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| Section | Weight | Objectives |
|---|---|---|
| Master Data Management | 5% | - Describe the methods for aligning data across systems - Describe the considerations for a Master Data Management strategy |
| Data Modeling/Database Design | 25% | - Describe the options and considerations for Large Data Volumes (LDV) - Explain the usage of external objects - Describe the considerations when designing a data model for Salesforce - Describe the implications of LDV on data modeling |
| Large Data Volumes | 20% | - Describe the implications of LDV on data modeling - Describe the considerations for Large Data Volumes (LDV) - Describe the features and best practices for LDV |
| Data Governance | 10% | - Describe the considerations for data quality - Describe the elements of a data governance framework - Describe the considerations for data classification |
| Data Migration | 15% | - Describe the considerations for data migration - Describe the tools and best practices for data migration - Describe the implications of data migration on data quality |
| Salesforce Data Management | 25% | - Describe the considerations for data migration - Describe the considerations for data governance - Describe the implications of LDV on data management - Describe the considerations for data archival |
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NEW QUESTION # 144
Universal Containers has successfully migrated 50 million records into five different objects multiple times in a full copy sandbox. The Integration Engineer wants to re-run the test again a month before it goes live into Production. What is the recommended approach to re-run the test?
Answer: B
Explanation:
The recommended approach to re-run the test is to refresh the full copy sandbox and re-run the data migration test. A full copy sandbox is a replica of the production Org, including all data, metadata, and attachments. Refreshing a full copy sandbox means creating a new copy of the production Org and replacing the existing sandbox. This would ensure that the test is run on a clean and up-to-date environment, without any leftover data or configuration from previous tests.
Truncating or hard deleting objects would not be sufficient, because they would not remove all the data or metadata from the sandbox, and they could also affect other dependent objects or processes.
NEW QUESTION # 145
Universal Containers (UC) is in the process of selling half of its company. As part of this split, UC's main Salesforce org will be divided into two org:org A and org B, UC has delivered these requirements to its data architect
1. The data model for Org B will drastically change with different objects, fields, and picklist values.
2. Three million records will need to be migrated from org A to org B for compliance reasons.
3. The migrate will need occur within the next two month, prior to be split.
Which migrate strategy should a data architect use to successfully migrate the date?
Answer: B
Explanation:
Using an ETL tool to orchestrate the migration is the best strategy for this scenario, as it can handle the data model changes, the large volume of records, and the tight timeline.
NEW QUESTION # 146
Universal Containers is planning out their archiving and purging plans going forward for their custom objects Topic__c and Comment__c. Several options are being considered, including analytics snapshots, offsite storage, scheduled purges, etc. Which three questions should be considered when designing an appropriate archiving strategy?
Answer: A,B,E
Explanation:
The three questions that should be considered when designing an appropriate archiving strategy are: If reporting is necessary, can the information be aggregated into fewer, summary records?
Will the data being archived need to be reported on or accessed in any way in the future? Are there any regulatory restrictions that will influence the archiving and purging plans? These questions are important because they help determine the scope, frequency, and method of archiving and purging data from Salesforce. For example, if reporting is necessary, then summary records or analytics snapshots can be used to store aggregated data and reduce the number of records that need to be archived. If the data being archived needs to be accessed in the future, then offsite storage or external objects can be used to retain the data and make it available on demand. If there are any regulatory restrictions, such as GDPR or HIPAA, then the archiving and purging plans need to comply with them and ensure data security and privacy
NEW QUESTION # 147
A large Automobile company has implemented SF for its Sales Associates. Leads flow from its website to SF using a batch integration in SF. The Batch job connects the leads to Accounts in SF. Customer visiting their retail stores are also created in SF as Accounts.
The company has noticed a large number of duplicate accounts in SF. On analysis, it was found that certain customers could interact with its website and also visit the store. The Sales associates use Global Search to search for customers in Salesforce before they create the customers.
Which scalable option should a data Architect choose to implement to avoid duplicates?
Answer: B
Explanation:
The data architect should choose to implement duplicate rules in SF (Salesforce) to validate duplicates during the account creation process. Duplicate rules are a feature in Salesforce that allow users to define criteria and actions for detecting and preventing duplicate records. By creating duplicate rules for accounts, the data architect can ensure that any leads from the website or customers from the retail stores that match existing accounts in Salesforce are flagged or blocked before they are created as new accounts. This will help avoid duplicate accounts in Salesforce and maintain data quality.
NEW QUESTION # 148
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: A,B
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 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 ules 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 # 149
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