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Free Databricks Databricks-Certified-Professional-Data-Scientist Exam Actual Questions

The questions for Databricks-Certified-Professional-Data-Scientist were last updated On Jun 15, 2025

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Question No. 1

While working with Netflix the movie rating websites you have developed a recommender system that has produced ratings predictions for your data set that are consistently exactly 1 higher for the user-item pairs in your dataset than the ratings given in the dataset. There are n items in the dataset. What will be the calculated RMSE of your recommender system on the dataset?

Dn/2
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. RMSE is calculated as the square root of the mean of the squares of the errors. The error in every case in this example is 1. The square of 1 is 1 The average of n items with value 1 is 1 The square root of 1 is 1 The RMSE is therefore 1

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Correct Answer: A

Question No. 2

Which of the following technique can be used to the design of recommender systems?

ANaive Bayes classifier

BPower iteration

CCollaborative filtering

D1 and 3

E2 and 3
One approach to the design of recommender systems that has seen wide use is collaborative filtering. Collaborative filtering methods are based on collecting and analyzing a large amount of information on users' behaviors, activities or preferences and predicting what users will like based on their similarity to other users. A key advantage of the collaborative filtering approach is that it does not rely on machine analyzable content and therefore it is capable of accurately recommending complex items such as movies without requiring an 'understanding' of the item itself. Many algorithms have been used in measuring user similarity or item similarity in recommender systems. For example the k-nearest neighbor (k-NN) approach and the Pearson Correlation

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Correct Answer: C

Question No. 3

You are working on a email spam filtering assignment, while working on this you find there is new word e.g. HadoopExam comes in email, and in your solutions you never come across this word before, hence probability of this words is coming in either email could be zero. So which of the following algorithm can help you to avoid zero probability?

ANaive Bayes

BLaplace Smoothing

CLogistic Regression

DAll of the above
Laplace smoothing is a technique for parameter estimation which accounts for unobserved events. It is more robust and will not fail completely when data that has never been observed in training shows up.

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Correct Answer: B

Question No. 4

In unsupervised learning which statements correctly applies

AIt does not have a target variable

BInstead of telling the machine Predict Y for our data X, we're asking What can you tell me about X?

Ctelling the machine Predict Y for our data X
In unsupervised learning we don't have a target variable as we did in
classification and regression.
Instead of telling the machine Predict Y for our data X, we're asking What can you
tell me about X?
Things we ask the machine to tell us about
X may be What are the six best groups we can make out of X? or What three
features occur together most frequently in X?

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Correct Answer: A, B

Question No. 5

Select the choice where Regression algorithms are not best fit

AWhen the dimension of the object given

BWeight of the person is given

CTemperature in the atmosphere

DEmployee status
Regression algorithms are usually employed when the data points are inherently numerical variables (such as the dimensions of an object the weight of a person, or the temperature in the atmosphere) but unlike Bayesian algorithms, they're not very good for categorical data (such as employee status or credit score description).

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Correct Answer: D