Free Analytics-Con-201 sample questions

Correct answer: Flatten the Region hierarchy into the dataset and use a security predicate comparing the logged-in user's Region attribute to the flattened dataset columns.

Since the security requirement is based on a custom 'Region' object relationship rather than the standard Salesforce Role Hierarchy, standard inheritance won't work directly. The best approach is to flatten the relevant hierarchy or attributes into the dataset during data prep and then use a security predicate (e.g., 'Region' == '$User.Region__c' || 'VP_Flag' == true) to control visibility.

Correct answer: Check for Multicollinearity warnings in the Model Alerts or Data Checker.

Multicollinearity occurs when two or more independent variables are highly correlated (e.g., Region and Support Team, if one team covers exactly one region). Einstein Discovery flags this because it makes it difficult to isolate the effect of each variable. If Region is a proxy for Support Team, they will likely be flagged as collinear.

Correct answer: {{ column(Toggle_Step.selection, ["value"]).asObject() }}

To dynamically replace a measure (which consists of an aggregation function and a field, like ['sum', 'Amount']), you need to project the selection as an object or array of strings that the widget query can understand. The 'column' function is appropriate here to retrieve the selected values, and '.asObject()' formats it correctly for the JSON 'measures' property.

Correct answer: Recipes

Data Prep Recipes are the modern, recommended tool for complex transformations. Unlike Dataflows, Recipes natively support complex window functions (like sliding averages), multi-level aggregations, and provide a visual interface for joining multiple datasets with preview capabilities.

Correct answer: False

CRM Analytics security inheritance supports the Role Hierarchy and Sharing Rules, but it does NOT support Manual Sharing or Apex-managed sharing. For these cases, a security predicate must be used.

Correct answer: The 'Discount' variable was not selected as an 'Actionable' variable in the story configuration.

Einstein Discovery only generates suggestions (Actionable Insights) for variables that are explicitly flagged as 'Actionable' during the story setup. Even if a variable is highly predictive, it won't appear in recommendations if the system believes the user cannot control it.