Data-Architect Sample Questions Answers

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

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

 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

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.

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

According to the official Salesforce guide1, using Bulk API serial mode for data migration can help avoid locking issues by processing batches in a single thread. This mode ensures that batches are processed in the order they are received and that only one batch is processed at a time. This reduces the risk of lock contention and deadlocks, especially when updating parent and child records in a lookup relationship. Option B is the correct answer because it suggests using Bulk API serial mode for data migration. Option A is incorrect because using Bulk API parallel mode for data migration can cause locking issues by processing batches in multiple threads. This mode does not guarantee the order of batch processing and can result in concurrent updates to the same records, which can lead to lock contention and deadlocks. Option C is incorrect because inserting the order in another custom object and using Batch Apex to move the records to Service_Order__c object adds unnecessary complexity and overhead to the data migration process. Option D is incorrect because changing the lookup configuration to “Clear the value of this field” when lookup record is deleted does not address the locking issues caused by data migration, but rather by record deletion.

The setup audit trail tracks changes made in your org’s setup area for the past 180 days1. You can export the setup audit trail and find the fields in question by filtering by action type, user, or date2.

Using 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.

Removing custom indexes on the data being loaded will prevent unnecessary index maintenance and improve the data load speed. Deferring sharing calculations of the Salesforce Org will avoid frequent sharing rule evaluations and reduce the load time. Deactivating approval processes and workflow rules will prevent triggering any automation logic that might slow down or fail the data load.

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.

To recommend a clean migration, the data architect should set up a staging database, and define external IDs to merge, clean duplicate data, and load into Salesforce. This will allow the data architect to consolidate and deduplicate the data from two legacy systems before importing it into Salesforce using external IDs as unique identifiers. Option A is incorrect because defining external IDs for an object, migrating second database to first database, and loading into Salesforce will not ensure that the data is clean and duplicate-free. Option B is incorrect because defining duplicate rules in Salesforce, and loading data into Salesforce from both databases will not prevent the duplicate data from being imported into Salesforce. Option D is incorrect because defining external IDs for an object, inserting data from one database, and using upsert for a second database will not handle the duplicate data from both databases.

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.

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

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

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

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.

 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, 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.

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.

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

Using scheduled batch Apex to copy aggregate information into a custom object and delete the original records can improve the performance and reduce the data storage limits by removing unnecessary data and keeping only the summary data that is needed for reporting. Configuring the Salesforce Archiving feature to archive older records and remove them from the data storage limits can also help with performance and storage issues by moving historical data to a separate system that is still accessible but does not affect the operational data

Storing the data in a staging database and de-duplicating identical records (option D) is the best solution for a successful migration to mitigate the pain point, as it allows the data architect to identify and merge duplicate customers before they are imported into Salesforce. Hiring an intern manually de-duplicate the records after migrating to Salesforce (option A) is not a good solution, as it may be time-consuming and error-prone, and it does not prevent duplicate records from being created in Salesforce. Migrating the data as is and using Salesforce’s de-duplicating feature (option B) is also not a good solution, as it may cause data quality issues and conflicts, and it does not address the root cause of the duplication. Having the users manually clean the data in the old system prior to migration (option C) is also not a good solution, as it may be unrealistic and impractical, and it does not leverage any automated tools or processes.

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.

 Creating validation rules to check if the required attributes are entered is the best option to mandate this when customers are created in Salesforce. Validation rules allow you to specify criteria that must be met before a record can be saved. You can use validation rules to ensure that customers have a first name, last name, and email when they are created in Salesforce. This way, you can prevent incomplete or invalid data from being sent to your MDM solution.

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.

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.

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 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.

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.

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

 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.

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.

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

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

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

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

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

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.

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

According to the official Salesforce guide1, assigning leads to a single dummy user can cause performance issues and data skew, especially if the dummy user owns more than 10,000 records. Data skew occurs when a single user or a small number of users own a disproportionately large number of records, which can affect query performance and sharing calculations. Option B is the correct answer because it suggests creating multiple dummy users and assigning leads to them, which can distribute the load and reduce data skew. Option A is incorrect because assigning the dummy user to the last role in the role hierarchy does not affect the performance or data skew issues. Option C is incorrect because assigning the dummy user to the highest role in the role hierarchy can worsen the performance and data skew issues, as it will grant access to more users and records. Option D is incorrect because periodically deleting leads can cause data loss and does not address the root cause of the problem.

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 correct answer is A, B, and D. To maintain the relationship hierarchy during migration, a data architect should use data loader to export the data from source org and then import or upsert into the target org in sequential order, create an external ID field for each object in the target org and map source record IDs to this field, and replace source record IDs with new record IDs from the target org in the import file. These steps will ensure that the records are linked correctly and the relationships are preserved. Option C is incorrect because redefining the master detail relationship fields to lookup relationship fields in the target org will change the behavior and security of the data model. Option E is incorrect because keeping the relationship fields populated with the source record IDs in the import file will cause errors and prevent the records from being imported.

