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

• Question 131:

  A data scientist is asked to implement an article recommendation feature for an on-line magazine.

  The magazine does not want to use client tracking technologies such as cookies or reading history. Therefore, only the style and subject matter of the current article is available for making recommendations. All of the magazine's articles are stored in a database in a format suitable for analytics.

  Which method should the data scientist try first?

  A. K Means Clustering

  B. Naive Bayesian

  C. Logistic Regression

  D. Association Rules

  Correct Answer: A

  Explanation: kmeans uses an iterative algorithm that minimizes the sum of distances from each object to its cluster centroid, over all clusters. This algorithm moves objects between clusters until the sum cannot be decreased further. The result is a set of clusters that are as compact and well-separated as possible. You can control the details of the minimization using several optional input parameters to kmeans, including ones for the initial values of the cluster centroids, and for the maximum number of iterations. Clustering is primarily an exploratory technique to discover hidden structures of the data: possibly as a prelude to more focused analysis or decision processes. Some specific applications of k-means are image processing^ medical and customer segmentation. Clustering is often used as a lead-in to classification. Once the clusters are identified, labels can be applied to each cluster to classify each group based on its characteristics. Marketing and sales groups use k-means to better identify customers who have similar behaviors and spending patterns.

• Question 132:

  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. Linear regression

  D. Quantiles

  Correct Answer: C

  Explanation: Linear regression models a linear relationship of a scalar dependent variable y to one or more explanatory independent variables x to build a model of coefficients.

• Question 133:

  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. Integration

  B. Differentiation

  C. Transformation

  D. Generalization

  Correct Answer: A

  

  

• Question 134:

  Select the correct objectives of principal component analysis:

  A. To reduce the dimensionality of the data set

  B. To identify new meaningful underlying variables

  C. To discover the dimensionality of the data set

  D. Only 1 and 2

  E. All 1, 2 and 3

  Correct Answer: E

  Explanation: Principal component analysis (PCA) involves a mathematical procedure that transforms a number of (possibly) correlated variables into a (smaller) number of uncorrelated variables called principal components. The first principal component accounts for as much of the variability in the data as possible: and each succeeding component accounts for as much of the remaining variability as possible. Objectives of principal component analysis

  1.

  To discover or to reduce the dimensionality of the data set.

  2.

  To identify new meaningful underlying variables.

• Question 135:

  You are asked to create a model to predict the total number of monthly subscribers for a specific magazine. You are provided with 1 year's worth of subscription and payment data, user demographic data, and 10 years worth of content of the magazine (articles and pictures). Which algorithm is the most appropriate for building a predictive model for subscribers?

  A. Linear regression

  B. Logistic regression

  C. Decision trees

  D. TF-IDF

  Correct Answer: A

  : A data model explicitly describes a relationship between predictor and response variables. Linear regression fits a data model that is linear in the model coefficients. The most common type of linear regression is a least-squares fit, which can fit both lines and polynomials, among other linear models. Before you model the relationship between pairs of quantities, it is a good idea to perform correlation analysis to establish if a linear relationship exists between these quantities. Be aware that variables can have nonlinear relationships, which correlation analysis cannot detect. For more information, see Linear Correlation. If you need to fit data with a nonlinear model, transform the variables to make the relationship linear. Alternatively try to fit a nonlinear function directly using either the Statistics and Machine Learning Toolbox nlinfit function, the Optimization Toolbox Isqcurvefit function, or by applying functions in the Curve Fitting Toolbox.

• Question 136:

  You are creating a regression model with the input income, education and current debt of a customer, what could be the possible output from this model?

  A. Customer fit as a good

  B. Customer fit as acceptable or average category

  C. expressed as a percent, that the customer will default on a loan

  D. 1 and 3 are correct

  E. 2 and 3 are correct

  Correct Answer: C

  Explanation: Regression is the process of using several inputs to produce one or more outputs. For example The input might be the income, education and current debt of a customer The output might be the probability, expressed as a percent that the customer will default on a loan. Contrast this to classification where the output is not a number, but a class.

• Question 137:

  Refer to the exhibit.

  

  You are using K-means clustering to classify customer behavior for a large retailer. You need to determine the optimum number of customer groups. You plot the within-sum-of- squares (wss) data as shown in the exhibit. How many customer groups should you specify?

  A. 2

  B. 3

  C. 4

  D. 8

  Correct Answer: C

• Question 138:

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

  A. Effective in high dimensional spaces.

  B. it is memory efficient

  C. possible to specify custom kernels

  D. Effective in cases where number of dimensions is greater than the number of samples

  E. Number of features is much greater than the number of samples, the method still give good performances

  F. SVMs directly provide probability estimates

  Correct Answer: ABCD

  Explanation: Support vector machines (SVMs) are a set of supervised learning methods used for classification, regression and outliers detection.

  The advantages of support vector machines are:

  Effective in high dimensional spaces.

  Still effective in cases where number of dimensions is greater than the number of samples.

  Uses a subset of training points in the decision function (called support vectors), so it is also memory efficient.

  Versatile: different Kernel functions can be specified for the decision function. Common kernels are provided, but it is also possible to specify custom kernels. The disadvantages of support vector machines include:

  If the number of features is much greater than the number of samples, the method is likely to give poor performances.

  SVMs do not directly provide probability estimates, these are calculated using an expensive five-fold cross-validation.