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Databricks-Certified-Professional-Data-Scientist Exam Dump

Vendor

Databricks

Exam Code

Databricks-Certified-Professional-Data-Scientist

Exam Name

Databricks Certified Professional Data Scientist Exam

Exam Duration

Total Questions

138

$ 49

PDF VERSION

DEMO QUESTIONS

Q1:

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 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.

Correct Answer: C

Q2:

Which of the following is a Continuous Probability Distributions?

A: Binomial probability distribution

B: Negative binomial distribution

C: Poisson probability distribution

D: Normal probability distribution

Correct Answer: D

Q3:

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 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.

Correct Answer: C

Q4:

You are having 1000 patients’ data with the height and age. Where age in years and height in meters. You wanted to create cluster using this two attributes. You wanted to have near equal effect for both the age and height while creating the cluster. What you can do?

A: You will be adding height with the numeric value 100

B: You will be converting each height value to centimeters

C: You will be dividing both age and height with their respective standard deviation

D: You will be taking square root of height When you see the data age in years would have values like 50, 60r 70 90 years etc. And while calculating distance from centroid maximum possible value can be 90-0 and its square will be 8100. While using heights in meter can be 2-0.5(1.5) meters and its square will be 2.25 only. So you can see age has more effect than height. Hence bringing the height on same level you can convert it into centimeters. Can bring data upto 200 centimeters and then it be more effective like square of 200 maximum. However there is another approach is to divide the each value with its standard deviation, which will not have impact of the units e.g. age/sd of the age, which results in value without unit. This can also help in reducing the effect of units.

Correct Answer: B, C

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