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| Section | Weight | Objectives |
|---|---|---|
| Data Integration | 15% | - Sharing rules impact on data integration - Handling locks and parallelism during migration - API-based integration (REST, SOAP, Bulk API) - ETL tools and data migration strategies - Real-time vs batch integration patterns |
| Data Architecture | 20% | - Archiving and data lifecycle management - Performance tuning and optimization - Governor limits and bulk processing - Architecture patterns and design principles |
| Master Data Management | 20% | - Data cleansing techniques - Data duplication and matching rules - Identity resolution |
| Data Governance | 20% | - Data quality standards and metrics - Data stewardship roles and responsibilities - Data retention and archiving policies - Compliance and regulatory considerations |
| Data Modeling | 25% | - Normalization and denormalization strategies - Indexing strategies - Big Objects and external objects - Large Data Volumes (LDV) design considerations - Skinny tables for performance optimization |
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NEW QUESTION # 227
Universal Containers (UC) has multi -level account hierarchies that represent departments within their major Accounts. Users are creating duplicate Contacts across multiple departments. UC wants to clean the data so as to have a single Contact across departments. What two solutions should UC implement to cleanse their data? Choose 2 answers
Answer: A,C
NEW QUESTION # 228
Universal Containers (UC) has implemented Sales Cloud and it has been noticed that Sales reps are not entering enough data to run insightful reports and dashboards. UC executives would like to monitor and measure data quality metrics. What solution addresses this requirement?
Answer: C
NEW QUESTION # 229
Developers at Universal Containers need to build a report for the business which displays Accounts opened in the past year grouped by industry. This report will also include information from contacts, opportunities, and orders. There are several million Accounts in the system. Which two options should be recommended to make this report perform well and satisfy the business need?
Answer: A,C
Explanation:
Using an indexed data field with bounded data filters can improve the report performance by making the query more selective and reducing the number of records to scan. Using formula fields to surface information from related entities on the report can also enhance the report performance by avoiding joins and complex calculations
NEW QUESTION # 230
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: B,D
NEW QUESTION # 231
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 # 232
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