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UC is preparing to implement sales cloud and would like to its users to have read only access to an account record if they have access to its child opportunity record. How would a data architect implement this sharing requirement between objects?
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.
UC is rolling out Sales App globally to bring sales teams together on one platform. UC expects millions of opportunities and accounts to be creates and is concerned about the performance of the application.
Which 3 recommendations should the data architect make to avoid the data skew? Choose 3 answers.
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
NTO would like to retrieve their SF orgs meta data programmatically for backup within a various external. Which API is the best fit for accomplishing this task?
The best API for retrieving Salesforce org metadata programmatically is the Metadata API. The Metadata API provides access to the metadata that defines the structure and configuration of an org, such as custom objects, fields, workflows, security settings, etc. It also supports deploying, retrieving, creating, updating, and deleting metadata components. The Metadata API can be used with various tools, such as Ant, Workbench, or IDEs.
Universal Containers (UC) has 1,000 accounts and 50,000 opportunities. UC has an enterprise security requirement to export all sales data outside of Salesforce on a weekly basis. The security requirement also calls for exporting key operational data that includes events such as file downloads, logins, logouts, etc. Which two recommended approaches would address the above requirement?
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.
Northern Trail Outfitter has implemented Salesforce for its associates nationwide, Senior management is concerned that the executive dashboard is not reliable for their real-time decision-making. On analysis, the team the following issues with data entered in Salesforce.
Information in certain records is incomplete.
Incorrect entry in certain fields causes records to be excluded in report fitters.
Duplicate entries cause incorrect counts.
Which three steps should a data architect recommend to address the issues?
According to the Salesforce documentation3, data quality is the measure of how well the data in Salesforce meets the expectations and requirements of the users and stakeholders. Data quality can be affected by various factors, such as data entry errors, data duplication, data inconsistency, data incompleteness, data timeliness, etc. To address the issues with data quality that affect the reliability of executive dashboards, a data architect should recommend:
Building a sales data warehouse with purpose-built data marts for dashboards and senior management reporting (option B). This means creating a separate database or system that stores and organizes sales data from Salesforce and other sources for analytical purposes. A data warehouse can provide a single source of truth for sales data and enable faster and more accurate reporting and analysis. A data mart is a subset of a data warehouse that focuses on a specific subject or business area, such as sales performance, customer segmentation, product profitability, etc. A data mart can provide tailored and relevant data for different users or groups based on their needs and interests.
Exploring third-party data providers to enrich and augment information entered in Salesforce (option C). This means using external services or tools that can validate, correct, update, and enhance the data that is entered or imported into Salesforce. This can help improve data quality and accuracy, and reduce data duplication and incompleteness.
Leveraging Salesforce features, such as validation rules, to avoid incomplete and incorrect records (option D). This means using features that allow defining rules and criteria to validate the data that is entered or updated by the users or integrations. This can help prevent invalid or incorrect data from being saved, and trigger actions or alerts to correct or improve the data.
Periodically exporting data to cleanse data and import them back into Salesforce for executive reports (option A) is not a good solution, as it can be time-consuming, error-prone, and inefficient. It may also cause data inconsistency and synchronization issues between Salesforce and other systems. Designing and implementing data-quality dashboard to monitor and act on records that are incomplete or incorrect (option E) is also not a good solution, as it can be complex, costly, and difficult to maintain. It may also not address the root causes of data quality issues or prevent them from occurring in the first place.
