Free and Premium Salesforce Data-Architect Dumps Questions Answers

The recommended data model for consumer account data to be stored in Salesforce is to use the Account object with Person Accounts and a new B2C page layout. Person Accounts are a special type of accounts that allow you to store information about individual consumers who are not associated with a business account2. Person Accounts have the following advantages3:

They allow you to use the same standard objects and features that you use for business accounts, such as contacts, opportunities, cases, etc.

They enable you to create different page layouts and record types for B2C and B2B customers, which allows you to customize the user interface and business logic for each segment.

They support large data volumes and can scale up to 50 million records or more, which meets the expected growth of consumers over the next five years.

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

 The Data Architect should consider the limitations on Org data storage and the Bulk API limitations on picklist fields when denormalizing the data model into a single Transaction object with a Branch Office picklist. The Org data storage limit is the total amount of data that can be stored in a Salesforce Org, and it depends on the edition and license type of the Org1. The Bulk API limit on picklist fields is the maximum number of values that can be imported or exported using the Bulk API, and it is 1,000 values per picklist field2. These limitations could affect the performance and scalability of the data model, and the Data Architect should plan accordingly.

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.

Changing Opportunity Amount field access to Read Only for sales users field-level security (option C) is the best way to address the overwriting issue, as it prevents sales users from updating the field that is controlled by PPS, and ensures data consistency and accuracy. Creating a custom field for Opportunity amount that PSS updates separating the field sales user updates (option A) or creating a custom field for Opportunity amount that sales users update separating the field that PPS updates (option D) are not good solutions, as they may create confusion and inconsistency with the Opportunity Forecast feature. Changing PSS integration to update only Opportunity Amount field when the value is null (option B) is also not a good solution, as it may cause data loss or conflicts with the sales users’ inputs.

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

  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.

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

According to the 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.

Data skew occurs when a large number of child records are associated with a single parent record, or when a single user owns a large number of records. This can cause performance issues and lock contention. To avoid data skew, the data architect should limit assigning one user 10,000 records ownership, limit associating 10,000 opportunities to one account, and limit associating 10,000 records looking up to the same record

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

The 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.

The three key steps that a data architect should take when merging data from multiple systems into Salesforce are:

Analyze each system’s data model and perform gap analysis. This step involves understanding the structure and meaning of the data in each system, identifying the common and unique data elements, and mapping the data fields between the systems. This step also involves assessing the quality and consistency of the data, and identifying any data cleansing or transformation needs.

Utilize an ETL tool to merge, transform, and de-duplicate data. This step involves using an ETL tool to connect to the source systems, extract the data, apply any data transformations or validations, and load the data into Salesforce. This step also involves applying de-duplication rules or algorithms to avoid creating duplicate records in Salesforce.

Work with stakeholders to define record and field survivorship rules. This step involves collaborating with the business users and owners of the data to determine which records and fields should be retained or overwritten in case of conflicts or discrepancies. This step also involves defining the criteria and logic for record and field survivorship, and implementing them in the ETL tool or in Salesforce.

Creating new fields to store additional values from all the systems is not a key step, but rather a possible outcome of the gap analysis. It may not be necessary or desirable to create new fields for every value from every system, as it may result in redundant or irrelevant data. Installing a 3rd party AppExchange tool to handle the merger is not a key step, but rather a possible option for choosing an ETL tool. It may not be the best option depending on the requirements, budget, and preferences of the organization.

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.

 Implementing a custom big object to store the data is the best recommendation to reduce the performance degradation of the platform, as it allows storing large volumes of data that do not need real-time access or reporting. Custom big objects can be defined using metadata API or developer console, and support up to 1 billion records per object. Creating a custom object or using the standard order object would consume a lot of storage space and impact the performance of queries and reports. Using a standard big object defined by salesforce would not be applicable for order records, as standard big objects are predefined for specific use cases such as audit trails or field history.

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

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

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

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

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.

