CRM-ANALYTICS-AND-EINSTEIN-DISCOVERY-CONSULTANT STUDY GUIDE, LATEST CRM-ANALYTICS-AND-EINSTEIN-DISCOVERY-CONSULTANT TEST SAMPLE

CRM-Analytics-and-Einstein-Discovery-Consultant Study Guide, Latest CRM-Analytics-and-Einstein-Discovery-Consultant Test Sample

CRM-Analytics-and-Einstein-Discovery-Consultant Study Guide, Latest CRM-Analytics-and-Einstein-Discovery-Consultant Test Sample

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Salesforce Certified CRM Analytics and Einstein Discovery Consultant Sample Questions (Q35-Q40):

NEW QUESTION # 35
Universal Containers (UC) is looking to create a dashboard for whitespace analysis. UC wants to view a particularcustomer and see what similarcustomers have bought.
Which recipe transformation is helpful for the consultant to use while creating the dataset?

  • A. Timeseries Forecasting
  • B. Cluster
  • C. Predict Missing Values

Answer: B


NEW QUESTION # 36
An insurance company has many Einstein Analytics dashboards that show the influence of weather, such as atmospheric temperature, on customer cases. A service agent commented that it is sometimes difficult to determine, by looking at a dashboard, whether the temperature data is reported in degrees Fahrenheit or in degrees Celsius.
How can a dashboard designer ensure the temperature data is easier to interpret on the dashboards?

  • A. Use the XMD node in the dataflow to customize the number format of the field.
  • B. Customize the number format in the number widget properties of the dashboard.
  • C. Add a text widget to the dashboard to indicate Fahrenheit or Celsius.
  • D. In Explorer, go to the Fields menu and then customize the number format of the measure.

Answer: C


NEW QUESTION # 37
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 outcome variable may be causing data leakage.
  • B. The dataset contains multiple dominant values.
  • C. The model contains too many outlier values.

Answer: A


NEW QUESTION # 38
A CRM Analytics consultant has prepared a CSV file to be uploaded to CRM Analytics. By mistake, one of the column headers is modified as random non-alphanumeric characters "*&**(&*(%", which went unnoticed prior to uploading the file.
What is the expected behavior of the uploaded CSV column?

  • A. The column header Is set to *&**(&*(%.
  • B. The column header is auto-updated to "Column" + column number.
  • C. The columnheaderis prefixed with "X" upon upload.

Answer: C

Explanation:
When uploading CSV files into CRM Analytics, column headers must follow certain formatting rules.
Headers containing non-alphanumeric characters, such as "&**(&(%", will automatically be adjusted.
Specifically, if the column header starts with non-alphanumeric characters or contains such characters, CRM Analytics will prefix the header with "X" to ensure compatibility with internal naming conventions. This behavior ensures that the column can be referenced in the platform without causing errors or conflicts.


NEW QUESTION # 39
Cloud Kicks (CK) has a dashboard in CRM Analytics with forecasting dat
a. One widget is a compare table using the timeseries function showing the quarterly forecast. However, CK is interested in enhancing the dashboard with a weekly forecast per customer.
How should CK achieve this?

  • A. An SAQL query with a timeseries statement specifying Opportunity Name as the partition and Y-M-W as the Date Cols
  • B. An SAQL query with a timeseries statement specifying Account Name as the partition and Y-M-W as the Date Cols
  • C. An SAQL query with @ timeseries statement specifying Account Name as the partition and Y-M-D as the Date Cols

Answer: B

Explanation:
To create a weekly forecast per customer, an SAQL (Salesforce Analytics Query Language) query can be used. The timeseries function generates forecast data based on a specified date range. In this case, the forecast is customer-specific, meaning the partition should be based on Account Name to ensure that the forecast is generated for each customer. The date column (Date Cols) should be set to Y-M-W to aggregate the forecast data on a weekly basis.


NEW QUESTION # 40
......

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