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| Section | Objectives |
|---|---|
| Topic 1: Salesforce Data Model and Platform Capabilities | - Standard and custom objects - Relationships and schema design - Reporting and analytics considerations |
| Topic 2: Data Migration | - Data cleansing and transformation - Data migration strategies and planning |
| Topic 3: Data Integration | - ETL vs ELT strategies - API-based integration approaches - Integration patterns in Salesforce |
| Topic 4: Data Architecture and Design | - Handling large data volume and scalability considerations - Conceptual and logical data modeling - Data modeling best practices in Salesforce |
| Topic 5: Data Management | - Data governance principles - Data quality management - Master data management (MDM) concepts |
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NEW QUESTION # 231
Universal Containers (UC) is building a Service Cloud call center application and has a multisystem support solution. UC would like or ensure that all systems have access to the same customer information. What solution should a data architect recommend?
Answer: A
Explanation:
A master data management (MDM) strategy for customer data can help UC ensure that all systems have access to the same customer information, without loading or duplicating data in all systems. An MDM strategy can also help UC avoid data conflicts and inconsistencies that may arise from having multiple systems as owners of data.
NEW QUESTION # 232
Universal Containers (UC) is implementing a Salesforce project with large volumes of data and daily transactions. The solution includes both real-time web service integrations and Visualforce mash - ups with back-end systems. The Salesforce Full sandbox used by the project integrates with full-scale back-end testing systems. What two types of performance testing are appropriate for this project? Choose 2 answer
Answer: C,D
Explanation:
Pre-go-live automated page-load testing against the Salesforce Full sandbox can help identify and resolve any performance bottlenecks or issues before deploying the solution to production.
The Full sandbox is an ideal environment for performance testing as it replicates the production org in terms of data, metadata, and integrations. Stress testing against the web services hosted by the integration middleware can also help evaluate the scalability and reliability of the integration solution under high load conditions.
NEW QUESTION # 233
UC has one SF org (Org A) and recently acquired a secondary company with its own Salesforce org (Org B). UC has decided to keep the orgs running separately but would like to bidirectionally share opportunities between the orgs in near-real time. Which 3 options should a data architect recommend to share data between Org A and Org B? Choose 3 answer
Answer: C,D,E
Explanation:
Leveraging Heroku Connect and Heroku Postgres, middleware tools, or Salesforce Connect and the cross-org adapter are all viable options to share data between Org A and Org B in near-real time. Installing a 3rd party AppExchange tool may not provide bidirectional sync or may have additional costs. Developing an Apex class that pushes opportunity data between orgs daily via the Apex schedule may not meet the near-real time requirement.
NEW QUESTION # 234
DreamHouse Realty has a legacy system that captures Branch Offices and Transactions.
DreamHouse Realty has 15 Branch Offices. Transactions can relate to any Branch Office.
DreamHouse Realty has created hundreds of thousands of Transactions per year. A Data Architect needs to denormalize this data model into a single Transaction object with a Branch Office picklist. What are two important considerations for the Data Architect in this scenario?
(Choose two.)
Answer: B,C
Explanation:
The Data Architect should consider the limitations on Org data storage and the Bulk API limitations on picklist fields when denormalizing the data model into a single Transaction object with a Branch Office picklist. The Org data storage limit is the total amount of data that can be stored in a Salesforce Org, and it depends on the edition and license type of the Org. The Bulk API limit on picklist fields is the maximum number of values that can be imported or exported using the Bulk API, and it is 1,000 values per picklist field. These limitations could affect the performance and scalability of the data model, and the Data Architect should plan accordingly.
NEW QUESTION # 235
NTO need to extract 50 million records from a custom object everyday from its Salesforce org.
NTO is facing query timeout issues while extracting these records. What should a data architect recommend in order to get around the time out issue?
Answer: D
Explanation:
The best solution to extract 50 million records from a custom object everyday from Salesforce org without facing query timeout issues is to use an ETL tool for extraction of records. ETL stands for extract, transform, and load, and it refers to a process of moving data from one system to another. An ETL tool is a software application that can connect to various data sources, perform data transformations, and load data into a target destination. ETL tools can handle large volumes of data efficiently and reliably, and they often provide features such as scheduling, monitoring, error handling, and logging. Using a custom auto number and formula field and use that to chunk records while extracting data is a possible workaround, but it requires creating additional fields and writing complex queries. The REST API can extract data as it automatically chunks records by 200, but it has some limitations, such as a maximum of 50 million records per query job.
Asking SF support to increase the query timeout value is not feasible because query timeout values are not configurable
NEW QUESTION # 236
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