TEST D-DS-FN-23 REGISTRATION & D-DS-FN-23 FREE LEARNING CRAM

Test D-DS-FN-23 Registration & D-DS-FN-23 Free Learning Cram

Test D-DS-FN-23 Registration & D-DS-FN-23 Free Learning Cram

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EMC Dell Data Science Foundations Sample Questions (Q173-Q178):

NEW QUESTION # 173
You are asked to create a model to predict the total number of monthly subscribers for a specific magazine. You are provided with 1 year's worth of subscription and payment data, user demographic data, and 10 years worth of content of the magazine (articles and pictures).
Which algorithm is the most appropriate for building a predictive model for subscribers?

  • A. TF-IDF
  • B. Logistic regression
  • C. Linear regression
  • D. Decision trees

Answer: C


NEW QUESTION # 174
When should you consider using multinomial logistic regression over binary logistic regression?

  • A. Dependent variable is continuous or categorical
  • B. Dependent variable is continuous or dichotomous
  • C. Dependent variable has more than two categories
  • D. Dependent variable is continuous only

Answer: C

Explanation:
Multinomial logistic regression should be used when the dependent variable has more than two categories (i.
e., it is categorical with more than two possible outcomes). This differs from binary logistic regression, which is used when the dependent variable is binary (i.e., has two categories).


NEW QUESTION # 175
Consider the following SQL statement:
SELECT employee_id, year, salary, avg(salary)
OVER
(PARTITION BY employee_id ORDER BY year ROWS BETWEEN 2 PRECEDING AND CURRENT ROW) as result_1 FROM employee ORDER BY employee_id, year For each employee_id, what is returned as result_1?

  • A. Three year rolling average salary
  • B. Average salary across all employee_id values
  • C. Four year rolling average salary
  • D. Average employee_id

Answer: A


NEW QUESTION # 176
Which R function plots a distribution of a single variable along two different axes?

  • A. density ()
  • B. summaryQ
  • C. table()
  • D. rug()

Answer: D

Explanation:
The rug() function in R adds small tick marks along the axes of a plot to represent the distribution of a variable, effectively showing it along both axes for better visual interpretation.


NEW QUESTION # 177
Refer to exhibit.

You are asked to write a report on how specific variables impact your client's sales using a data set provided to you by the client. The data includes 15 variables that the client views as directly related to sales, and you are restricted to these variables only.
After a preliminary analysis of the data, the following findings were made:
1. Multicollinearity is not an issue among the variables
2. Only three variables A, B, and C have significant correlation with sales You build a linear regression model on the dependent variable of sales with the independent variables of A, B, and C. The results of the regression are seen in the exhibit. You cannot request additional data.
What is a way that you could try to increase the R2 of the model without artificially inflating it?

  • A. Break variables A, B, and C into their own univariate models
  • B. Create interaction variables based only on variables A, B, and C
  • C. Create clusters based on the data and use them as model inputs
  • D. Force all 15 variables into the model as independent variables

Answer: C


NEW QUESTION # 178
......

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