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Salesforce Certified CRM Analytics and Einstein Discovery Consultant Sample Questions:

1. At Universal Containers, a dashboard is built to track sales metrics. The Opportunity amount field has conditional formatting added - it turns green for closed won and red for closed lost.
How should a CRM Analytics consultant enhance the dashboard design to become more accessible to users with color vision challenges?

A) Position Alt Texts next to the KPIs to make itmore understandable.
B) Make the numbers bold to emphasize whether it is won or lost.
C) Add up and down arrow next to the KPIs to make it more understandable.

2. What are various ways to incorporate blank space in a CRM Analytics dashboard?

A) 1. Use the "Cell Spacing" layout property.2. Increase the dashboard granularity via columns, and use blank columns.
B) 1. Increase the dashboard granularity via columns, and use blank columns.Use pages to break content into multiple tabs.
C) Use the "Fine" row height option in layout properties, and use blank rows,Use the "With Spacing" row height property.

3. A model created with a GLM algorithm produced unsatisfactory results.
When re-running the model, which type of algorithm should the consultant use to improve the results?

A) Support Vector Machines
B) K-Nearest Neighbors
C) XGBoost

4. 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?

A) Only SOSL queries can be used as data syne filters.
B) Incremental data syne Is NOT supported for big objects.
C) Custom big objects are NOT supported by CRM Analytics.

5.
Which timeseries statement will fix the query?

A) q= timeseries q generate 'sum_Sales' as 'Forecasted_Sales' with (dateCols=('Date_Year', 'Date Quarter', "Y-Q"), partition='Region', seasonality=4);
B) q = timeserles q generate 'sum_Sales' as 'Forecasted_Sales' with (length=12,dateCols=("Date_Year',
'Date_Month', "Y-M"), partition='Region');
C) q = timeseries q generate 'sum_Sales' as 'Forecasted_Sales' with (dateCols=('Date_Quarter',
'Date_Month', "Q-M"), partition='Region', ignoreLast=true);

Solutions: