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| Section | Weight | Objectives |
|---|---|---|
| Design | 29% | - Solution Design
|
| Implement | 41% | - Core Contact Center Configuration
|
| Deploy | 10% | - Deployment Planning
|
| Discovery | 20% | - KPIs and Success Metrics
|
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NEW QUESTION # 198
Your scenario involves migrating to a new chat platform integrated with Salesforce. Which deployment process best facilitates transition with minimal downtime?
Answer: C
Explanation:
Transitioning to a new chat platform integrated with Salesforce can be effectively managed through various deployment strategies, each suitable based on the specific scenario: A. Phased deployment gradually transitions agents and customers, minimizing disruption. B. Parallel deployment allows running both the old and new platforms simultaneously to ensure continuous service. C. Cutover deployment switches systems during a planned downtime, which is typically scheduled for low-usage times. Selecting the appropriate method depends on factors like the complexity of integration and the organization's tolerance for downtime.
NEW QUESTION # 199
You're validating performance requirements for the deployed system. Which tool helps assess system responsiveness and handle load testing?
Answer: B
Explanation:
Assessing system performance and responsiveness involves various tools and approaches:
A: Analyzing system logs and resource utilization data helps identify performance bottlenecks.
B: Utilizing performance testing tools to simulate user activity enables evaluation of system response times and stability under various load conditions.
C: Monitoring agent feedback and user experience reports provides insights into real-world performance issues post-deployment.
These combined approaches offer a comprehensive evaluation of the system's performance, ensuring it meets the required standards for responsiveness and load handling.
NEW QUESTION # 200
The customer expects high data volumes and complex reporting needs. Which solution supports scalability and advanced analysis?
Answer: D
Explanation:
For handling high data volumes and complex reporting needs, combining Salesforce Einstein Analytics with external data warehouse solutions offers the best scalability and advanced analysis capabilities. Einstein Analytics provides AI-powered insights and predictive analysis, while external data warehouses allow for storage of large data volumes and complex querying. This combination, as suggested in option "D. Both (b) and (c) combined for data storage, advanced analysis, and visual data exploration," offers a comprehensive solution for handling large-scale data and sophisticated analytical demands.
Reference: Salesforce Documentation on Einstein Analytics
NEW QUESTION # 201
You encounter unexpected data inconsistencies during migration. Which approach helps maintain data integrity?
Answer: A
Explanation:
When encountering unexpected data inconsistencies during migration, maintaining data integrity is paramount. The best approach involves pausing the migration to thoroughly investigate the inconsistencies, identifying the root cause whether they stem from data corruption, mapping mistakes, or other issues, and resolving these problems before proceeding. This ensures that the data integrity is not compromised, which is crucial for the successful use of the new system. "C. Pausing the migration, investigating the inconsistencies, and fixing the source data or mapping errors" is therefore the most effective strategy for dealing with data inconsistencies during migration.
Reference: Salesforce Data Quality Management
NEW QUESTION # 202
While some of your suggestions aim to address Ursa Major Solar's need to accommodate new agents during peak season, they raise potential concerns and might not be the most optimal approach. Here's a breakdown:
Ursa Major Solar (UMS) is implementing Email-to-Case in its ...
Salesforce notaro which it has used in the past 2 years as its Case
Management system, to place is the party Email Channel provider. UMS is excited to learn about the artificial in capability in Einstein Case Classification and would like to prioritize the capturing the build as must-have After verifying the license, how should a consultant include the Einstein Case Classification capability in the same project?
Add two requirements to the backlog in the current release date
EmailMessage object to meet data requirements for Einstein formal Car
Cassium be up Einstein Case Classification.
Add the requirement to a future release since Einstein rends at least 400 the past 6 months to build the model in Email Channel Add the requirement in the current release as the customer are case records and because of the reed to prontice the manner Classification The most appropriate approach for incorporating Einstein Case Classification into the same project is to:
A) A Add two requirements to the backlog in the current release:
B) Ursa Major Solar has recently seen a big increase in case volume due to the launch of new product.
What should the company do to deflect cases?
Implement an internal knowledge base.
Implement a customer-facing knowledge base Implement a new channel in
SMS. Choose
While both implementing an internal knowledge base and implementing a
customer-facing knowledge base can help deflect cases, the most
impactful choice for Ursa Major Solar is:
Implementing a customer-facing knowledge base.
Ursa Major Solar decided to roll out Einstein Bots in all of the enhanced digital snipe channels for its retail customers, including Facebook Messenger, WhatsApp and Message for In-app and Web. The Salesforce administrators are not sure how many boss they sa and why.
Which recommendation should a consultant make to the Salesforce ..
Answer: A
Explanation:
Facebook Messenger
Explanation:
To incorporate Einstein Case Classification effectively in the ongoing project, the consultant should add two requirements to the backlog in the current release. This approach ensures that the project team can address the need for Einstein Case Classification without delaying the current implementation schedule. The EmailMessage object will need to be configured to meet data requirements for Einstein Case Classification, which will then enable the automated classification and routing of incoming cases based on content analysis. This strategic inclusion in the current release facilitates immediate enhancement of case management efficiency without the wait times associated with gathering historical data, as would be necessary if waiting for a future release.
Reference: https://help.salesforce.com/s/articleView?id=sf.einstein_service_case_classification.htm For deflecting cases, implementing a customer-facing knowledge base is the most impactful choice for Ursa Major Solar. A customer-facing knowledge base allows customers to self-serve by accessing information and solutions directly, which can significantly reduce the volume of incoming cases by addressing common inquiries and issues before they escalate to live support interactions.
Reference: https://help.salesforce.com/s/articleView?id=sf.knowledge_about.htm For the deployment of Einstein Bots across enhanced digital service channels, the recommendation should be to use the same Einstein Bot across all channels (Facebook Messenger, WhatsApp, Message for In-app, and Web). This approach leverages the ability of Einstein Bots to be configured once and deployed across multiple channels, simplifying maintenance and ensuring consistent customer experiences across all digital touchpoints.
Reference: https://help.salesforce.com/s/articleView?id=sf.bots_overview.htm
NEW QUESTION # 203
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