Databricks-Certified-Professional-Data-Scientist問題集、Databricks実際の試験問題

質問 1

What are the advantages of the mutual information over the Pearson correlation for text classification problems?

A. The mutual information is easier to parallelize.

B. The mutual information has a meaningful test for statistical significance.

C. The mutual information can signal non-linear relationships between the dependent and independent variables.

D. The mutual information doesn't assume that the variables are normally distributed.

正解: A

解説: (PassTest メンバーにのみ表示されます)

質問 2

You are building a classifier off of a very high-dimensiona data set similar to shown in the image with 5000 variables (lots of columns, not that many rows). It can handle both dense and sparse input. Which technique is most suitable, and why?

A. Logistic regression with L1 regularization, to prevent overfitting

B. k-nearest neighbors, because it uses local neighborhoods to classify examples

C. Random forest because it is an ensemble method

D. Naive Bayes, because Bayesian methods act as regularlizers

正解: A

解説: (PassTest メンバーにのみ表示されます)

質問 3

A fruit may be considered to be an apple if it is red, round, and about 3" in diameter. A naive Bayes classifier considers each of these features to contribute independently to the probability that this fruit is an apple, regardless of the

A. Presence of the other features.

B. Presence or absence of the other features

C. None of the above

D. Absence of the other features.

正解: B

解説: (PassTest メンバーにのみ表示されます)

質問 4

Suppose you have been given two Random Variables X and Y, whose joint distribution is already known, the marginal distribution of X is simply the probability distribution of X averaging over information about Y.
It is the probability distribution of X when the value of Y is not known. So how do you calculate the marginal distribution of X

A. This is typically calculated by integrating(ln case of continuous variable) the joint probability distribution over Y.

B. This is typically calculated by integrating the joint probability distribution over Y

C. This is typically calculated by summing (In case of discrete variable) the joint probability distribution over Y

D. This is typically calculated by summing the joint probability distribution over Y.

正解: A,B,C,D

解説: (PassTest メンバーにのみ表示されます)

質問 5

Select the statement which applies correctly to the Naive Bayes

A. Sensitive to how the input data is prepared

B. Works with nominal values

C. Works with a small amount of data

正解: A,B,C

質問 6

What describes a true limitation of Logistic Regression method?

A. It does not handle correlated variables well.

B. It does not handle redundant variables well.

C. It does not handle missing values well.

D. It does not have explanatory values.

正解: C

質問 7

You are studying the behavior of a population, and you are provided with multidimensional data at the individual level. You have identified four specific individuals who are valuable to your study, and would like to find all users who are most similar to each individual. Which algorithm is the most appropriate for this study?

A. Linear regression

B. Decision trees

C. K-means clustering

D. Association rules

正解: C

解説: (PassTest メンバーにのみ表示されます)

質問 8

What is one modeling or descriptive statistical function in MADlib that is typically not provided in a standard relational database?

A. Expected value

B. Variance

C. Quantiles

D. Linear regression

正解: D

解説: (PassTest メンバーにのみ表示されます)

質問 9

Select the correct statement which applies to Principal component analysis (PCA)

A. Is a mathematical procedure that transforms a number of (possibly) correlated variables into a (higher) number of uncorrelated variables

B. Is a mathematical procedure that transforms a number of (possibly) correlated variables into a (smaller) number of uncorrelated variables.

C. Increase the dimensionality of the data set.

D. 1 and 2 are correct

E. 1 and 3 are correct

正解: B

解説: (PassTest メンバーにのみ表示されます)

質問 10

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 clusters based on the data and use them as model inputs

C. Create interaction variables based only on variables A, B, and C

D. Force all 15 variables into the model as independent variables

正解: B

解説: (PassTest メンバーにのみ表示されます)

Databricks Certified Professional Data Scientist - Databricks-Certified-Professional-Data-Scientist 模擬練習