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Free DATABRICKS-CERTIFIED-PROFESSIONAL-DATA-SCIENTIST Exam Braindumps
Databricks Certified Professional Data Scientist Exam Questions 31–35 (page: 8)

Updated On: 9-Feb-2026
Page 8 of 29
PDF with 138 Questions

In which of the scenario you can use the regression to predict the values

  1. Samsung can use it for mobile sales forecast
  2. Mobile companies can use it to forecast manufacturing defects
  3. Probability of the celebrity divorce
  4. Only 1 and 2
  5. All 1 ,2 and 3

Answer(s): E

Explanation:

Regression is a tool which Companies may use this for things such as sales forecasts or forecasting manufacturing defects. Another creative example is predicting the probability of celebrity divorce.



RMSE is a good measure of accuracy, but only to compare forecasting errors of different models for a______, as it is scale-dependent.

  1. Between Variables
  2. Particular Variable
  3. Among all the variables
  4. All of the above are correct

Answer(s): B

Explanation:

The RMSE serves to aggregate the magnitudes of the errors in predictions for various times into a single measure of predictive power. RMSE is a good measure of accuracy, but only to compare forecasting errors of different models for a particular variable and not between variables, as it is scale-dependent.



You are creating a Classification process where input is the income, education and current debt of a customer, what could be the possible output of this process.

  1. Probability of the customer default on loan repayment
  2. Percentage of the customer loan repayment capability
  3. Percentage of the customer should be given loan or not
  4. The output might be a risk class, such as "good", "acceptable", "average", or "unacceptable".

Answer(s): D

Explanation:

Classification 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 a risk class, such as "good", "acceptable", "average", or "unacceptable". Contrast this to regression where the output is a number not a class.



Let's say you have two cases as below for the movie ratings
1. You recommend to a user a movie with four stars and he really doesn't like it and he'd rate it two stars
2. You recommend a movie with three stars but the user loves it (he'd rate it five stars). So which statement correctly applies?

  1. In both cases, the contribution to the RMSE is the same
  2. In both cases, the contribution to the RMSE is the different
  3. In both cases, the contribution to the RMSE, could varies
  4. None of the above

Answer(s): A



RMSE is a useful metric for evaluating which types of models?

  1. Logistic regression
  2. Naive Bayes classifier
  3. Linear regression
  4. All of the above

Answer(s): C

Explanation:

Error calculation allows you to see how well a machine learning method is performing.
One way of determining this performance is to calculate a numerical error This number is sometimes a percent,
however it can also be a score or distance. The goal is usually to minimize an error percent or distance:
however th goal may be to minimize or maximize a score. Encog supports the following error calculation methods.

Sum of Squares Error (ESS)
Root Mean Square Error (RMS)
Mean Square Error (MSE) (default)
SOM Error (Euclidean Distance Error)

RMSE measures error of a predicted numeric value, and so applies to contexts like regression and some recommender system techniques,
which rely on predicting a numeric value. It is not relevant to classification techniques like logistic regression and Naive Bayes, which predict categorical values. It also is not relevant to unsupervied techniques like clustering. The root-mean-square deviation (RMSD) or root-mean-square error (RMSE) is a frequently used measure of the differences between values predicted by a model or an estimator and the values actually observed. Basically,
the RMSD represents the sample standard deviation of the differences between predicted values and observed values.
These individual differences are called residuals when the calculations are performed over the data sample that was used for estimation, and are called prediction errors when computed out-of-sample. The RMSD serves to aggregate the magnitudes of the errors in predictions for various times into a single measure of predictive power. RMSD is a good measure of accuracy,
but only to compare forecasting errors of different models for a particular variable and not between variables, as it is scale-dependent.



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