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

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 three characteristics of a Skinny table that help improve report and query performance are: Skinny tables can contain frequently used fields and thereby help avoid joins. Skinny tables are kept in sync with changes to data in the source tables. Skinny tables do not include records that are available in the recycle bin. These characteristics are beneficial because they reduce the query complexity and execution time, and improve the data accuracy and freshness. For example, skinny tables can contain frequently used fields from multiple objects, such as Account and Contact, and thereby help avoid joins that can slow down queries4. Skinny tables are updated automatically when the source tables are modified, so they always reflect the latest data5. Skinny tables do not include records that are available in the recycle bin, so they only contain active records that are relevant for reports and queries.

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.

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

Leverage an External ID from source system orders to import related order lines. This is the recommended solution because it allows you to use the upsert operation to match records based on the External ID field, which is indexed and unique. This avoids the need to query the parent order IDs in Salesforce before importing the order line items, which can cause time-out errors or long delays1.

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

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

The two features that a data architect should recommend to share records between Salesforce orgs are:

Change Data Capture (CDC): This is a feature that publishes change events for changes in Salesforce records, such as create, update, delete, and undelete operations. Change events can be subscribed by external systems or other Salesforce orgs using various tools, such as Platform Events, Streaming API, or CometD. CDC can help keep data in sync between Salesforce orgs in near real time89.

Salesforce to Salesforce (S2S): This is a feature that allows sharing records and related data with other Salesforce orgs that you partner with. You can choose which records and fields to share, and which orgs to share with. You can also accept updates from the other orgs to keep the data consistent10.

Salesforce Connect is not a good feature to share records between Salesforce orgs because it is intended for integrating external data sources with Salesforce using external objects. Federation Search is not a good feature to share records between Salesforce orgs because it is intended for searching across multiple Salesforce orgs without sharing data.

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

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

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

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

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

According to 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.

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

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

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

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

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

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

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.

Using Salesforce duplicate management and using validation rules on new record create and edit are two things that an architect should consider using to maintain data quality for managing Contacts. Salesforce duplicate management allows the architect to create matching rules and duplicate rules to identify, prevent, or allow duplicate records based on various criteria. Validation rules allow the architect to enforce data quality standards and business logic by displaying error messages when users try to save invalid data. The other options are not relevant or helpful for maintaining data quality

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.

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

According to the Salesforce documentation4, Marketing Cloud is a platform that allows creating and managing marketing campaigns across multiple channels, such as email, mobile, social media, web, etc. Marketing Cloud can integrate with Sales Cloud and other Salesforce products to share data and insights. One of the ways to integrate Marketing Cloud with Sales Cloud is using Marketing Cloud Connect5, which allows syncing data between the two platforms using synchronized data sources.

However, if UC occasionally gets a list of prospects from third-party sources as CSV files for marketing purposes, it may not be necessary or efficient to load them into Sales Cloud first and then sync them with Marketing Cloud. This can consume large amounts of storage in Sales Cloud, which has a limit based on the license type6. It can also cause data quality issues, such as duplicates or outdated information.

A better option for UC is to load the contacts directly to Marketing Cloud using Import Definition, which allows importing data from external files or databases into Marketing Cloud data extensions. Data extensions are custom tables that store marketing data in Marketing Cloud. This way, UC can avoid storage issues in Sales Cloud and optimize the marketing process by sending marketing communications directly from Marketing Cloud.

To track prospects that are converted to customers, UC can have a reconciliation process that compares the contacts in Marketing Cloud with the accounts or contacts in Sales Cloud. This can be done using SQL queries or API calls to access and compare data from both platforms. Alternatively, UC can use Marketing Cloud Connect to sync the converted contacts from Sales Cloud to Marketing Cloud using synchronized data sources.

Loading the CSV files in Einstein Analytics and syncing with Marketing Cloud prior to sending marketing communications (option A) is not a good option, as it can add unnecessary complexity and latency to the process. Einstein Analytics is a platform that allows creating and analyzing data using interactive dashboards and reports. It is not designed for importing and exporting data for marketing purposes.

Loading the CSV files in an external database and syncing with Marketing Cloud prior to sending marketing communications (option B) is also not a good option, as it can incur additional costs and maintenance for the external database. It can also introduce data security and privacy risks, as the data may not be encrypted or protected by Salesforce.

