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Salesforce Analytics-Con-201 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Einstein Discovery22%- Deployment & Usage
  • 1. Monitor and maintain model performance
  • 2. Embed predictions in dashboards and Lightning pages
  • 3. Interpret results and recommendations
- Model Building & Configuration
  • 1. Select prediction type: numeric, binary, multi-class
  • 2. Train, evaluate, and refine models
  • 3. Prepare and assess data for modeling
Topic 2: Security & Access12%- Sharing & Permissions
  • 1. Manage user and group access
  • 2. Implement row-level security and inheritance
  • 3. Configure app, dataset, and field-level security
Topic 3: Dashboard Design & Development25%- Design Principles
  • 1. Design for mobile and Lightning compatibility
  • 2. Apply best practices for usability and performance
- Build & Configure Dashboards
  • 1. Use SAQL for advanced queries
  • 2. Implement bindings, filters, and interactions
  • 3. Create lenses, steps, and visualizations
Topic 4: 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. Resolve data sync and refresh issues
  • 2. Optimize dataset performance
  • 3. Implement data modeling and relationships
Topic 5: Analytics Administration & Deployment18%- Implementation & Maintenance
  • 1. Manage versioning and deployment
  • 2. Use Dashboard Inspector and Publisher
  • 3. Monitor and optimize query performance

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Salesforce Certified CRM Analytics and Einstein Discovery Consultant Sample Questions (Q93-Q98):

NEW QUESTION # 93
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: A

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 # 94
An CRM Analytics consultant is working with Ursa Major Solar to build a dashboard to understand customer renewals. Each subscription is captured as a Closed Won Opportunity within Salesforce and asingle Account should only have one active subscription. The consultant notices the Opportunity record does NOT specify whether it is a renewal or a net new subscription.
Which data transformation should the consultant use to determine if a subscription is new or a renewal?

Answer: C

Explanation:
To determine whether a subscription is new or a renewal from the Opportunity records in Salesforce, the consultant should utilize a Custom Formula in the data transformation process. Here's the rationale:
* Custom Formula Usage: By employing a custom formula, the consultant can create a logical expression that checks the historical data associated with each account. If an account has previous closed-won opportunities, any new opportunities can be labeled as renewals; otherwise, they are considered new subscriptions.
* Data Insight: This method provides a straightforward way to derive new insights (new vs. renewal) directly from existing data without altering the data structure itself, making it a non-invasive and efficient solution.
* Implementation: The custom formula can be applied in a recipe or directly within a dataflow in CRM Analytics, offering flexibility in how and where the transformation is executed.


NEW QUESTION # 95
What is a benefit of introducing a second local connector?

Answer: A

Explanation:
Introducing a second local connector in CRM Analytics can improve performance by enabling more granular control over data syncs. By having a separate connector, different datasets or recipes can be synchronized independently based on specific refresh needs, reducing load and improving overall performance. This approach helps optimize data flow operations, especially in large-scale deployments with varying data refresh requirements.


NEW QUESTION # 96
A consultant wants to understand what the important predictors are in a model.
Where is this information found?

Answer: B

Explanation:
The important predictors of a model in CRM Analytics can typically be found under theModel 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.


NEW QUESTION # 97
Universal Containers (UC) creates a dataset, "Book11", containing a budget per region per month for the first
6 months of the year, as shown in the graphic below. Now, UC wants to create a lens showing the total budget for each region for each month. Every combination of region and month must be shown in the lens, even if there is no data.

How should a CRM Analytics consultant help UC build this lens?

Answer: A


NEW QUESTION # 98
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