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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Salesforce Data Management | 25% | - Describe the considerations for data governance - Describe the considerations for data migration - Describe the considerations for data archival - Describe the implications of LDV on data management |
| Topic 2: Large Data Volumes | 20% | - Describe the considerations for Large Data Volumes (LDV) - Describe the implications of LDV on data modeling - Describe the features and best practices for LDV |
| Topic 3: Data Migration | 15% | - Describe the implications of data migration on data quality - Describe the tools and best practices for data migration - Describe the considerations for data migration |
| Topic 4: Data Governance | 10% | - Describe the considerations for data classification - Describe the elements of a data governance framework - Describe the considerations for data quality |
| Topic 5: Master Data Management | 5% | - Describe the methods for aligning data across systems - Describe the considerations for a Master Data Management strategy |
| Topic 6: Data Modeling/Database Design | 25% | - Describe the considerations when designing a data model for Salesforce - Describe the implications of LDV on data modeling - Describe the options and considerations for Large Data Volumes (LDV) - Explain the usage of external objects |
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137. Frage
Universal Containers (UC) has a requirement to create an Account plan object that is related to the Account object. Each Account plan needs to have an Account object, but the accessibility requirement of the Account plan is different from the Account object. What should an Architect recommend?
Antwort: A
Begründung:
Creating an account plan object with a lookup relationship to Account with validation rules to enforce the Account association can help UC meet their requirement. A lookup relationship allows different accessibility requirements for the account plan object and the account object, as well as different ownership and sharing settings. A validation rule can ensure that each account plan has an account associated with it.
138. Frage
To avoid creating duplicate Contacts, a customer frequently uses Data Loader to upsert Contact records into Salesforce. What common error should the data architect be aware of when using upsert?
Antwort: D
Begründung:
Data Loader uses external Id fields to match records in the CSV file with records in Salesforce during an upsert operation. If the CSV file contains duplicate external Id values within the same file, Data Loader will throw an error saying "Duplicate Id Specified" and will not process those records. Therefore, it is important to ensure that the CSV file does not have any duplicate external Id values before using Data Loader to upsert records.
139. Frage
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?
Antwort: B
Begründung:
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.
140. Frage
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?
Antwort: B,D,E
Begründung:
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.
141. Frage
UC is migrating individual customers (B2C) data from legacy systems to SF. There are millions of customers stored as accounts and contacts in legacy database. Which object model should a data architect configure within SF?
Antwort: C
Begründung:
The best object model to configure within SF for migrating individual customers (B2C) data from legacy systems is to leverage person account object in Salesforce. Person accounts are a special type of accounts that store information about individual people by combining certain account and contact fields into a single record. Person accounts are useful for B2C scenarios where there is no need to associate a company name with a contact. Person accounts also support standard Salesforce features and functionality, such as leads, campaigns, reports, dashboards, etc.
Leverage custom person account object in SF is not a good option because there is no such thing as a custom person account object. Leverage custom account and contact object in SF is not a good option because it would require creating and maintaining additional objects and fields that may not be necessary or compatible with standard Salesforce features. Leverage standard account and contact object in SF is not a good option because it would require filling in dummy values for the account name field, which is mandatory for standard accounts.
142. Frage
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