Databricks-Certified-Professional-Data-Scientist無料問題集「Databricks Certified Professional Data Scientist」

質問 1

Question-3: In machine learning, feature hashing, also known as the hashing trick (by analogy to the kernel trick), is a fast and space-efficient way of vectorizing features (such as the words in a language), i.e., turning arbitrary features into indices in a vector or matrix. It works by applying a hash function to the features and using their hash values modulo the number of features as indices directly, rather than looking the indices up in an associative array. So what is the primary reason of the hashing trick for building classifiers?

（A）It creates the smaller models

（B）It reduces the non-significant features e.g. punctuations

（C）It requires the lesser memory to store the coefficients for the model

（D）Noisy features are removed

正解：C 解答を投票する

解説: (JPNTest メンバーにのみ表示されます)

質問 2

What describes a true property of Logistic Regression method?

（A）It is robust with redundant variables and correlated variables.

（B）It handles missing values well.

（C）It works well with variables that affect the outcome in a discontinuous way.

（D）It works well with discrete variables that have many distinct values.

正解：A 解答を投票する

質問 3

Select the choice where Regression algorithms are not best fit

（A）When the dimension of the object given

（B）Temperature in the atmosphere

（C）Employee status

（D）Weight of the person is given

正解：C 解答を投票する

解説: (JPNTest メンバーにのみ表示されます)

質問 4

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

（A）1 and 2 are correct

（B）1 and 3 are correct

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

（D）Increase the dimensionality of the data set.

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

正解：E 解答を投票する

解説: (JPNTest メンバーにのみ表示されます)

質問 5

In which phase of the analytic lifecycle would you expect to spend most of the project time?

（A）Operationalize

（B）Discovery

（C）Communicate Results

（D）Data preparation

正解：D 解答を投票する

解説: (JPNTest メンバーにのみ表示されます)

質問 6

Which of the following question statement falls under data science category?

（A）How many products have been sold in a last month?

（B）Where is a problem for sales?

（C）What happened in last six months?

（D）What happens, if these scenario continues?

（E）Which is the optimal scenario for selling this product?

正解：D、E 解答を投票する

解説: (JPNTest メンバーにのみ表示されます)

質問 7

Which of the following are advantages of the Support Vector machines?

（A）Effective in cases where number of dimensions is greater than the number of samples

（B）Number of features is much greater than the number of samples, the method still give good performances

（C）possible to specify custom kernels

（D）Effective in high dimensional spaces.

（E）it is memory efficient

（F）SVMs directly provide probability estimates

正解：A、C、D、E 解答を投票する

解説: (JPNTest メンバーにのみ表示されます)

質問 8

In which phase of the data analytics lifecycle do Data Scientists spend the most time in a project?

（A）Discovery

（B）Data Preparation

（C）Communicate Results

（D）Model Building

正解：B 解答を投票する

質問 9

A data scientist wants to predict the probability of death from heart disease based on three risk factors: age, gender, and blood cholesterol level. What is the most appropriate method for this project?

（A）K-means clustering

（B）Apriori algorithm

（C）Linear regression

（D）Logistic regression

正解：D 解答を投票する

解説: (JPNTest メンバーにのみ表示されます)

質問 10

Which method is used to solve for coefficients bO, b1, ... bn in your linear regression model:

（A）Apriori Algorithm

（B）Ridge and Lasso

（C）Ordinary Least squares

（D）Integer programming

正解：C 解答を投票する

解説: (JPNTest メンバーにのみ表示されます)

Databricks-Certified-Professional-Data-Scientist 無料問題集「Databricks Certified Professional Data Scientist」

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