To help you get your Databricks Certified Professional Data Scientist Certification,Passcert provides the best Databricks Certified Professional Data Scientist Exam Dumps to guide you how to clear the Databricks Certified Professional Data Scientist Exam.With the help of the Databricks Certified Professional Data Scientist Exam Dumps, you can prepare for your Databricks Certified Professional Data Scientist Exam on your own in a short time. You can learn all the questions and answers to understand the concepts of all the topics and pass your Databricks Certified Professional Data Scientist Exam successfully.

The Databricks Certified Professional Data Scientist certification exam assesses the understanding of the basics of machine learning and the steps in the machine learning lifecycle, including data preparation, feature engineering, the training of models, model selection, interpreting models, and the production of models. The exam also assesses the understanding of basic machine learning algorithms and techniques, including linear regression, logistic regression, regularization, decision trees, tree-based ensembles, basic clustering algorithms, and matrix factorization techniques. The basics of model management with MLflow, like logging and model organization, are also assessed.

Number of Questions:60 questions

Duration: 120 minutes

Passing Score: 70%

Delevery: online proctor

Format: Multiple-choice

bias-variance tradeoff

in-sample vs. out-of sample data

categories of machine learning

applied statistics concepts

data preparation

feature engineering

model training, selection, and production

interpreting models

linear, logistic, and regularized regression

tree-based models like decision trees, random forest and gradient boosted trees

unsupervised techniniques like K-means and PCA

specific algorithms like ALS for recommendation and isolation forests for outlier detection

You are asked to create a model to predict the total number of monthly subscribers for a specific magazine. You are provided with 1 year's worth of subscription and payment data, user demographic data, and 10 years worth of content of the magazine (articles and pictures). Which algorithm is the most appropriate for building a predictive model for subscribers?

A.Linear regression

B.Logistic regression

C.Decision trees

D.TF-IDF

Answer : A

You are working in a data analytics company as a data scientist, you have been given a set of various types of Pizzas available across various premium food centers in a country. This data is given as numeric values like Calorie. Size, and Sale per day etc. You need to group all the pizzas with the similar properties, which of the following technique you would be using for that?

A.Association Rules

B.Naive Bayes Classifier

C.K-means Clustering

D.Linear Regression

E.Grouping

Answer : C

Which of the below best describe the Principal component analysis

A.Dimensionality reduction

B.Collaborative filtering

C.Classification

D.Regression

E.Clustering

Answer : A

You have collected the 100's of parameters about the 1000's of websites e.g. daily hits, average time on the websites, number of unique visitors, number of returning visitors etc. Now you have find the most important parameters which can best describe a website, so which of the following technique you will use

A.PCA (Principal component analysis)

B.Linear Regression

C.Logistic Regression

D.Clustering

Answer : A

Refer to the exhibit.

You are building a decision tree. In this exhibit, four variables are listed with their respective values of info-gain.

Based on this information, on which attribute would you expect the next split to be in the decision tree?

A.Credit Score

B.Age

C.Income

D.Gender

Answer : A