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Salesforce Analytics-Con-201 Exam Syllabus Topics:

SectionWeightObjectives
Data Layer23%- Data Sources & Integration
  • 1. Extract, load, and prepare data using Data Manager
  • 2. Define datasets and map Salesforce objects
  • 3. Use Dataflows and Recipes for transformation
- Data Management & Optimization
  • 1. Implement data modeling and relationships
  • 2. Resolve data sync and refresh issues
  • 3. Optimize dataset performance
Analytics Administration & Deployment18%- Implementation & Maintenance
  • 1. Manage versioning and deployment
  • 2. Use Dashboard Inspector and Publisher
  • 3. Monitor and optimize query performance
Security & Access12%- Sharing & Permissions
  • 1. Configure app, dataset, and field-level security
  • 2. Manage user and group access
  • 3. Implement row-level security and inheritance
Dashboard Design & Development25%- Design Principles
  • 1. Apply best practices for usability and performance
  • 2. Design for mobile and Lightning compatibility
- Build & Configure Dashboards
  • 1. Implement bindings, filters, and interactions
  • 2. Create lenses, steps, and visualizations
  • 3. Use SAQL for advanced queries
Einstein Discovery22%- Model Building & Configuration
  • 1. Train, evaluate, and refine models
  • 2. Prepare and assess data for modeling
  • 3. Select prediction type: numeric, binary, multi-class
- Deployment & Usage
  • 1. Monitor and maintain model performance
  • 2. Interpret results and recommendations
  • 3. Embed predictions in dashboards and Lightning pages

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Salesforce Certified CRM Analytics and Einstein Discovery Consultant Sample Questions (Q29-Q34):

NEW QUESTION # 29
A company realizes it has a lot of rich information around its cases, but unfortunately, most of this isunstructured/textual data. Thecompany is exploring how to include some of this information in its case prioritization.
Which option within CRM Analytics should a consultant leverage?

Answer: A

Explanation:
For a company with a wealth of unstructured textual data in their cases, the "Detect Sentiment" transformation within CRM Analytics Recipes is a crucial tool. This transformation analyzes the sentiment of the text data- whether it's positive, neutral, or negative-and this insight can be highly valuable in case prioritization processes. Here's why this transformation is useful:
* Insight into Customer Sentiments: By detecting sentiment, the company can prioritize cases based on the urgency and emotional tone expressed in the text, which might indicate customer dissatisfaction or urgency.
* Automation and Efficiency: Automatically categorizing cases based on sentiment can streamline workflows and ensure that critical cases are handled promptly.
* Enhanced Customer Service: Responding to negative sentiments swiftly can improve customer satisfaction and potentially mitigate issues before they escalate.


NEW QUESTION # 30
The below image shows a numeric outcome being deployed (Regression).

Which metric is used to calculate the performance of the model in production, specifically in the Model Manager?
The below image shows a numeric outcome being deployed (Regression).
Which metric is used to calculate the performance of the model in production, specifically in the Model Manager?

Answer: C

Explanation:
In the context of a regression model being deployed, the performance metrics used to evaluate its effectiveness in production typically include:
* Root Mean Square Error (RMSE): This metric provides a measure of the average magnitude of the errors between predicted values by the model and the actual values, giving a sense of how accurately the model predicts the outcome.
* Minimum Square Error: While less commonly referenced as "Minimum Square Error", metrics like Mean Squared Error (MSE) are often used to quantify the average of the squares of the errors- essentially, the average squared difference between the estimated values and what is estimated.
These metrics are crucial for assessing the performance of regression models in CRM Analytics, as they directly reflect the accuracy and reliability of the model's predictions in real-world applications.


NEW QUESTION # 31
A consultant is building a CRM Analytics dashboard for Universal
Containers. The consultant has enabled data sync to increase the
speed of datasets refreshing.
How often will the data on the dashboard be refreshed?

Answer: A


NEW QUESTION # 32
What can a consultant accomplish in the Predictions page of a model?

Answer: C

Explanation:
In the Predictions page of a model within CRM Analytics (formerly Einstein Discovery), users have the capability to interact with the model to see how different input values affect predictions. Here's a detailed look at what can be accomplished:
* Manual Input of Predictor Values: Users can manually input or change values for predictor fields to see how these changes alter the predicted outcomes. This is especially useful for testing hypothetical scenarios and understanding how sensitive the model is to various inputs.
* Real-Time Interaction: This functionality provides a hands-on way to explore the model's behavior, offering immediate feedback on how input variations impact predictions, which can help in refining the model or in training users on its application.
* Scenario Analysis: By adjusting predictor values, users can conduct scenario analyses, which are crucial for strategic planning and decision-making.


NEW QUESTION # 33
Cloud Kicks needs a CRM Analytics consultant to install the Appointment Analytics App. After installation, they realize the wrong field was picked and the app did not have access to a newly created field that should be used instead of the old one.
What is the first step the consultant should take to prevent erroneous dataset/dashboard creation?

Answer: A

Explanation:
If the wrong field is selected in the initial setup of an app or dataset, it is important to stop any data processing activities (like recipe executions) to prevent erroneous data from being loaded into datasets and dashboards. In this case, stopping the recipe from running in Data Manager is the correct first step. Once the recipe is stopped, the consultant can update the field selection or make other necessary corrections before restarting the process.


NEW QUESTION # 34
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