Free DATABRICKS-CERTIFIED-PROFESSIONAL-DATA-SCIENTIST Exam Braindumps
Databricks Certified Professional Data Scientist Exam Questions 51–55 (page: 12)

Updated On: 9-Feb-2026

Scenario: Suppose that Bob can decide to go to work by one of three modes of transportation, car, bus, or commuter train. Because of high traffic, if he decides to go by car. there is a 50% chance he will be late. If he goes by bus, which has special reserved lanes but is sometimes overcrowded, the probability of being late is only 20%. The commuter train is almost never late, with a probability of only 1 %, but is more expensive than the bus.
Suppose that Bob is late one day, and his boss wishes to estimate the probability that he drove to work that day by car. Since he does not know Which mode of transportation Bob usually uses, he gives a prior probability of 1 3 to each of the three possibilities.
Which of the following method the boss will use to estimate of the probability that Bob drove to work?

  1. Naive Bayes
  2. Linear regression
  3. Random decision forests
  4. None of the above

Answer(s): A

Explanation:

Bayes' theorem (also known as Bayes' rule) is a useful tool for calculating conditional probabilities.



In which of the following scenario you should apply the Bay's Theorem

  1. The sample space is partitioned into a set of mutually exclusive events {A1, A2, . .., An }.
  2. Within the sample space, there exists an event B, for which P(B) > 0.
  3. The analytical goal is to compute a conditional probability of the form: P(Ak | B ).
  4. In all above cases

Answer(s): D



Marie is getting married tomorrow, at an outdoor ceremony in the desert. In recent years, it has rained only 5 days each year. Unfortunately, the weatherman has predicted rain for tomorrow.
When it actually rains, the weatherman correctly forecasts rain 90% of the time.
When it doesn't rain, he incorrectly forecasts rain 10% of the time.
Which of the following will you use to calculate the probability whether it will rain on the day of Marie's wedding?

  1. Naive Bayes
  2. Logistic Regression
  3. Random Decision Forests
  4. All of the above

Answer(s): A

Explanation:

The sample space is defined by two mutually-exclusive events - it rains or it does not rain. Additionally, a third event occurs when the weatherman predicts rain. You should consider Bayes' theorem when the following conditions exist.
· The sample space is partitioned into a set of mutually exclusive events {A1, A2,... :An}. · Within the sample space, there exists an event B: for which P(B) > 0. · The analytical goal is to compute a conditional probability of the form: P( Ak B).



Your company has organized an online campaign for feedback on product quality and you have all the responses for the product reviews, in the response form people have check box as well as text field. Now you know that people who do not fill in or write non-dictionary word in the text field are not considered valid feedback. People who fill in text field with proper English words are considered valid response.
Which of the following method you should not use to identify whether the response is valid or not?

  1. Naive Bayes
  2. Logistic Regression
  3. Random Decision Forests
  4. Any one of the above

Answer(s): D

Explanation:

In this problem you have been given high-dimensional independent variables like yeS; nO; no English words , 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



Suppose you have been given a relatively high-dimension set of independent variables and you are asked to come up with a model that predicts one of Two possible outcomes like "YES" or "NO", then which of the following technique best fit.

  1. Support vector machines
  2. Naive Bayes
  3. Logistic regression
  4. Random decision forests
  5. All of the above

Answer(s): E

Explanation:

In this problem you have been given high-dimensional independent variables like yeS; nO; no English words , 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



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