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| Section | Objectives |
|---|---|
| Topic 1: Data Migration | - Data cleansing and transformation - Data migration strategies and planning |
| Topic 2: Salesforce Data Model and Platform Capabilities | - Reporting and analytics considerations - Standard and custom objects - Relationships and schema design |
| Topic 3: Data Management | - Data governance principles - Data quality management - Master data management (MDM) concepts |
| Topic 4: Data Integration | - Integration patterns in Salesforce - API-based integration approaches - ETL vs ELT strategies |
| Topic 5: Data Architecture and Design | - Data modeling best practices in Salesforce - Conceptual and logical data modeling - Handling large data volume and scalability considerations |
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NEW QUESTION # 28
What is a key cause of ownership data skew?
Answer: B
Explanation:
Ownership skew occurs when one user owns a large number of records, affecting performance.
NEW QUESTION # 29
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: B,C,E
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 # 30
To address different compliance requirements, such as general data protection regulation (GDPR), personally identifiable information (PII), of health insurance Portability and Accountability Act (HIPPA) and others, a SF customer decided to categorize each data element in SF with the following:
- Data owner
- Security Level, such as confidential
- Compliance types such as GDPR, PII, HIPPA
A compliance audit would require SF admins to generate reports to manage compliance.
What should a data architect recommend to address this requirement?
Answer: D
Explanation:
The data architect should recommend using field metadata attributes for compliance categorization, data owner, and data sensitivity level. This will allow the SF admins to generate reports to manage compliance based on the field metadata attributes that are defined for each data element in SF.
NEW QUESTION # 31
A Salesforce customer has plenty of data storage. Sales Reps are complaining that searches are bringing back old records that aren't relevant any longer. Sales Managers need the data for their historical reporting. What strategy should a data architect use to ensure a better user experience for the Sales Reps?
Answer: A
Explanation:
Archiving and purging old data from Salesforce on a monthly basis is a good strategy to improve the user experience for the Sales Reps, as it will reduce the clutter and improve the search performance. Creating a permission set or setting data access to private are not effective ways to hide old data from Sales Reps, as they will still consume data storage and affect search results.
Using Batch Apex to archive old data on a rolling nightly basis is also not a good option, as it will consume API requests and processing time, and may not comply with the data retention policy.
NEW QUESTION # 32
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,C
Explanation:
Option B is correct because data model is a key artifact that an architect should use to document the data architecture for a multi-system, enterprise Salesforce implementation. Data model describes the structure and relationship of data entities within an organization. Option C is correct because integration specification is another key artifact that an architect should use to document the data architecture for a multi-system, enterprise Salesforce implementation. Integration specification defines the scope, requirements, design, testing, and deployment of integration solutions between Salesforce and other systems.
NEW QUESTION # 33
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