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| Section | Weight | Objectives |
|---|---|---|
| Data Architecture | 20% | - Archiving and data lifecycle management - Architecture patterns and design principles - Performance tuning and optimization - Governor limits and bulk processing |
| Data Integration | 15% | - Real-time vs batch integration patterns - ETL tools and data migration strategies - Sharing rules impact on data integration - Handling locks and parallelism during migration - API-based integration (REST, SOAP, Bulk API) |
| Data Governance | 20% | - Data stewardship roles and responsibilities - Data retention and archiving policies - Compliance and regulatory considerations - Data quality standards and metrics |
| Data Modeling | 25% | - Big Objects and external objects - Normalization and denormalization strategies - Indexing strategies - Skinny tables for performance optimization - Large Data Volumes (LDV) design considerations |
| Master Data Management | 20% | - Data cleansing techniques - Identity resolution - Data duplication and matching rules |
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NEW QUESTION # 191
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,B,E
Explanation:
According to the Salesforce documentation3, 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.
Periodically exporting data to cleanse data and import them back into Salesforce for executive reports (option A) is not a good solution, as it can be time-consuming, error-prone, and inefficient. It may also cause data inconsistency and synchronization issues between Salesforce and other systems. Designing and implementing data-quality dashboard to monitor and act on records that are incomplete or incorrect (option E) is also not a good solution, as it can be complex, costly, and difficult to maintain. It may also not address the root causes of data quality issues or prevent them from occurring in the first place.
NEW QUESTION # 192
UC has millions of Cases and are running out of storage. Some user groups need to have access to historical cases for up to 7 years.
Which 2 solutions should a data architect recommend in order to minimize performance and storage issues?
Choose 2 answers:
Answer: B,D
NEW QUESTION # 193
Universal Containers has implemented Salesforce for its operations. In order for customers to be created in their MDM solution, the customer record needs to have the following attributes:
1. First Name
2. Last Name
3. Email
Which option should the data architect recommend to mandate this when customers are created in Salesforce?
Answer: C
NEW QUESTION # 194
What is an advantage of using Custom metadata type over Custom setting?
Answer: B
Explanation:
Custom metadata records are deployable using packages, which makes them easier to migrate from one environment to another. Custom settings records are not deployable using packages, and they are copied from production to sandbox. Custom metadata types are not available for reporting, and custom metadata records are not editable in Apex.
NEW QUESTION # 195
An Architect needs to document the data architecture for a multi-system, enterprise Salesforce implementation.
Which two key artifacts should the Architect use? (Choose two.)
Answer: A,B
NEW QUESTION # 196
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