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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Master Data Management | 20% | - Data duplication and matching rules - Identity resolution - Data cleansing techniques |
| Topic 2: Data Integration | 15% | - Real-time vs batch integration patterns - ETL tools and data migration strategies - Handling locks and parallelism during migration - API-based integration (REST, SOAP, Bulk API) - Sharing rules impact on data integration |
| Topic 3: Data Governance | 20% | - Data stewardship roles and responsibilities - Compliance and regulatory considerations - Data quality standards and metrics - Data retention and archiving policies |
| Topic 4: Data Modeling | 25% | - Skinny tables for performance optimization - Large Data Volumes (LDV) design considerations - Indexing strategies - Big Objects and external objects - Normalization and denormalization strategies |
| Topic 5: Data Architecture | 20% | - Architecture patterns and design principles - Governor limits and bulk processing - Archiving and data lifecycle management - Performance tuning and optimization |
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NEW QUESTION # 45
Universal Containers (UC) needs to move millions of records from an external enterprise resource planning (ERP) system into Salesforce.
What should a data architect recommend to be done while using the Bulk API in serial mode instead of parallel mode?
Answer: C
NEW QUESTION # 46
Universal Containers (UC) management has identified a total of ten text fields on the Contact object as important to capture any changes made to these fields, such as who made the change, when they made the change, what is the old value, and what is the new value. UC needs to be able to report on these field data changes within Salesforce for the past 3 months. What are two approaches that will meet this requirement? Choose 2 answers
Answer: A,C
Explanation:
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.
NEW QUESTION # 47
Universal Containers is creating a new B2C service offering for consumers to ship goods across continents. This is in addition to their well-established B2B offering. Their current Salesforce org uses the standard Account object to track B2B customers. They are expecting to have over 50,000,000 consumers over the next five years across their 50 business regions. B2C customers will be individuals. Household data is not required to be stored. What is the recommended data model for consumer account data to be stored in Salesforce?
Answer: A
NEW QUESTION # 48
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: A
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 # 49
Universal Containers (UC) has lead assignment rules to assign leads to owners. Leads not routed by assignment rules are assigned to a dummy user. Sales rep are complaining of high load times and issues with accessing leads assigned to the dummy user.
What should a data architect recommend to solve these performance issues?
Answer: D
NEW QUESTION # 50
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