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Databricks-Certified-Professional-Data-Scientist: Databricks Certified Professional Data Scientist Exam

Databricks Certified Professional Data Scientist Exam

Certification Provider: Databricks

Exam Name: Databricks Certified Professional Data Scientist Exam

Number of questions in our database: 138

Exam Version: May. 23, 2023

Exam Official Topics:

Topic 1: A intermediate understanding of the steps in the machine learning lifecycle/ Model training, selection, and production A complete understanding of the basics of machine learning/ in-sample vs. out-of sample data
Topic 2: Applied statistics concepts/ bias-variance tradeoff
Topic 3: A complete understanding of basic machine learning algorithms and techniques/ Unsupervised techniniques like K-means and PCA
Topic 4: A complete understanding of the basics of machine learning model management/ Linear, logistic, and regularized regression
Topic 5: Tree-based models like decision trees, random forest and gradient boosted trees/ Categories of machine learning
Topic 6: Specific algorithms like ALS for recommendation and isolation forests for outlier detection/ Logging and model organization with MLflow

Free Databricks Databricks Certified Professional Data Scientist Exam Exam Actual Questions

The questions for Databricks Certified Professional Data Scientist Exam were last updated On May. 23, 2023

Question #1

A data scientist wants to predict the probability of death from heart disease based on three risk factors: age, gender, and blood cholesterol level. What is the most appropriate method for this project?

ALinear regression

BK-means clustering

CLogistic regression

DApriori algorithm
Logistic regression is used widely in many fields, including the medical and social sciences. For example, the Trauma and Injury Severity Score (TRISS), which is widely used to predict mortality in injured patients, was originally developed by Boyd et al. using logistic regression. Many other medical scales used to assess severity of a patient have been developed using logistic regression. Logistic regression may be used to predict whether a patient has a given disease (e.g. diabetes; coronary heart disease), based on observed characteristics of the patient (age, sex, body mass index, results of various blood tests, etc.; age, blood cholesterol level, systolic blood pressure, relative weight, blood hemoglobin level, smoking (at 3 levels), and abnormal electrocardiogram.).Another example might be to predict whether an American voter will vote Democratic or Republican, based on age, income, sex, race, state of residence, votes in previous elections, etc. The technique can also be used in engineering, especially for predicting the probability of failure of a given process, system or product. It is also used in marketing applications such as prediction of a customer's propensity to purchase a product or halt a subscription, etc.[citation needed] In economics it can be used to predict the likelihood of a person's choosing to be in the labor force, and a business application would be to predict the likelihood of a homeowner defaulting on a mortgage. Conditional random fields, an extension of logistic regression to sequential data, are used in natural language processing.

Reveal Solution

Correct Answer: C

Question #2

The figure below shows a plot of the data of a data matrix M that is 1000 x 2. Which line represents the first principal component?

Ayellow

Bblue

CNeither
Principal component analysis (PCA) involves a mathematical procedure that transforms a number of (possibly) correlated variables into a (smaller) number of uncorrelated variables called principal components. The first principal component
accounts for as much of the variability in the data as possible, and each succeeding component accounts for as much of the remaining variability as possible.
The first principal component corresponds to the greatest variance in the data. The blue line is evidently this first principal component, because if we project the data onto the blue line, the data is more spread out (higher variance) than if projected
onto any other line, including the yellow one.

Reveal Solution

Correct Answer: B

Question #3

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?

AAssociation Rules

BNaive Bayes Classifier

CK-means Clustering

DLinear Regression

EGrouping
Using K means clustering you can create group of objects based on their properties. Where K is number of the groups. In this case, in each group you determine the center of the group and then find the how far each object characteristics from the center. If it is near the center than it can be part of the group. Suppose we have 100 objects and we need to determine 4 groups. Hence, here K=4. Now we determine 4 center values and based on that center value we determine the distance of each object from the center.

Reveal Solution

Correct Answer: C

Question #4

The figure below shows a plot of the data of a data matrix M that is 1000 x 2. Which line represents the first principal component?

Ayellow

Bblue

Reveal Solution

Correct Answer: B

Question #5

AAssociation Rules

BNaive Bayes Classifier

CK-means Clustering

DLinear Regression

Reveal Solution

Correct Answer: C

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