DATABRICKS-CERTIFIED-PROFESSIONAL-DATA-SCIENTIST Exam Questions

Refer to the exhibit. You are building a decision tree. In this exhibit, four variables are listed with their respective values of info-gain. Based on this information, on which at...

You have collected the 100's of parameters about the 1000's of websites e.g. daily hits, average time on the websites, number of unique visitors, number of returning visitors etc....

Which of the below best describe the Principal component analysis

You are working in a data analytics company as a data scientist, you have been given a set of various types of Pizzas available across various premium food centers in a country. Th...

Feature Hashing approach is "SGD-based classifiers avoid the need to predetermine vector size by simply picking a reasonable size and shoehorning the training data into vectors of...

What are the advantages of the Hashing Features?

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...

Suppose A, B , and C are events. The probability of A given B , relative to P(|C), is the same as the probability of A given B and C (relative to P ). That is,

What is the considerable difference between L1 and L2 regularization?

Regularization is a very important technique in machine learning to prevent overfitting. Mathematically speaking, it adds a regularization term in order to prevent the coefficients...

Select the correct option which applies to L2 regularization

Regularization is a very important technique in machine learning to prevent over fitting. And Optimizing with a L1 regularization term is harder than with an L2 regularization term...

Logistic regression is a model used for prediction of the probability of occurrence of an event. It makes use of several variables that may be......

Spam filtering of the emails is an example of

Select the choice where Regression algorithms are not best fit

Question-13. Which of the following is not the Classification algorithm?

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 lar...

Under which circumstance do you need to implement N-fold cross-validation after creating a regression model?

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 me...

You are analyzing data in order to build a classifier model. You discover non-linear data and discontinuities that will affect the model. Which analytical method would you recommen...

Your customer provided you with 2. 000 unlabeled records three groups. What is the correct analytical method to use?

What describes a true limitation of Logistic Regression method?

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

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?

Question-18. What is the best way to ensure that the k-means algorithm will find a good clustering of a collection of vectors?

A website is opened 3 times by a user. What is the probability of he clicks 2 times the advertisement, is best calculated by

Suppose a man told you he had a nice conversation with someone on the train. Not knowing anything about this conversation, the probability that he was speaking to a woman is 50% (a...

Which of the following could be features?

Refer to image below

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...

Select the correct statement regarding the naive Bayes classification

In which of the following scenario we can use Naive Bayes theorem for classification

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

Support vector machines (SVMs) are a set of supervised learning methods used for

Select the correct problems which can be solved using SVMs

Which is an example of supervised learning?

Which of the following are point estimation methods?

In statistics, maximum-likelihood estimation (MLE) is a method of estimating the parameters of a statistical model. When applied to a data set and given a statistical model, maximu...