement

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

The 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.

Ask support to create a skinny table for Application with the necessary reporting fields. This is an option that can improve report performance by creating a custom table that contains frequently used fields and copies their values from standard or custom objects. Skinny tables are kept in sync with their source tables and are used in place of them for reports and queries4. Add custom indexes to the Date fields used for filtering the report. This is another option that can improve report performance by creating custom indexes on fields that are often used as filters or join conditions in reports and queries. Custom indexes can speed up data retrieval and reduce query execution time

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

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

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.

Running a report that shows the last time the key fields were updated can help Get Cloudy Consulting identify stale or outdated data and prioritize data cleansing activities. Running a report that shows the percentage of blanks for the important fields can help Get Cloudy Consulting measure the completeness of their data and identify gaps or missing value

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

The best object model to configure within SF for migrating individual customers (B2C) data from legacy systems is to leverage person account object in Salesforce. Person accounts are a special type of accounts that store information about individual people by combining certain account and contact fields into a single record4. Person accounts are useful for B2C scenarios where there is no need to associate a company name with a contact. Person accounts also support standard Salesforce features and functionality, such as leads, campaigns, reports, dashboards, etc5. Leverage custom person account object in SF is not a good option because there is no such thing as a custom person account object. Leverage custom account and contact object in SF is not a good option because it would require creating and maintaining additional objects and fields that may not be necessary or compatible with standard Salesforce features. Leverage standard account and contact object in SF is not a good option because it would require filling in dummy values for the account name field, which is mandatory for standard accounts.

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

The correct answer is A, enable defer sharing calculations, and suspend sharing rule calculations. Defer sharing calculations and suspend sharing rule calculations are features that allow you to temporarily disable the automatic recalculation of sharing rules when you load large volumes of data. This can improve the performance and speed of your data load process by avoiding unnecessary system calculations. Loading the data through data loader, leveraging the bulk API, or enabling granular locking are also options that can help with data load times, but they do not directly address the system calculations issue.

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.

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.

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.

Creating an account plan object with a lookup relationship to Account with validation rules to enforce the Account association can help UC meet their requirement. A lookup relationship allows different accessibility requirements for the account plan object and the account object, as well as different ownership and sharing settings. A validation rule can ensure that each account plan has an account associated with it.

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

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

Service Cloud License (option C) is the license type that UC users need to fulfill UC’s requirements, as it allows them to track customer complaints, provide white papers on products, and provide subscription-based support. Sales Cloud License (option A) is mainly for managing sales processes and leads, Lightning Platform Starter License (option B) is for building custom apps and workflows, and Salesforce License (option D) is a generic term that does not specify a particular license type.

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.