At ValidExamDumps, we consistently monitor updates to the Salesforce ANC-301 exam questions by Salesforce. Whenever our team identifies changes in the exam questions, objectives, focus areas or requirements, We immediately update our exam questions for both PDF and online practice exams. This commitment ensures our customers always have access to the most current and accurate questions. By preparing with these up to date and 100% exam domain coverage questions, our customers can successfully pass the Salesforce Implement and Manage CRM Analytics exam on their first attempt without needing additional materials or study guides.
Other certification materials providers often include outdated or removed questions by Salesforce in their ANC-301 exam. These outdated questions lead to customers failing their Salesforce Implement and Manage CRM Analytics exam. In contrast, we ensure our questions bank includes only precise and up-to-date questions. Our main priority is your success in the Salesforce ANC-301 exam, not profiting from selling obsolete exam questions in PDF or Online Practice Test.
Exhibit.
Universal Containers has a dashboard for sales managers to visualize the Year Over Year (YoY) growth of their customers. The formula used is:
YoY = [(This Year --- Last Year) / Last Year] %
Based on the graphic, when there is not an account in the Last Year column, the YoY Growth shows null results. The sales managers want to replace it with 100% value.
What is the correct function to use?
Universal Containers (UC) builds three Einstein Discovery models in Salesforce to predict and maximize its revenue per customer. The models are for every region UC has a business: EMEA, AMER, and APAC.
How should a consultant help UC deploy the three Einstein models to Salesforce?
In deploying Einstein Discovery models that are tailored to different regions (EMEA, AMER, and APAC), the best approach is to segment the account data by region and apply the specific model designed for each segment. This method ensures the following:
Relevance and Accuracy: Each model can be specialized to understand and predict based on regional dynamics, which may differ significantly across geographies in terms of market behavior, customer preferences, and economic conditions.
Efficiency: Deploying region-specific models avoids the dilution of predictive power that might occur if a single model were used across all regions, which could lead to less accurate predictions.
Scalability: This approach is scalable as UC can further refine each model as more regional data becomes available or as regional market conditions evolve.
A consultant wants to understand what the important predictors are in a model.
Where is this information found?
The important predictors of a model in CRM Analytics can typically be found under the Model Settings. This area provides detailed information about the configuration and the inputs (predictors) used to train the model. Insights into which predictors have the most significant impact on the model's outcomes can be gleaned from this section, enabling a deeper understanding of the model's internal workings and the factors driving predictions.
CRM Analytics team is asked to build a Service Analytics dashboard for the service agents.
What are the main "Deep Design Thinking" principles the team should keep in mind during the discovery sessions?
The sales team at Cloud Kicks is requesting that datasets for their dashboards be refreshed every hour. The CRM Analytics consultant investigates if this is possible and finds that the dashboards use five datasets created from two recipes. The first recipe takes 43 minutes to run and the second recipe takes 25 minutes to run.
Which consideration should the consultant keep in mind?
In CRM Analytics, recipes are used to prepare and combine data for datasets. The total duration of the recipe runtime is an important consideration when scheduling dataset refreshes. In this case, the combined runtime for both recipes (43 minutes + 25 minutes = 68 minutes) exceeds 1 hour. Since dataset refreshes cannot be scheduled more frequently than the total recipe runtime, it would be impossible to refresh the datasets every hour. This limitation must be considered when managing dataset refresh schedules.
