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Salesforce Analytics-Con-201 Exam Syllabus Topics:

Salesforce Certified CRM Analytics and Einstein Discovery Consultant Sample Questions:

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

A) Model Settings
B) Model Deployment Wizard
C) Einstein Recommendations

2. consultant is reviewing a model that is set to maximize the daily sales quantity of consumer products in stores, and they see this recommendation.

Which action should the consultant take?

A) Verifyclient expectations that Store is a strong predictor for daily sales quantity.
B) Ignore alert; the explanation of variation is only 35%, which is below 50%,
C) Remove the Store field from the model definition, because that is the recommended action.

3. Universal Containers has a dashboard for sales managers. They need to visualize the percentage of their opportunities in the pipeline in a Gauge chart. They want to customize the chart to keep track if they are below or beyond the target.

Which widget parameters should a consultant use?

A) Reference Line, Markers, Conditional Formatting
B) Range Values, Angle, Conditional Formatting
C) Reference Line, Angle, Range Values

4. 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?

A) Insert more fields in the dataset action "Display Fields" so the users know which account they are acting on.
B) Ask the admin to start applying duplicate rules based on the account's Name field.
C) Discuss with the stakeholders If the action Is really necessary for the table.

5. A data architect wants to use a recipe transformation to implement row level security based on role hierarchy in Salesforce.
Which transformation should the architect use to level the dataset hierarchy?

A) Aggregate
B) Flatten
C) Join

Solutions: