Free DATABRICKS-CERTIFIED-PROFESSIONAL-DATA-SCIENTIST Exam Braindumps
Databricks Certified Professional Data Scientist Exam Questions 26–30 (page: 7)

Updated On: 9-Feb-2026

Suppose that the probability that a pedestrian will be tul by a car while crossing the toad at a pedestrian crossing without paying attention to the traffic light is lo be computed. Let H be a discrete random variable taking one value from (Hit. Not Hit). Let L be a discrete random variable taking one value from (Red. Yellow. Green).
Realistically, H will be dependent on L That is, P(H = Hit) and P(H = Not Hit) will take different values depending on whether L is red, yellow or green. A person is. for example, far more likely to be hit by a car when trying to cross while Hie lights for cross traffic are green than if they are red In other words, for any given possible pair of values for Hand L. one must consider the joint probability distribution of H and L to find the probability* of that pair of events occurring together if Hie pedestrian ignores the state of the light.

Here is a table showing the conditional probabilities of being bit. defending on ibe stale of the lights (Note that the columns in this table must add up to 1 because the probability of being hit oi not hit is 1 regardless of the stale of the light.)

  1. 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.
  2. marginal probability that P(H=Not Hit) is the sum of the H=Not Hit row
  3. marginal probability that P(H=Not Hit) is the sum of the H= Hit row

Answer(s): A,B

Explanation:

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



You have modeled the datasets with 5 independent variables called A,B,C,D and E having relationships which is not dependent each other, and also the variable A,B and C are continuous and variable D and E are discrete (mixed mode).

Now you have to compute the expected value of the variable let say A, then which of the following computation you will prefer

  1. Integration
  2. Differentiation
  3. Transformation
  4. Generalization

Answer(s): A

Explanation:







RMSE measures error of a predicted

  1. Numerical Value
  2. Categorical values
  3. For booth Numerical and categorical values

Answer(s): A



Suppose you have made a model for the rating system, which rates between 1 to 5 stars. And you calculated that RMSE value is 1.0 then which of the following is correct

  1. It means that your predictions are on average one star off of what people really think
  2. It means that your predictions are on average two star off of what people really think
  3. It means that your predictions are on average three star off of what people really think
  4. It means that your predictions are on average four star off of what people really think

Answer(s): A



You are creating a regression model with the input income, education and current debt of a customer, what could be the possible output from this model.

  1. Customer fit as a good
  2. Customer fit as acceptable or average category
  3. expressed as a percent, that the customer will default on a loan
  4. 1 and 3 are correct
  5. 2 and 3 are correct

Answer(s): C

Explanation:

Regression is the process of using several inputs to produce one or more outputs. For example The input might be the income, education and current debt of a customer The output might be the probability, expressed as a percent that the customer will default on a loan. Contrast this to classification where the output is not a number, but a class.



Viewing page 7 of 29
Viewing questions 31 - 35 out of 138 questions


Post your Comments and Discuss Databricks DATABRICKS-CERTIFIED-PROFESSIONAL-DATA-SCIENTIST exam prep with other Community members:

AI Tutor AI Tutor 👋 I’m here to help!