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Salesforce Analytics-Con-201 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Dashboard Design & Development25%- Build & Configure Dashboards
  • 1. Create lenses, steps, and visualizations
  • 2. Use SAQL for advanced queries
  • 3. Implement bindings, filters, and interactions
- Design Principles
  • 1. Apply best practices for usability and performance
  • 2. Design for mobile and Lightning compatibility
Topic 2: 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
Topic 3: Einstein Discovery22%- Deployment & Usage
  • 1. Embed predictions in dashboards and Lightning pages
  • 2. Interpret results and recommendations
  • 3. Monitor and maintain model performance
- Model Building & Configuration
  • 1. Select prediction type: numeric, binary, multi-class
  • 2. Prepare and assess data for modeling
  • 3. Train, evaluate, and refine models
Topic 4: Data Layer23%- Data Sources & Integration
  • 1. Extract, load, and prepare data using Data Manager
  • 2. Use Dataflows and Recipes for transformation
  • 3. Define datasets and map Salesforce objects
- Data Management & Optimization
  • 1. Implement data modeling and relationships
  • 2. Optimize dataset performance
  • 3. Resolve data sync and refresh issues
Topic 5: Analytics Administration & Deployment18%- Implementation & Maintenance
  • 1. Use Dashboard Inspector and Publisher
  • 2. Monitor and optimize query performance
  • 3. Manage versioning and deployment

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Salesforce Certified CRM Analytics and Einstein Discovery Consultant Sample Questions (Q13-Q18):

NEW QUESTION # 13
A consultant runs the sharing inheritance coverage assessment for the Opportunity object and finds that some records exceed 400 sharing descriptors.
What should the consultant do?

Answer: C

Explanation:
When a record exceeds 400 sharing descriptors, it can cause performance issues or sharing rule complications in CRM Analytics. In such cases, the recommended solution is to usesecurity predicates, which allow fine- tuned control over which data is visible to users based on their sharing rules and permissions. Security predicates reduce the number of sharing descriptors by enforcing security at the dataset level rather than relying solely on record-sharing mechanisms.
Increasing the sharing descriptor limit is not an available option, and Salesforce Support does not typically increase this limit, making the use of security predicates the best approach.
Reference:Security predicatesand their use to optimize data visibility and sharing within CRM Analytics are explained in theCRM Analyticsdocumentation related to security and performance.


NEW QUESTION # 14
CRM Analytics users at Cloud Kicks are granted access to an app with specific dashboards. When trying to download a specific widget, they are unable to do so.
What is causing the issue?

Answer: A

Explanation:
As in the previous question, the issue is related to permissions. Users can be granted access to dashboards and apps, but if their permission set does not explicitly allow the "Download Data" action, they will not be able to download the widget data. This is a common issue encountered when users have restricted permissions, especially in environments where security and data access are tightly controlled.


NEW QUESTION # 15
What is the purpose of the CRM Analytics Dashboard Inspector?

Answer: A

Explanation:
The CRM Analytics Dashboard Inspector is a powerful tool used to troubleshoot and optimize dashboards. Its primary function is to display the underlying SAQL (Salesforce Analytics Query Language) query executed for each widget. It helps users see the final query that is run and the corresponding results. This feature allows CRM Analytics consultants and developers to diagnose issues, optimize performance, and understand how data is being processed in the dashboard.
While the Inspector helps view execution times and identify bottlenecks, it does not automatically resolve performance issues (which is why option B is incorrect). It simply provides visibility into query performance and execution details, allowing the user to make manual optimizations.
Reference: Detailed information on theDashboard Inspectortool can be found under the "Building Lenses, Dashboards, and Apps in CRM Analytics" module and theEinstein Discoverytraining content within Salesforce documentation.


NEW QUESTION # 16
Cloud Kicks has a dashboard that displays accounts and opportunities data in a table that contains actions to open the records in Salesforce. Since the company has allowed several accounts to be created with the same names, when users try to perform actions, they are prompted with only a record ID to select, leaving the users confused and unable to act.
How should the CRM Analytics consultant solve this problem?

Answer: B


NEW QUESTION # 17
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 # 18
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