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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Data Architecture | 20% | - Architecture patterns and design principles - Performance tuning and optimization - Governor limits and bulk processing - Archiving and data lifecycle management |
| Topic 2: Data Modeling | 25% | - Large Data Volumes (LDV) design considerations - Normalization and denormalization strategies - Indexing strategies - Big Objects and external objects - Skinny tables for performance optimization |
| Topic 3: Master Data Management | 20% | - Identity resolution - Data cleansing techniques - Data duplication and matching rules |
| Topic 4: Data Governance | 20% | - Data retention and archiving policies - Compliance and regulatory considerations - Data quality standards and metrics - Data stewardship roles and responsibilities |
| Topic 5: Data Integration | 15% | - Handling locks and parallelism during migration - ETL tools and data migration strategies - Sharing rules impact on data integration - API-based integration (REST, SOAP, Bulk API) - Real-time vs batch integration patterns |
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NEW QUESTION # 115
Get Cloudy Consulting monitors 15,000 servers, and these servers automatically record their status every 10 minutes. Because of company policy, these status reports must be maintained for 5 years. Managers at Get Cloudy Consulting need access to up to one week's worth of these status reports with all of their details.
An Architect is recommending what data should be integrated into Salesforce and for how long it should be stored in Salesforce.
Which two limits should the Architect be aware of? (Choose two.)
Answer: B,C
NEW QUESTION # 116
Universal Containers (UC) is a business that works directly with individual consumers (B2C). They are moving from a current home-grown CRM system to Salesforce. UC has about one million consumer records. What should the architect recommend for optimal use of Salesforce functionality and also to avoid data loading issues?
Answer: C
Explanation:
According to the exam guide, one of the objectives is to "describe best practices for implementing a single-org strategy in a B2C scenario"1. This implies that option D is the best practice for loading individual consumers as contacts in Salesforce. This approach avoids creating unnecessary accounts and reduces data duplication. Option C is not correct because it creates one account per contact, which increases data volume and complexity. Options A and B are not correct because they do not leverage the standard contact object, which provides native functionality and integration with other Salesforce features.
NEW QUESTION # 117
Universal Containers (UC) is migrating from a legacy system to Salesforce CRM, UC is concerned about the quality of data being entered by users and through external integrations.
Which two solutions should a data architect recommend to mitigate data quality issues?
Answer: C,D
Explanation:
According to the Salesforce documentation1, 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 mitigate data quality issues, some of the recommended solutions are:
Leverage picklist and lookup fields where possible (option A). This means using fields that restrict the values or references that can be entered by the users or integrations. This can help reduce data entry errors, enforce data consistency, and improve data accuracy.
Leverage validation rules and workflows (option C). 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.
Leveraging Apex to validate the format of data being entered via a mobile device (option B) is not a good solution, as it can be complex, costly, and difficult to maintain. It is better to use standard features or declarative tools that can handle data validation more effectively. Leveraging third-party AppExchange tools (option D) is also not a good solution, as it can incur additional costs and dependencies. It is better to use native Salesforce features or custom solutions that can handle data quality more efficiently.
NEW QUESTION # 118
UC is planning a massive SF implementation with large volumes of dat
a. 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: A
NEW QUESTION # 119
A shipping and logistics company has created a large number of reports within Sales Cloud since Salesforce was introduced. Some of these reports analyze large amounts of data regarding the whereabouts of the company's containers, and they are starting to time out when users are trying to run the reports. What is a recommended approach to avoid these time-out issues?
Answer: A
Explanation:
Improving reporting performance by replacing the existing reports in Sales Cloud with new reports based on Analytics Cloud can avoid the time-out issues by leveraging the power and scalability of Analytics Cloud. Analytics Cloud can handle large volumes of data and provide faster and more interactive reports than Sales Cloud .
NEW QUESTION # 120
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