Databricks Databricks-Certified-Professional-Data-Scientist Databricks Certified Professional Data Scientist Exam Exam Practice Test Questions

• Question 1

  In which phase of the analytic lifecycle would you expect to spend most of the project time?

  • Discovery
    
  • Data preparation
    
  • Communicate Results
    
  • Operationalize
    In the data preparation phase of the Data Analytics Lifecycle, the data range and distribution can be obtained. If the data is skewed, viewing the logarithm of the data (if it's all positive) can help detect structures that might otherwise be overlooked in a graph with a regular, nonlogarithmic scale.
    When preparing the data, one should look for signs of dirty data, as explained in the
    previous section. Examining if the data is unimodal or multimodal will give an idea of how many distinct populations with different behavior patterns might be mixed into the overall population. Many modeling techniques assume that the data follows a
    normal distribution. Therefore, it is important to know if the available dataset can match that assumption before applying any of those modeling techniques.
    

  Show Answer Answer: 2 Next

• Question 2

  Which technique you would be using to solve the below problem statement? 'What is the probability that individual customer will not repay the loan amount?'

  • Classification
    
  • Clustering
    
  • Linear Regression
    
  • Logistic Regression
    
  • Hypothesis testing
    

  Show Answer Answer: 4 Next

• Question 3

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

  • Effective in high dimensional spaces.
    
  • it is memory efficient
    
  • possible to specify custom kernels
    
  • Effective in cases where number of dimensions is greater than the number of samples
    
  • Number of features is much greater than the number of samples, the method still give good performances
    
  • SVMs directly provide probability estimates
    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.
    

  Show Answer Answer: 1,,2,,3,,4 Next

• Question 4

  What type of output generated in case of linear regression?

  • Continuous variable
    
  • Discrete Variable
    
  • Any of the Continuous and Discrete variable
    
  • Values between 0 and 1
    Linear regression model generate continuous output variable.
    

  Show Answer Answer: 1 Next

• Question 5

  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 of the other features.
    
  • Absence of the other features.
    
  • Presence or absence of the other features
    
  • None of the above
    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.
    

  Show Answer Answer: 3 Next