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

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.

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.

Option D is correct because data flows should be reviewed with the business users to determine the system of record per object or field1. The system of record is the authoritative source of data for a given entity or attribute2. It may vary depending on the business context and requirements. Option A is not correct because account and opportunity data originates in Salesforce, but it may not be the system of record for all attributes or objects if they are updated by other systems2. Option B is not correct because whatever system updates the attribute or object may not be the system of record for that field or object if there are conflicting or overlapping updates from other systems2. Option C is not correct because whatever integration data flow runs last may not determine which system is the system of record if there are different business rules or logic applied by different systems2.

 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.

  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.

A data quality problem that could cause customers to be called by multiple Sales Reps is having duplicate Contact records in the system. Duplicate records can result from data entry errors, data imports, or integrations with other systems. Duplicate records can lead to confusion, inefficiency, and customer dissatisfaction

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

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.

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

Enabling Account de-duplication by creating matching rules in Salesforce, which will mass merge duplicate Accounts, is what UC should do to ensure proper data quality for their Account records. Matching rules allow UC to define how Salesforce identifies duplicate Accounts based on various criteria, such as name, address, phone number, etc. Mass merge allows UC to merge up to 200 duplicate Accounts at a time, based on the matching rules. This simplifies and automates the process of de-duplicating Accounts and improves data quality. The other options are either more time-consuming, costly, or error-prone for ensuring proper data quality.

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.

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

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

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

Creating a 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.

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

Enabling granular locking (option A) is the best recommendation to mitigate these errors, as it allows finer control over how records are locked during automated or manual processes, and reduces the chances of lock contention or deadlock. Removing SOQL statements from Apex Loops (option B) is a good practice for improving performance and avoiding governor limits, but it does not directly address the lock errors issue. Enabling sharing recalculations (option C) is also not relevant for this issue, as it is used to update sharing rules and recalculate access for records. Asking Salesforce support for additional CPU power (option D) is also not a viable solution, as it does not solve the root cause of the lock errors.

The recommended approach to re-run the test is to refresh the full copy sandbox and re-run the data migration test. A full copy sandbox is a replica of the production Org, including all data, metadata, and attachments. Refreshing a full copy sandbox means creating a new copy of the production Org and replacing the existing sandbox. This would ensure that the test is run on a clean and up-to-date environment, without any leftover data or configuration from previous tests. Truncating or hard deleting objects would not be sufficient, because they would not remove all the data or metadata from the sandbox, and they could also affect other dependent objects or processes.

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.

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

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

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

 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.

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

 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.

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

Implicit sharing will automatically handle this sharing requirement with standard functionality, as it grants read-only access to parent accounts when users have access to child opportunities. This is also known as account-opportunity sharing3. Creating a criteria-based sharing rule (option A) or an owner-based sharing rule (option D) is not necessary, as they are used to grant additional access based on record criteria or ownership. Adding appropriate users to the account team (option C) is also not required, as it is used to grant access to specific users or groups for individual accounts.

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.

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

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

The correct answer is A. To provide additional guidance for users when they enter information in a standard field, a data architect should provide custom help text under field properties. This will display a help icon next to the field label that users can hover over to see the help text. Option B is incorrect because creating a custom page with help text for user guidance will require additional development effort and may not be easily accessible by users. Option C is incorrect because adding custom help text in default value for the field will overwrite the actual default value of the field and may confuse users. Option D is incorrect because adding a label field with help text adjacent to the custom field will clutter the page layout and may not be visible to users.

Contacting Salesforce to enable Defer Sharing Rules is the feature that would ensure that performance, when moving Sales Reps between regions, remains adequate while meeting existing requirements. Defer Sharing Rules allows you to defer sharing rule recalculation when you make changes to public groups or roles that are used in sharing rules. This can improve performance and avoid locking issues when you move users between groups or roles2. Implementing data archiving for old Shipment records will not help with the performance issue related to sharing rules. Contacting Salesforce to create Skinny tables on Shipment will not help with the performance issue related to sharing rules. Configuring shipment OWD to Public Read/Write will not meet the existing requirements of having a Private sharing model.

The recommended solution for Universal Containers (UC) to implement its new Internet of Things technology is to create a new Container Reading custom object with a master-detail relationship to Container which is created when a new measure is received for a specific container. Implement an archiving process that runs every hour. This solution would allow UC to store and access current and historical temperature and humidity data for each container on Salesforce, and use reports and dashboards to analyze it. However, since UC expects a large volume of data over time, they should implement an archiving process that moves data off-platform after a certain period of time to avoid hitting the Org data storage limit and maintain optimal performance. The other options are not recommended, as they would either not store the historical data on Salesforce, or create too many custom fields on the Container object that could impact performance and usability.