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Free DATABRICKS-CERTIFIED-PROFESSIONAL-DATA-SCIENTIST Exam Braindumps
Databricks Certified Professional Data Scientist Exam Questions 56–60 (page: 13)

Updated On: 9-Feb-2026
Page 13 of 29
PDF with 138 Questions

A bio-scientist is working on the analysis of the cancer cells. To identify whether the cell is cancerous or not, there has been hundreds of tests are done with small variations to say yes to the problem. Given the test result for a sample of healthy and cancerous cells, which of the following technique you will use to determine whether a cell is healthy?

  1. Linear regression
  2. Collaborative filtering
  3. Naive Bayes
  4. Identification Test

Answer(s): C

Explanation:

In this problem you have been given high-dimensional independent variables like yes, no: test results etc. and you have to predict either valid or not valid (One of two). So all of the below technique can be applied to this problem.
Support vector machines Naive Bayes Logistic regression Random decision forests



Find out the classifier which assumes independence among all its features?

  1. Neural networks
  2. Linear Regression
  3. Naive Bayes
  4. Random forests

Answer(s): C

Explanation:

A Bayes classifier is a simple probabilistic classifier based on applying Bayes' theorem (from Bayesian statistics) with strong (naive) independence assumptions. A more descriptive term for the underlying probability model would be "independent feature model". A Bayes classifier is a simple probabilistic classifier based on applying Bayes' theorem (from Bayesian statistics) with strong (naive) independence assumptions. A more descriptive term for the underlying probability model would be "independent feature model". In simple terms, a naive Bayes classifier assumes that the presence (or absence) of a particular feature of a class is unrelated to the presence (or absence) of any other feature. For example, a fruit may be considered to be an apple if it is red, round, and about 4" in diameter Even if these features depend on each other or upon the existence of the other features, a naive Bayes classifier considers all of these properties to independently contribute to the probability that this fruit is an apple.



Digit recognition, is an example of.....

  1. Classification
  2. Clustering
  3. Unsupervised learning
  4. None of the above

Answer(s): A

Explanation:

Supervised learning is fairly common in classification problems because the goal is often to get the computer to learn a classification system that we have created. Digit recognition: once again, is a common example of classification learning. More generally, classification learning is appropriate for any problem where deducing a classification is useful and the classification is easy to determine. In some cases, it might not even be necessary to give pre-determined classifications to every instance of a problem if the agent can work out the classifications for itself. This would be an example of unsupervised learning in a classification context.



You are using one approach for the classification where to teach the agent not by giving explicit categorizations, but by using some sort of reward system to indicate success, where agents might be rewarded for doing certain actions and punished for doing others.
Which kind of this learning

  1. Supervised
  2. Unsupervised
  3. Regression
  4. None of the above

Answer(s): B

Explanation:

Unsupervised learning seems much harder: the goal is to have the computer learn how to do something that we don't tell it how to do! The approach is to teach the agent not by giving explicit categorizations, but by using some sort of reward system to indicate success. Note that this type of training will generally fit into the decision problem framework because the goal is not to produce a classification but to make decisions that maximize rewards. This approach nicely generalizes to the real world, where agents might be rewarded for doing certain actions and punished fordoing others.



Select the correct statement which applies to Principal component analysis (PCA)

  1. Is a mathematical procedure that transforms a number of (possibly) correlated variables into a (smaller) number of uncorrelated variables.
  2. Is a mathematical procedure that transforms a number of (possibly) correlated variables into a (higher) number of uncorrelated variables
  3. Increase the dimensionality of the data set.
  4. 1 and 3 are correct
  5. 1 and 2 are correct

Answer(s): A

Explanation:

Principal component analysis (PCA) involves a mathematical procedure that transforms a number of (possibly) correlated variables into a (smaller) number of uncorrected 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.



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