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| Section | Objectives |
|---|---|
| Salesforce Data Model and Platform Capabilities | - Relationships and schema design - Reporting and analytics considerations - Standard and custom objects |
| Data Architecture and Design | - Data modeling best practices in Salesforce - Handling large data volume and scalability considerations - Conceptual and logical data modeling |
| Data Integration | - API-based integration approaches - Integration patterns in Salesforce - ETL vs ELT strategies |
| Data Migration | - Data cleansing and transformation - Data migration strategies and planning |
| Data Management | - Data quality management - Master data management (MDM) concepts - Data governance principles |
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NEW QUESTION # 88
UC has the following system:
- Billing system.
- Customer support system.
- CRM system.
US has been having trouble with business intelligence across the different systems. Recently US implemented a master data management (MDM) solution that will be the system of truth for the customer records.
Which MDM data element is needed to allow reporting across these systems?
Answer: A
Explanation:
The correct answer is A, global unique customer number. A global unique customer number is a data element that can uniquely identify each customer across different systems. It can be used as a key to link customer records from different sources and enable reporting across these systems.
Email address, phone number, and full name are not reliable or consistent identifiers for customers, as they can change over time or be shared by multiple customers.
NEW QUESTION # 89
Northern Trail Outfitter has implemented Salesforce for its associates nationwide, Senior management is concerned that the executive dashboard is not reliable for their real-time decision- making. On analysis, the team the following issues with data entered in Salesforce.
- Information in certain records is incomplete.
- Incorrect entry in certain fields causes records to be excluded in
report fitters.
- Duplicate entries cause incorrect counts.
Which three steps should a data architect recommend to address the issues?
Answer: A,C,D
Explanation:
According to the Salesforce documentation, data quality is the measure of how well the data in Salesforce meets the expectations and requirements of the users and stakeholders. Data quality can be affected by various factors, such as data entry errors, data duplication, data inconsistency, data incompleteness, data timeliness, etc. To address the issues with data quality that affect the reliability of executive dashboards, a data architect should recommend:
Building a sales data warehouse with purpose-built data marts for dashboards and senior management reporting (option B). This means creating a separate database or system that stores and organizes sales data from Salesforce and other sources for analytical purposes. A data warehouse can provide a single source of truth for sales data and enable faster and more accurate reporting and analysis. A data mart is a subset of a data warehouse that focuses on a specific subject or business area, such as sales performance, customer segmentation, product profitability, etc. A data mart can provide tailored and relevant data for different users or groups based on their needs and interests.
Exploring third-party data providers to enrich and augment information entered in Salesforce (option C). This means using external services or tools that can validate, correct, update, and enhance the data that is entered or imported into Salesforce. This can help improve data quality and accuracy, and reduce data duplication and incompleteness.
Leveraging Salesforce features, such as validation rules, to avoid incomplete and incorrect records (option D). This means using features that allow defining rules and criteria to validate the data that is entered or updated by the users or integrations. This can help prevent invalid or incorrect data from being saved, and trigger actions or alerts to correct or improve the data.
NEW QUESTION # 90
Which factor impacts query selectivity the most?
Answer: B
Explanation:
Selectivity depends on how many records match the filter.
NEW QUESTION # 91
Universal Containers (UC) wants to capture information on how data entities are stored within the different applications and systems used within the company. For that purpose, the architecture team decided to create a data dictionary covering the main business domains within UC. Which two common techniques are used building a data dictionary to store information on how business entities are defined?
Answer: A,B
Explanation:
A data dictionary is a document that describes the structure, format, and meaning of data entities and attributes. A common technique to build a data dictionary is to use an entity relationship diagram (ERD), which shows the logical relationships between objects and fields in a graphical way. Another technique is to use the Salesforce Metadata API, which allows you to retrieve and deploy the metadata that defines your Salesforce org
NEW QUESTION # 92
An architect has been asked by a client to develop a solution that will integrate data and resolve duplicates and discrepancies between Salesforce and one or more external systems. What two factors should the architect take into consideration when deciding whether or not to use a Master Data Management system to achieve this solution? Choose 2 answer
Answer: A,B
Explanation:
Whether the system of record changes for different tables and the number of systems that are integrating with each other are two factors that the architect should take into consideration when deciding whether or not to use a Master Data Management system to achieve the solution of integrating data and resolving duplicates and discrepancies between Salesforce and one or more external systems. The system of record is the authoritative source of truth for a given entity or field in a given context. If different systems have different systems of record for different tables, then a Master Data Management system can help to manage and synchronize the data across systems and ensure data quality and consistency. The number of systems that are integrating with each other is another factor that affects the complexity and scalability of the integration solution. If there are many systems that need to integrate with each other, then a Master Data Management system can provide a centralized and standardized way of integrating data and resolving duplicates and discrepancies across systems. The other factors are not relevant or important for deciding whether or not to use a Master Data Management system, as they do not affect the data quality or integration challenges that a Master Data Management system can address.
NEW QUESTION # 93
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