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Salesforce Certified CRM Analytics and Einstein Discovery Consultant Sample Questions:

1. Exhibit.

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

A) Use "Connect Data Sources" and create a connection to connect the two datasets.
B) Use result binding/Interaction in the filters section of the query "Type 1".
C) Use "Connect Data Sources" and create a connection to connect the two widgets.

2. Cloud Kicks (CK) wants to use CRM Analytics to analyze trends of its sales pipeline in order to accelerate the company's sales process. To do so, CK needs to know the average time an opportunity spends in each stage. The data can be found in the Opportunity History object, but the value is not pre- calculated in Salesforce, so a consultant recommends using a recipe to calculate it.
How should the consultant use a recipe to calculate the average time an opportunity spends in each stage?

A) Flatten transformation with offset expressions to calculate the duration
B) An aggregate transformation with offset parameter to calculate the duration
C) Custom transformation with a multiple row formula to calculate the duration

3. A CRM Analytics administrator is working on deploying a dataflow and a dataset (generated by this dataflow) to another org.
While creating a change set, they notice that the components are NOT visible to be included in the change set.
What is the reason for this?

A) The administrator does NOT have access to the assets on CRM Analytics.
B) The administrator does NOT have system administrator permission to include the assets In the change set,
C) Assets are kept in the Private App and are unavailable to include in the change set.

4. A manager at Cloud Kicks wants to separate and analyze accounts based on numeric information of its opportunity records. The data includes things like amount, quantity of products, contacts, and quotes.
How should the CRM Analytics consultant accomplish this?

A) Cluster in the recipes and select the metrics used for clusterization.
B) Bucket for each measure and then use a global bucket to segment accounts.
C) Aggregate to summarize related data to account level.

5. After the initial creation of a model, the first model insight explains
93% of the variation of the outcome variable. This is unusually high.
What is the most likely reason for this?

A) The dataset contains multiple dominant values.
B) The model contains too many outlier values.
C) The outcome variable may be causing data leakage.

Solutions: