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Salesforce Analytics-Con-201 Exam Syllabus Topics:

SectionObjectives
Einstein Discovery- Model building and predictions
- Insights interpretation and recommendations
Data Integration and Connectivity- Connecting external data sources
- Data synchronization and refresh strategies
Dashboard and App Development- Lens creation and dashboard design
- Interactive filters and user experience optimization
Data Management and Preparation- Dataset creation and configuration in CRM Analytics
- Data transformation and dataflows
Security and Access- App and dataset permissions
- Row-level security and sharing rules

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Salesforce Certified CRM Analytics and Einstein Discovery Consultant Sample Questions (Q11-Q16):

NEW QUESTION # 11
Universal Containers intends to use a custom Salesforce big object in its org and visualize the data using CRM Analytics. As the number of rows to be synced is quite large, the CRM Analytics consultant is looking to set up an incremental sync with additional filters added as part of the data sync to improve performance.
What should the consultant keep in mind while implementing this?

Answer: C


NEW QUESTION # 12
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: B

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 # 13
Exhibit.

Given that the queries are using different datasets, which change should a CRM Analytics consultant make to solve this issue?

Answer: C


NEW QUESTION # 14
Universal Containers (UC) uses a Microsoft Azure SQL Data Warehouse to gather information about sales reps' objectives. UC wants to use CRM Analytics to gain insights from this data and automatically load it into a CRM Analytics dataset daily. The data also needs to be transformed and merged with data from the company's org.
Which CRM Analytics user interface features should be used to complete these requirements?

Answer: A

Explanation:
Reference:CRM Analytics Connectors and Dataflows


NEW QUESTION # 15
Universal Containers has a single dataset that contains the attainment and commission fields for all sales reps.
Each sales rep should be able view the attainment data for each rep in their division. Each repshould only be able to see their own commission data.
Which option should a CRM Analytics consultant use to enforce this requirement?

Answer: B


NEW QUESTION # 16
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