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| Section | Weight | Objectives |
|---|---|---|
| Data Migration | 15% | - Migration strategy and planning
|
| Data Integration | 20% | - External data sources
|
| Master Data Management | 5% | - MDM implementation approaches
|
| Data Governance, Quality and Security | 15% | - Data quality management
|
| Large Data Volumes and Performance | 20% | - LDV architecture design
|
| Data Modeling and Database Design | 25% | - Object type selection
|
>> Plat-Arch-201 New Test Materials <<
Thousands of people are interested in earning the Salesforce Certified Platform Data Architect (Plat-Arch-201) certification exam because it comes with multiple career benefits. Prep4sureExam have designed a product that contains the Plat-Arch-201 latest questions. These Salesforce Plat-Arch-201 Exam Dumps are ideal for applicants who have a short time and want to clear the Salesforce Certified Platform Data Architect (Plat-Arch-201) exam for the betterment of their future.
NEW QUESTION # 52
Universal Containers (UC) has adopted Salesforce as its primary sales automated tool. UC has
100,00 customers with a growth rate of 10% a year, UC uses an on-premise web-based billing and invoice system that generates over 1 million invoices a year supporting a monthly billing cycle.
The UC sales team needs to be able to pull a customer record and view their account status, Invoice history, and opportunities without navigating outside of Salesforce.
What should a data architect use to provide the sales team with the required functionality?
Answer: D
Explanation:
To provide the sales team with the required functionality, a data architect should use a mashup page that will present the billing system records within Salesforce. A mashup page is a web page that combines data from multiple sources into a single integrated view. A mashup page can be created using Visualforce or Lightning Web Components, and can use Salesforce Connect or custom integrations to access external data from the on-premise web-based billing and invoice system. This will allow the sales team to pull a customer record and view their account status, invoice history, and opportunities without navigating outside of Salesforce.
NEW QUESTION # 53
Universal Containers (UC) has a multi-level master-detail relationship for opportunities, a custom opportunity line item object, and a custom discount request. UC has opportunity as master and custom line item object as detail in master-detail relationship. UC also has a custom line item object as master and a custom discount request object as detail in another master-detail relationship. UC has a requirement to show all sums of discounts across line items at an opportunity level. What is the recommended solution to address these requirements?
Answer: B
Explanation:
The recommended solution to address the requirements of Universal Containers (UC) is to roll-up discount request amount at the line-item-level and line-item-level summary discount at the opportunity level. This solution would allow UC to show all sums of discounts across line items at an opportunity level using standard roll-up summary fields. Since UC has a multi-level master- detail relationship for opportunities, a custom opportunity line item object, and a custom discount request object, they can use two levels of roll-up summary fields to aggregate the discount amounts from the child objects to the parent objects. For example, they can create a roll-up summary field on the custom line item object to sum up the discount request amounts from the custom discount request object. Then, they can create another roll-up summary field on the opportunity object to sum up the line-item-level summary discounts from the custom line item object.
NEW QUESTION # 54
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: B,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 # 55
Universal Containers wants to implement a data -quality process to monitor the data that users are manually entering into the system through the Salesforce UI. Which approach should the architect recommend?
Answer: B
Explanation:
Utilizing an app from the AppExchange to create data-quality dashboards is the approach that the architect should recommend for UC to implement a data-quality process to monitor the data that users are manually entering into the system through the Salesforce UI. An app from the AppExchange can provide ready-made or customizable dashboards that can display various metrics and indicators of data quality, such as completeness, accuracy, consistency, timeliness, etc. These dashboards can help UC to measure and evaluate their data quality performance and identify any areas that need improvement or attention. The other options are not relevant or effective for implementing a data-quality process, as they would either not address the issue of manual data entry, not provide data-quality monitoring, or not leverage the benefits of Salesforce.
NEW QUESTION # 56
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,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 # 57
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