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Databricks DATABRICKS-CERTIFIED-PROFESSIONAL-DATA-SCIENTIST Exam Prep
Databricks Certified Professional Data Scientist Exam (Page 11 )

Updated On: 20-Aug-2026
Page 5 of 29
PDF with 138 Questions

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

  1. Presence of the other features.
  2. Absence of the other features.
  3. Presence or absence of the other features
  4. None of the above

Answer(s): C

Explanation:

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.



Select the correct statement regarding the naive Bayes classification

  1. it only requires a small amount of training data to estimate the parameters
  2. Independent variables can be assumed
  3. only the variances of the variables for each class need to be determined
  4. for each class entire covariance matrix need to be determined

Answer(s): A,B,C

Explanation:

An advantage of naive Bayes is that it only requires a small amount of training data to estimate the parameters (means and variances of the variables) necessary for classification. Because independent variables are assumed, only the variances of the variables for each class need to be determined and not the entire covariance matrix.



In which of the following scenario we can use naTve Bayes theorem for classification

  1. Classify whether a given person is a male or a female based on the measured features. The features include height, weight and foot size.
  2. To classify whether an email is spam or not spam
  3. To identify whether a fruit is an orange or not based on features like diameter, color and shape

Answer(s): A,B,C

Explanation:

naive Bayes classifiers have worked quite well in many real-world situations, famously document classification and spam filtering. They requires a small amount of training data to estimate the necessary parameters



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

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

Answer(s): A,B,C,D

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.



Support vector machines (SVMs) are a set of supervised learning methods used for

  1. Linear classification
  2. Non-linear classification
  3. Regression

Answer(s): A,B,C

Explanation:

In machine learning, support vector machines (SVMs). also support vector networks[1]) are supervised learning models with associated learning algorithms that analyze data and recognize patterns^ used for classification and regression analysis. In addition to performing linear classification, SVMs can efficiently perform a non-linear classification using what is called the kernel tricky implicitly mapping their inputs into high-dimensional feature spaces.



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