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| Section | Objectives |
|---|---|
| Master Data Management (MDM) | - MDM architecture
|
| Data Modeling and Database Design | - Data modeling concepts
|
| Data Governance and Management | - Security and compliance
|
| Large Data Volume and Performance | - Scalability considerations
|
| Data Integration | - Integration patterns
|
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NEW QUESTION # 205
In their legacy system. Universal Containers has a monthly accounts receivable report that compiles data from Accounts, Contacts, Opportunities, Orders. and Order Line Items. What difficulty will an architect run into when implementing this in Salesforce?
Answer: C
Explanation:
Salesforce allows up to four objects in a single report type. This means that if an architect wants to create a report that compiles data from Accounts, Contacts, Opportunities, Orders, and Order Line Items, they will run into a difficulty because that would require five objects in a single report type. To overcome this limitation, the architect can either create two separate report types and join them in a joined report, or use an external reporting tool that can access data from multiple objects.
NEW QUESTION # 206
Universal Containers (UC) has a Salesforce org with multiple automated processes defined for group membership processing, UC also has multiple admins on staff that perform manual adjustments to the role hierarchy. The automated tasks and manual tasks overlap daily, and UC is experiencing "lock errors" consistently.
What should a data architect recommend to mitigate these errors?
Answer: C
Explanation:
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.
NEW QUESTION # 207
UC is planning a massive SF implementation with large volumes of data. As part of the org's implementation, several roles, territories, groups, and sharing rules have been configured. The data architect has been tasked with loading all of the required data, including user data, in a timely manner.
What should a data architect do to minimize data load times due to system calculations?
Answer: C
Explanation:
The correct answer is A, enable defer sharing calculations, and suspend sharing rule calculations. Defer sharing calculations and suspend sharing rule calculations are features that allow you to temporarily disable the automatic recalculation of sharing rules when you load large volumes of data. This can improve the performance and speed of your data load process by avoiding unnecessary system calculations. Loading the data through data loader, leveraging the bulk API, or enabling granular locking are also options that can help with data load times, but they do not directly address the system calculations issue.
NEW QUESTION # 208
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: B
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.
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.
NEW QUESTION # 209
During the implementation of Salesforce, a customer has the following requirements for Sales Orders:
1. Sales Order information needs to be shown to users in Salesforce.
2. Sales Orders are maintained in the on-premises enterprise resource planning (ERP).
3. Sales Order information has more than 150 million records.
4. Sales Orders will not be updated in Salesforce.
What should a data architect recommend for maintaining Sales Orders in salesforce?
Answer: C
NEW QUESTION # 210
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