Continuing to use the existing process to use Lead object to sync with Marketing Cloud and delete Lead records from Sales after the sync is complete (option D) is not a good option, as it can cause performance issues and data loss. Deleting Lead records from Sales can affect reporting and auditing, as well as trigger workflows and validations that may not be intended. It can also cause data inconsistency and synchronization errors between Sales Cloud and Marketing Cloud.

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

The 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.

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.

 Skinny tables are custom tables that contain frequently used fields from a standard or custom object. They are used to improve performance by reducing the number of database joins required for queries. Skinny tables have the following characteristics1:

They do not include soft-deleted records, which means they only contain active records and save space.

They avoid resource intensive joins by storing data from multiple objects in one table, which reduces the query time and complexity.

Their tables are kept in sync with their source tables when the source tables are modified, which ensures data consistency and accuracy.

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.

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

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

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.

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

 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.

 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

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

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.

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

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

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

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

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

 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

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

When denormalizing a data model into a single object with a picklist field, the data architect should consider the Bulk API limitations on picklist fields and the standard list view in-line editing. The Bulk API has a limit of 1,000 distinct picklist values per file1, which could be an issue if there are more than 1,000 venues in the future. The standard list view in-line editing allows users to edit multiple records at once, which could introduce data quality issues if the venue picklist is not validated or restricted2. The other options are not relevant to denormalizing a data model.

 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

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 Trailhead2, Salesforce Connect is a feature that allows administrators to integrate external data sources with Salesforce without copying or synchronizing the data. Salesforce Connect can use external objects to display the external data in Salesforce as if it were stored in Salesforce objects, enabling seamless user experience and real-time access. Option B is the correct answer because it suggests using Salesforce Connect and an external object to display the order history in Salesforce. Option A is incorrect because using an Apex callout to populate a text area field for displaying the order history can be inefficient and unreliable, as it requires making an HTTP request for each customer record and storing the order history as unstructured text. Option C is incorrect because importing the order history into a custom Salesforce object, update nightly can consume a lot of storage space and cause data latency, as the order history may change more frequently than once a night. Option D is incorrect because embedding the ERP system in an iframe and displaying on a custom tab can degrade the user experience and security, as it requires switching between tabs and authenticating with another system.

The three activities that will be required to identify a successful master data management strategy are:

Identify data to be replicated: This activity involves determining which data elements need to be copied from one system to another, and how frequently the replication should occur. This can help ensure data consistency and availability across systems.

Create a data archive strategy: This activity involves defining how historical data will be stored, accessed, and deleted over time. This can help optimize data storage, performance, and compliance.

Define the systems of record for critical data: This activity involves identifying which system owns and maintains the authoritative version of each data element. This can help avoid data conflicts and duplication across systems67.

Install a data warehouse is not a required activity, but rather a possible option for consolidating data from multiple sources for analytics purposes. Choose a Business Intelligence tool is not a required activity, but rather a possible option for visualizing and reporting on data from various sources.

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

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.

According to the exam guide, one of the objectives is to "describe the use cases and considerations for using AppExchange apps and Wave Analytics"1. This implies that options A and B are both valid approaches to provide more analytical insights. Option C is not correct because weekly snapshots are used to track changes over time, not to provide advanced analytics3. Option D is not correct because setup audit trails are used to monitor changes in the setup menu, not to provide analytical insights.

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.

 Data storage limits and API request limits are two important factors that affect the data integration and storage in Salesforce. Data storage limits determine how much data can be stored in Salesforce, and API request limits determine how many API calls can be made to Salesforce in a 24-hour period. Both of these limits depend on the edition and license type of the Salesforce org. Workflow rule limits and webservice callout limits are not directly related to data integration and storage, but rather to business logic and external services.

The recommended solution for the Cloud Kicks Data Architect to address the requirements is to create a custom object to track shipment requests and a child custom object to track shipment items. Implement an archiving process that moves data off-platform after three years. This solution would allow Cloud Kicks to store and manage their shipment data on Salesforce, and use Salesforce reports to analyze it. However, since Cloud Kicks expects a large volume of data over time, they should implement an archiving process that moves data off-platform after three years to avoid hitting the Org data storage limit and maintain optimal performance3. External objects are not a good option for this scenario, because they are stored off-platform in an external system, such as Heroku’s Postgres database, and they have limited functionality and performance compared to custom objects

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

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.

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

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

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.