Databricks-Certified-Professional-Data-Scientist試験無料問題集（140題）「Databricks Certified Professional Data Scientist 認定」

出題：1

You are working on a problem where you have to predict whether the claim is done valid or not. And you find that most of the claims which are having spelling errors as well as corrections in the manually filled claim forms compare to the honest claims. Which of the following technique is suitable to find out whether the claim is valid or not?

A. Logistic Regression

B. Any one of the above

C. Random Decision Forests

D. Naive Bayes

正解：B 解答を投票する

解説: (GoShiken メンバーにのみ表示されます)

出題：2

The figure below shows a plot of the data of a data matrix M that is 1000 x 2. Which line represents the first principal component?

A. yellow

B. blue

C. Neither

正解：B 解答を投票する

解説: (GoShiken メンバーにのみ表示されます)

出題：3

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

A. Expected value

B. Linear regression

C. Variance

D. Quantiles

正解：B 解答を投票する

解説: (GoShiken メンバーにのみ表示されます)

出題：4

Which is an example of supervised learning?

A. SVM

B. EM

C. SVD

D. PCA

E. k-means clustering

正解：A 解答を投票する

解説: (GoShiken メンバーにのみ表示されます)

出題：5

You are working in an ecommerce organization, where you are designing and evaluating a recommender system, you need to select which of the following metric wilt always have the largest value?

A. Both land 2

B. Information is not good enough.

C. Sum of Errors

D. Root Mean Square Error

E. Mean Absolute Error

正解：B 解答を投票する

出題：6

Select the correct objectives of principal component analysis

A. All 1, 2 and 3

B. To reduce the dimensionality of the data set

C. To identify new meaningful underlying variables

D. To discover the dimensionality of the data set

E. Only 1 and 2

正解：A 解答を投票する

解説: (GoShiken メンバーにのみ表示されます)

出題：7

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 requires the lesser memory to store the coefficients for the model

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

C. Noisy features are removed

D. It creates the smaller models

正解：A 解答を投票する

解説: (GoShiken メンバーにのみ表示されます)

出題：8

A denote the event 'student is female' and let B denote the event 'student is French'. In a class of 100 students suppose 60 are French, and suppose that 10 of the French students are females. Find the probability that if I pick a French student, it will be a girl, that is, find P(A|B).

A. 1/6

B. 2/6

C. 1/3

D. 2/3

正解：A 解答を投票する

解説: (GoShiken メンバーにのみ表示されます)

出題：9

You have modeled the datasets with 5 independent variables called A,B,C,D and E having relationships which is not dependent each other, and also the variable A,B and C are continuous and variable D and E are discrete (mixed mode).
Now you have to compute the expected value of the variable let say A, then which of the following computation you will prefer

A. Generalization

B. Differentiation

C. Transformation

D. Integration

正解：D 解答を投票する

解説: (GoShiken メンバーにのみ表示されます)

出題：10

A researcher is interested in how variables, such as GRE (Graduate Record Exam scores), GPA (grade point average) and prestige of the undergraduate institution, effect admission into graduate school. The response variable, admit/don't admit, is a binary variable.
Above is an example of

A. Logistic Regression

B. Hierarchical linear models

C. Linear Regression

D. Recommendation system

E. Maximum likelihood estimation

正解：A 解答を投票する

解説: (GoShiken メンバーにのみ表示されます)

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