Question 1

In a story investigating margin, large variations between product groups are dominating the story. Which action might an Einstein Consultant take to resolve this?

Options :

A : Add more data to the dataset.

B : Normalize the margin by product group.

C : Eliminate extreme values in the dataset.

D : Run separate stories for each region.

Answer: C

Question 2

A company uses role hierarchy to implement data security in Salesforce. They want the same role-hierarchy-based security model to apply in Einstein Analytics, but want the users in a role to see data owned by other users in the same role.
Using the 'flatten' transformation, which attribute setting achieves this requirement?

Options :

A : Set the attribute 'selfjield' to true.

B : Set the attribute 'selfjield' to false.

C : Set the attribute 'include_self_id' to true.

D : Set the attribute 'include_self_id' to false.

Answer: C

Question 3

What's the magic of faceting?

Options :

A : It binds charts together so that selecting something in one chart filters other charts by that choice.

B : It shifts the time period of the chart you're viewing.

C : It turns the dataflow on and off.

D : It creates a chart that shows all the faces of your data in a diamond chart.

Answer: A

Question 4

After the initial creation of a story, the first story insight explains 93% of the variation of the outcome variable. This is unusual high? What is the most likely multiple for this?

Options :

A : The dataset contains multiple dominant values.

B : The dataset contains too many rows.

C : The dataset used in the story suffers from too many outlier values.

D : The outcome variable is causing data leakage.

Answer: A

Question 5

Max story creations per org per month

Options :

A : 1000 (can purchase more)

B : 500 (can purchase more)

C : 200 (can purchase more)

D : 500

Answer: B