Cloudera Databricks Data Science Certification Questions and Answers (Dumps and Practice Questions)

Question : Regularization is a very important technique in machine learning to prevent overfitting.
Mathematically speaking, it adds a regularization term in order to prevent the coefficients to fit so perfectly to overfit.
The difference between the L1 and L2 is_________

1. L2 is the sum of the square of the weights, while L1 is just the sum of the weights
2. L1 is the sum of the square of the weights, while L2 is just the sum of the weights
3. L1 gives Non-sparse output while L2 gives sparse outputs
4. None of the above


Correct Answer : 1


Explanation:Regularization is a very important technique in machine learning to prevent overfitting. Mathematically speaking, it adds a regularization term in order to prevent the coefficients to fit so perfectly to overfit. The difference between the L1 and L2 is just that L2 is the sum of the square of the weights, while L1 is just the sum of the weights





Question :


1.
2.
3.
4.


Correct Answer : 1


Explanation: we can use Maximum A Posteriori (MAP) estimation to estimate P(y) and P(xi | y) ; the former is then the relative frequency of class y in the training set.
The different naive Bayes classifiers differ mainly by the assumptions they make regarding the distribution of P(xi | y) .






Question : Select the correct option which applies to L regularization


1. Computational efficient due to having analytical solutions
2. Non-sparse outputs
3. No feature selection
4. All of the above

Correct Answer : 4

Explanation: The difference between their properties can be promptly summarized as follows:

L1 Regularization
1. Computational inefficient on non-sparse cases
2. Sparse outputs
3. Built-in feature selection

L2 Regularization
1. Computational efficient due to having analytical solutions
2. Non-Sparse outputs
3. No feature selection

Related Questions

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Question : In which of the following scenario we can use naive Bayes theorem for classification

1. Classify whether a given person is a male or a female based on the measured features. The features include height, weight, and foot size.
2. To classify whether an email is spam or not spam
3. To identify whether a fruit is an orange or not based on features like diameter, color and shape
4. All 1,2 and 3
5. None of the above



Question :

Select the choice where Regression algorithms are not best fit

1. When the dimension of the object given
2. Weight of the person is given
3. Temperature in the atmosphere
4. Employee status





Question : Logistic regression does not work well in case of binary classification


1. True
2. False




Question : Which of the following is not the Classification algorithm?


1. Logistic Regression
2. Support Vector Machine
3. Neural Network
4. Hidden Markov Models
5. None of the above




Question : Suppose a man told you he had a nice conversation with someone on the train. Not knowing anything
about this conversation, the probability that he was speaking to a woman is 50% (assuming the train had an equal
number of men and women and the speaker was as likely to strike up a conversation with a man as with a woman).
Now suppose he also told you that his conversational partner had long hair. It is now more likely he was speaking
to a woman, since women are more likely to have long hair than men. ____________ can be used to calculate
the probability that the person was a woman.

1. SVM
2. MLE
3. Bayes' theorem
4. Logistic Regression




Question : Bayes' theorem cannot finds the actual probability of an event from the results of your tests?


1. True
2. False