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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Data Modeling/Database Design | 25% | - Describe the implications of LDV on data modeling - Explain the usage of external objects - Describe the options and considerations for Large Data Volumes (LDV) - Describe the considerations when designing a data model for Salesforce |
| Topic 2: Salesforce Data Management | 25% | - Describe the considerations for data archival - Describe the considerations for data migration - Describe the implications of LDV on data management - Describe the considerations for data governance |
| Topic 3: Data Governance | 10% | - Describe the considerations for data classification - Describe the considerations for data quality - Describe the elements of a data governance framework |
| Topic 4: Data Migration | 15% | - Describe the tools and best practices for data migration - Describe the considerations for data migration - Describe the implications of data migration on data quality |
| Topic 5: Large Data Volumes | 20% | - Describe the implications of LDV on data modeling - Describe the features and best practices for LDV - Describe the considerations for Large Data Volumes (LDV) |
| Topic 6: Master Data Management | 5% | - Describe the methods for aligning data across systems - Describe the considerations for a Master Data Management strategy |
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NEW QUESTION # 68
Universal Containers has a public website with several forms that create Lead records in Salesforce using the REST API. When designing these forms, which two techniques will help maintain a high level of data quality?
Answer: B,C
Explanation:
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.
NEW QUESTION # 69
UC has multiple SF orgs that are distributed across regional branches. Each branch stores local customer data inside its org's Account and Contact objects. This creates a scenario where UC is unable to view customers across all orgs. UC has an initiative to create a 360-degree view of the customer, as UC would like to see Account and Contact data from all orgs in one place. What should a data architect suggest to achieve this 360-degree view of the customer?
Answer: A
Explanation:
Consolidating the data from each org into a centralized datastore is the best suggestion to achieve a 360-degree view of the customer. This way, UC can have a single source of truth for all customer data and avoid data silos and inconsistencies. The other options are not feasible because they either require complex integration, additional cost, or data duplication.
NEW QUESTION # 70
North Trail Outfitters (NTO) operates a majority of its business from a central Salesforce org, NTO also owns several secondary orgs that the service, finance, and marketing teams work out of, At the moment, there is no integration between central and secondary orgs, leading to data- visibility issues.
Moving forward, NTO has identified that a hub-and-spoke model is the proper architect to manage its data, where the central org is the hub and the secondary orgs are the spokes.
Which tool should a data architect use to orchestrate data between the hub org and spoke orgs?
Answer: C
Explanation:
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.
NEW QUESTION # 71
Universal Containers (UC) has an open sharing model for its Salesforce users to allow all its Salesforce internal users to edit all contacts, regardless of who owns the contact. However, UC management wants to allow only the owner of a contact record to delete that contact. If a user does not own the contact, then the user should not be allowed to delete the record. How should the architect approach the project so that the requirements are met?
Answer: B
Explanation:
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.
NEW QUESTION # 72
UC has a requirement to migrate 100 million order records from a legacy ERP application into the salesforce platform. UC does not have any requirements around reporting on the migrated data.
What should a data architect recommend to reduce the performance degradation of the platform?
Answer: D
Explanation:
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.
NEW QUESTION # 73
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