Databricks DATABRICKS-CERTIFIED-PROFESSIONAL-DATA-SCIENTIST Online Practice Questions and Exam Preparation

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

• Question 1:

  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
  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

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  explanation:

  Explanation/Reference:

  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.

• Question 2:

  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
  C. 4

• Question 3:

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

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  explanation:

  Explanation/Reference:

  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 4:

  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
  A. Linear regression

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  explanation:

  Explanation/Reference:

  : 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 5:

  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
  E. All 1, 2 and 3

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  explanation:

  Explanation/Reference:

  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 6:

  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
  A. Integration

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  explanation:

  Explanation/Reference:

  

  

• Question 7:

  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
  C. Linear regression

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  explanation:

  Explanation/Reference:

  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 8:

  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
  A. K Means Clustering

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  explanation:

  Explanation/Reference:

  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 9:

  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 probability that this fruit is an apple, regardless of the:

  A. Presence of the other features.
  B. Absence of the other features.
  C. Presence or absence of the other features
  D. None of the above
  C. Presence or absence of the other features

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  explanation:

  Explanation/Reference:

  In simple terms, a naive Bayes classifier assumes that the value of a particular feature is unrelated to the presence or absence of any other feature, given the class variable. For example, 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 probability that this fruit is an apple, regardless of the presence or absence of the other features.

• Question 10:

  Spam filtering of the emails is an example of

  A. Supervised learning
  B. Unsupervised learning
  C. Clustering
  D. 1 and 3 are correct
  E. 2 and 3 are correct
  A. Supervised learning

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  explanation:

  Explanation/Reference:

  Clustering is an example of unsupervised learning. The clustering algorithm finds groups within the data without being told what to look for upfront. This contrasts with classification, an example of supervised machine learning, which is the process of determining to which class an observation belongs. A common application of classification is spam filtering. With spam filtering we use labeled data to train the classifier: e-mails marked as spam or ham.