Databricks Databricks Certification DATABRICKS-CERTIFIED-PROFESSIONAL-DATA-SCIENTIST Questions & Answers

• Question 41:

  Consider flipping a coin for which the probability of heads is p, where p is unknown, and our goa is to estimate p. The obvious approach is to count how many times the coin came up heads and divide by the total number of coin flips. If we flip the coin 1000 times and it comes up heads 367 times, it is very reasonable to estimate p as approximately 0.367. However, suppose we flip the coin only twice and we get heads both times. Is it reasonable to estimate p as 1.0? Intuitively, given that we only flipped the coin twice, it seems a bit rash to conclude that the coin will always come up heads, and____________is a way of avoiding such rash conclusions.

  A. Naive Bayes

  B. Laplace Smoothing

  C. Logistic Regression

  D. Linear Regression

  Correct Answer: B

  Explanation: Smooth the estimates: consider flipping a coin for which the probability of heads is p, where p is unknown, and our goal is to estimate p. The obvious approach is to count how many times the coin came up heads and divide by the total number of coin flips. If we flip the coin 1000 times and it comes up heads 367 times, it is very reasonable to estimate p as approximately 0.367. However, suppose we flip the coin only twice and we get heads both times. Is it reasonable to estimate p as 1.0? Intuitively, given that we only flipped the coin twice, it seems a bit rash to conclude that the coin will always come up heads, and smoothing is a way of avoiding such rash conclusions. A simple smoothing method, called Laplace smoothing (or Laplace's law of succession or add-one smoothing in RandN), is to estimate p by (one plus the number of heads) / (two plus the total number of flips). Said differently, if we are keeping count of the number of heads and the number of tails, this rule is equivalent to starting each of our counts at one, rather than zero. Another advantage of Laplace smoothing is that it avoids estimating any probabilities to be zero, even for events never observed in the data. Laplace add-one smoothing now assigns too much probability to unseen words

• Question 42:

  Suppose that we are interested in the factors that influence whether a political candidate wins an election. The outcome (response) variable is binary (0/1); win or lose. The predictor variables of interest are the amount of money spent on the campaign, the amount of time spent campaigning negatively and whether or not the candidate is an incumbent.

  Above is an example of:

  A. Linear Regression

  B. Logistic Regression

  C. Recommendation system

  D. Maximum likelihood estimation

  E. Hierarchical linear models

  Correct Answer: B

  Explanation: : Logistic regression Pros: Computationally inexpensive, easy to implement, knowledge representation easy to interpret Cons: Prone to underfitting, may have low accuracy Works with: Numeric values, nominal values

• Question 43:

  Let's say you have two cases as below for the movie ratings

  1.

  You recommend to a user a movie with four stars and he really doesn't like it and he'd rate it two stars

  2.

  You recommend a movie with three stars but the user loves it (he'd rate it five stars). So which statement correctly applies?

  A. In both cases, the contribution to the RMSE is the same

  B. In both cases, the contribution to the RMSE is the different

  C. In both cases, the contribution to the RMSE, could varies

  D. None of the above

  Correct Answer: A

• Question 44:

  Refer to Exhibit

  

  In the exhibit, the x-axis represents the derived probability of a borrower defaulting on a loan. Also in the exhibit, the pink represents borrowers that are known to have not defaulted on their loan, and the blue represents borrowers that are known to have defaulted on their loan. Which analytical method could produce the probabilities needed to build this exhibit?

  A. Linear Regression

  B. Logistic Regression

  C. Discriminant Analysis

  D. Association Rules

  Correct Answer: B

• Question 45:

  A. Naive Bayes classifier

  B. Collaborative filtering

  C. Logistic Regression

  D. Content-based filtering

  Correct Answer: B

  Explanation: One scenario of collaborative filtering application is to recommend interesting or popular information as judged by the community. As a typical example, stories appear in the front page of Digg as they are "voted up" (rated positively) by the community. As the community becomes larger and more diverse, the promoted stories can better reflect the average interest of the community members.

