Free Databricks-Certified-Professional-Data-Scientist – Databricks Certified Professional Data Scientist Exam Practice Test Question

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  Labels are not pre-assigned to each objects in the cluster.

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  Labels are pre-assigned to each objects in the cluster.

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  It classify the data based on the labels.

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  It discovers the center of each cluster.

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  It find each objects fall in which particular cluster
  
  
  

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  Requires the less memory

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  Less pass through the training data

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  Easily reverse engineer vectors to determine which original feature mapped to a vector location
  
  
  

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  The marginal probability P(H=Hit) is the sum along the H=Hit row of this joint distribution table, as this is the probability of being hit when the lights are red OR yellow OR green.

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  marginal probability that P(H=Not Hit) is the sum of the H=Not Hit row

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  marginal probability that P(H=Not Hit) is the sum of the H= Hit row
  
  
  

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  No Assumption about the data

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  Computationally expensive

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  Require less memory

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  Works with Numeric Values
  
  
  

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  This is typically calculated by summing the joint probability distribution over Y.

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  This is typically calculated by integrating the joint probability distribution over Y

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  This is typically calculated by summing (In case of discrete variable) the joint probability distribution over Y

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  This is typically calculated by integrating(ln case of continuous variable) the joint probability distribution over Y.
  
  
  

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  '
  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
  
  Description automatically generated
  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
  
  Description automatically generated
  
  
  
  
  
  

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  L1 regularization has more accuracy of the resulting model

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  Size of the model can be much smaller in L1 regularization than that produced by L2regularization

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  L2-regularization can be of vital importance when the application is deployed in resource-tight environments such as cell-phones.

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  All of the above are correct
  
  
  

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  Customer fit as a good

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  Customer fit as acceptable or average category

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

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  1 and 3 are correct

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  2 and 3 are correct
  
  
  

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  Numerical Value

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  Categorical values

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  For booth Numerical and categorical values
  
  
  

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  Naive Bayesian classifier

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  Decision tree

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  Linear regression

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  K-means clustering
  
  
  

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  Bays Theorem

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  Poisson Distribution

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  Binomial Distribution

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  None of the above