Pass the Salesforce Application Architect Data-Architect Questions and answers with ValidTests

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

Both C and D are valid methods to automatically archive and delete inactive Account data that is older than 3 years1. You can use an ETL tool or the Data Loader to schedule jobs to export and delete data based on certain criteria3. Option A is not recommended because the Force.com Workbench is a web-based tool that does not support scheduling or automation. Option B is not suitable because the weekly export file from the Salesforce UI does not delete data from Salesforce.

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.

The two recommended options to assign Account ownership to mitigate potential performance problems are to let a “system user” own all the Account records without assigning any role to this user in Role Hierarchy, or to let a “system user” own the Account records and assign this user to the lowest-level role in the Role Hierarchy. This is because these options would reduce the number of sharing calculations and rules that need to be applied to the Account records, and improve the performance and scalability of the system34. The other options are not recommended, as they would increase the sharing complexity and overhead, and potentially expose sensitive data to unauthorized users.

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.

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.

The data classification feature allows customers to classify their data in Salesforce based on two policies:

Data sensitivity level: This policy defines how sensitive the data is and what level of protection it requires. For example, high sensitivity data may require encryption or masking.

Compliance categorization policy: This policy defines how the data is regulated by various laws and standards. For example, GDPR or PCI DSS.

Option B is correct because data model is a key artifact that an architect should use to document the data architecture for a multi-system, enterprise Salesforce implementation1. Data model describes the structure and relationship of data entities within an organization2. Option C is correct because integration specification is another key artifact that an architect should use to document the data architecture for a multi-system, enterprise Salesforce implementation1. Integration specification defines the scope, requirements, design, testing, and deployment of integration solutions between Salesforce and other systems3. Option A is not correct because user stories are not key artifacts for documenting the data architecture, but agile development tools that capture the features and functionalities that users want from a system4. Option D is not correct because non-functional requirements are not key artifacts for documenting the data architecture, but quality attributes that specify how well a system performs its functions.

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

The 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.