• Question 46:

  The method based on principal component analysis (PCA) evaluates the features according to:

  A. The projection of the largest eigenvector of the correlation matrix on the initial dimensions

  B. According to the magnitude of the components of the discriminate vector

  C. The projection of the smallest eigenvector of the correlation matrix on the initial dimensions

  D. None of the above

  Correct Answer: A

  Explanation: Feature Selection:

  The method based on principal component analysis (PCA) evaluates the features according to the projection of the largest eigenvector of the correlation matrix on the initial dimensions, the method based on Fisher's linear discriminate

  analysis evaluates. Them according to the magnitude of the components of the discriminate vector.

• Question 47:

  You are working on a email spam filtering assignment, while working on this you find there is new word e.g. HadoopExam comes in email, and in your solutions you never come across this word before, hence probability of this words is coming in either email could be zero. So which of the following algorithm can help you to avoid zero probability?

  A. Naive Bayes

  B. Laplace Smoothing

  C. Logistic Regression

  D. All of the above

  Correct Answer: B

  Explanation: Laplace smoothing is a technique for parameter estimation which accounts for unobserved events. It is more robust and will not fail completely when data that has never been observed in training shows up.

• Question 48:

  You are designing a recommendation engine for a website where the ability to generate more personalized recommendations by analyzing information from the past activity of a specific user, or the history of other users deemed to be of

  similar taste to a given user. These resources are used as user profiling and helps the site recommend content on a user-by-user basis. The more a given user makes use of the system, the better the recommendations become, as the

  system gains data to improve its model of that user.

  What kind of this recommendation engine is ?

  A. Naive Bayes classifier

  B. Collaborative filtering

  C. Logistic Regression

  D. Content-based filtering

  Correct Answer: B

  Another aspect of collaborative filtering systems is the ability to generate more personalized recommendations by analyzing information from the past activity of a specific user, or the history of other users deemed to be of similar taste to a given user. These resources are used as user profiling and help the site recommend content on a user-by- user basis. The more a given user makes use of the system, the better the recommendations become, as the system gains data to improve its model of that user

• Question 49:

  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. Now you have find the most important parameters which can best describe a website, so which of the following technique you will use:

  A. PCA (Principal component analysis)

  B. Linear Regression

  C. Logistic Regression

  D. Clustering

  Correct Answer: A

  Explanation: Principal component analysis . or PCA, is a technique for taking a dataset that is in the form of a set of tuples representing points in a high-dimensional space and finding the dimensions along which the tuples line up best. The idea is to treat the set of tuples as a matrix M and find the eigenvectors for MMT or M T M . The matrix of these eigenvectors can be thought of as a rigid rotation in a high-dimensional space. When you apply this transformation to the original data, the axis corresponding to the principal eigenvector is the one along which the points are most "spread out,11 More precisely this axis is the one along which the variance of the data is maximized. Put another way, the points can best be viewed as lying along this axis, with small deviations from this axis.

• Question 50:

  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 summing 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 integrating(ln case of continuous variable) the joint probability distribution over Y.

  Correct Answer: ABCD

  Explanation: Given two random variables X and Y whose joint distribution is 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. This is typically calculated by summing or integrating the joint probability distribution over Y. ' For discrete random variables, the marginal probability mass function can be written as Pr(X = x). This is

  

  Text

  Description automatically generated with low confidence where Pr(X = x,Y = y) is the joint distribution of X and Y, while Pr(X = x|Y = y) is the conditional distribution of X given Y In this case, the variable Y has been marginalized out. Bivariate marginal and joint probabilities for discrete random variables are often displayed as two-way tables. Similarly for continuous random variables, the marginal probability density function can be written as pX(x). This is

  

  Diagram Description automatically generated with medium confidence where pX.Y(x.y) gives the joint distribution of X and Y while pX|Y(x|y) gives the conditional distribution for X given Y Again: the variable Y has been marginalized out.

  Note that a marginal probability can always be written as an expected value:

  

  Text, letter Intuitively, the marginal probability of X is computed by examining the conditional probability of X given a particular value of Y, and then averaging this conditional probability over the distribution of all values of Y This follows from the definition of expected value, i.e. in general

  

  A picture containing diagram