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| Section | Weight | Objectives |
|---|---|---|
| Salesforce Data Management | 25% | - Describe the implications of LDV on data management - Describe the considerations for data governance - Describe the considerations for data archival - Describe the considerations for data migration |
| 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 |
| Data Governance | 10% | - Describe the elements of a data governance framework - Describe the considerations for data classification - Describe the considerations for data quality |
| Data Modeling/Database Design | 25% | - Describe the considerations when designing a data model for Salesforce - Describe the implications of LDV on data modeling - Explain the usage of external objects - Describe the options and considerations for Large Data Volumes (LDV) |
| 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 |
| Master Data Management | 5% | - Describe the considerations for a Master Data Management strategy - Describe the methods for aligning data across systems |
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NEW QUESTION # 65
Universal Containers (UC) uses the following Salesforce products:
- Sales Cloud for customer management.
- Marketing Cloud for marketing.
- Einstein Analytics for business reporting.
UC occasionally gets a list of prospects from third-party source as comma-separated values (CSV) files for marketing purposes. Historically, UC would load contact Lead object in Salesforce and sync to Marketing Cloud to send marketing communications. The number of records in the Lead object has grown over time and has been consuming large amounts of storage in Sales Cloud, UC is looking for recommendations to reduce the storage and advice on how to optimize the marketing Cloud to send marketing communications. The number of records in the Lead object has grown over time and has been consuming large amounts of storage in Sales Cloud, UC is looking for recommendations to reduce the storage and advice on how to optimize the marketing process.
What should a data architect recommend to UC in order to immediately avoid storage issues in the future?
Answer: C
Explanation:
According to the Salesforce documentation, Marketing Cloud is a platform that allows creating and managing marketing campaigns across multiple channels, such as email, mobile, social media, web, etc. Marketing Cloud can integrate with Sales Cloud and other Salesforce products to share data and insights. One of the ways to integrate Marketing Cloud with Sales Cloud is using Marketing Cloud Connect, which allows syncing data between the two platforms using synchronized data sources.
However, if UC occasionally gets a list of prospects from third-party sources as CSV files for marketing purposes, it may not be necessary or efficient to load them into Sales Cloud first and then sync them with Marketing Cloud. This can consume large amounts of storage in Sales Cloud, which has a limit based on the license type. It can also cause data quality issues, such as duplicates or outdated information.
A better option for UC is to load the contacts directly to Marketing Cloud using Import Definition, which allows importing data from external files or databases into Marketing Cloud data extensions. Data extensions are custom tables that store marketing data in Marketing Cloud. This way, UC can avoid storage issues in Sales Cloud and optimize the marketing process by sending marketing communications directly from Marketing Cloud.
To track prospects that are converted to customers, UC can have a reconciliation process that compares the contacts in Marketing Cloud with the accounts or contacts in Sales Cloud. This can be done using SQL queries or API calls to access and compare data from both platforms.
Alternatively, UC can use Marketing Cloud Connect to sync the converted contacts from Sales Cloud to Marketing Cloud using synchronized data sources.
NEW QUESTION # 66
Northern Trail Outfitters would like to report on the type of customers. A custom field for customer type was created in Account object. Users need to be limited to the following defined choices when entering information in this field:
1. High Value
2. Medium Value
3. Low Value
Which strategy should a data architect recommend to configure customer type?
Answer: C
Explanation:
Single-select restricted picklist with defined choices can be a way to configure customer type. The article states that picklists are fields that allow users to select one or more predefined values from a list, and restricted picklists ensure that users can only select from the defined values. This can help to limit the choices for customer type and ensure data quality.
NEW QUESTION # 67
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: C,D,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 # 68
Cloud Kicks stores Invoice records in a custom object. Invoice records are being sent to the Accounting department with missing States and incorrectly formatted Postal Codes. Which two actions should Cloud Kicks take to improve data quality? (Choose two.)
Answer: A,D
Explanation:
Utilizing a Validation Rule with a REGEX operator on Postal Code and utilizing a Validation Rule with a CONTAINS operator on address fields are two actions that Cloud Kicks should take to improve data quality for their Invoice records. A Validation Rule with a REGEX operator can check if the Postal Code field matches a specific pattern or format, such as a five-digit number or a combination of letters and numbers. A Validation Rule with a CONTAINS operator can check if the address fields contain certain values, such as valid state abbreviations or country names.
These Validation Rules can prevent users from saving invalid or incomplete data and display error messages to guide them to correct the data. The other options are not effective or recommended for improving data quality, as they would either require additional customization, not enforce data standards, or not address the specific issues of missing states and incorrectly formatted postal codes
NEW QUESTION # 69
DreamHouse Realty has an integration that creates records in a Salesforce Custom Object. The Custom Object has a field marked as required on the page layout. DreamHouse Realty has noticed that many of the records coming from the external system are missing data in this field.
The Architect needs to ensure this field always contains data coming from the source system.
Which two approaches should the Architect take? Choose 2 answer
Answer: A,C
Explanation:
Setting up a Validation Rule to prevent blank values and marking the field required in setup at the field level are two approaches that the Architect should take to ensure that the field always contains data coming from the source system. A Validation Rule can display an error message when a record is created or edited with a blank value for the field, and prevent it from being saved. Marking the field required in setup at the field level can enforce the requirement for all records, regardless of the page layout or the source system. The other options are not effective or recommended for ensuring data quality, as they would either not prevent blank values, not apply to all records, or not address the root cause of the problem
NEW QUESTION # 70
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