Free Databricks Databricks-Certified-Professional-Data-Scientist Exam Dumps Questions & Answers

You are working in a classification model for a book, written by HadoopExam Learning Resources and decided to use building a text classification model for determining whether this book is for Hadoop or Cloud computing. You have to select the proper features (feature selection) hence, to cut down on the size of the feature space, you will use the mutual information of each word with the label of hadoop or cloud to select the 1000 best features to use as input to a Naive Bayes model. When you compare the performance of a model built with the 250 best features to a model built with the 1000 best features, you notice that the model with only 250 features performs slightly better on our test data.
What would help you choose better features for your model?

• A.Decrease the size of our training data
• B.Include least mutual information with other selected features as a feature selection criterion
• C.Include the number of times each of the words appears in the book in your model
• D.Evaluate a model that only includes the top 100 words

Suppose you have been given two Random Variables X and Y, whose joint distribution is already known, the marginal distribution of X is simply the probability distribution of X averaging over information about Y.
It is the probability distribution of X when the value of Y is not known. So how do you calculate the marginal distribution of X

• A.This is typically calculated by summing (In case of discrete variable) the joint probability distribution over Y
• B.This is typically calculated by integrating the joint probability distribution over Y
• C.This is typically calculated by summing the joint probability distribution over Y.
• D.This is typically calculated by integrating(ln case of continuous variable) the joint probability distribution over Y.

As a data scientist consultant at ABC Corp, you are working on a recommendation engine for the learning resources for end user. So Which recommender system technique benefits most from additional user preference data?

• A.Logistic Regression
• B.Item-based collaborative filtering
• C.Naive Bayes classifier
• D.Content-based filtering

You are using one approach for the classification where to teach the agent not by giving explicit categorizations, but by using some sort of reward system to indicate success, where agents might be rewarded for doing certain actions and punished for doing others. Which kind of this learning

• A.Unsupervised
• B.Regression
• C.Supervised
• D.None of the above

Refer to image below

• A.Option C
• B.Option D
• C.Option B
• D.Option A