Databricks Certified Professional Data Scientist Exam (Databricks-Certified-Professional-Data-Scientist) Free Practice Test

Question 1

Select the correct statement which applies to logistic regression

A. May have low accuracy

B. Only 1 and 3 are correct

C. Computationally inexpensive, easy to implement knowledge representation easy to interpret

D. All 1, 2 and 3 are correct

E. Works with Numeric values

Correct Answer: D

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Question 2

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...

A. L1 is the sum of the square of the weights, while L2 is just the sum of the weights

B. None of the above

C. L1 gives Non-sparse output while L2 gives sparse outputs

D. L2 is the sum of the square of the weights, while L1 is just the sum of the weights

Correct Answer: D

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Question 3

Select the correct algorithm of unsupervised algorithm

A. Support Vector Machines

B. K-Means

C. Naive Bayes

D. K-Nearest Neighbors

Correct Answer: D

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Question 4

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?

A. K-means clustering

B. Logistic regression

C. Linear regression

D. Apriori algorithm

Correct Answer: B

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Question 5

Suppose that the probability that a pedestrian will be tul by a car while crossing the toad at a pedestrian crossing without paying attention to the traffic light is lo be computed. Let H be a discrete random variable taking one value from (Hit. Not Hit). Let L be a discrete random variable taking one value from (Red. Yellow.
Green).
Realistically, H will be dependent on L That is, P(H = Hit) and P(H = Not Hit) will take different values depending on whether L is red, yellow or green. A person is. for example, far more likely to be hit by a car when trying to cross while Hie lights for cross traffic are green than if they are red In other words, for any given possible pair of values for Hand L. one must consider the joint probability distribution of H and L to find the probability* of that pair of events occurring together if Hie pedestrian ignores the state of the light Here is a table showing the conditional probabilities of being bit. defending on ibe stale of the lights (Note that the columns in this table must add up to 1 because the probability of being hit oi not hit is 1 regardless of the stale of the light.)

A. marginal probability that P(H=Not Hit) is the sum of the H=Not Hit row

B. marginal probability that P(H=Not Hit) is the sum of the H= Hit row

C. The marginal probability P(H=Hit) is the sum along the H=Hit row of this joint distribution table, as this is the probability of being hit when the lights are red OR yellow OR green.

Correct Answer: A,C

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Question 6

A denote the event 'student is female' and let B denote the event 'student is French'. In a class of 100 students suppose 60 are French, and suppose that 10 of the French students are females. Find the probability that if I pick a French student, it will be a girl, that is, find P(A|B).

A. 2/6

B. 2/3

C. 1/6

D. 1/3

Correct Answer: C

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Question 7

You are working with the Clustering solution of the customer datasets. There are almost 40 variables are available for each customer and almost 1.00,0000 customer's data is available. You want to reduce the number of variables for clustering, what would you do?

A. You will find the correlation among the variables and from the highly co-related variables, you will be considering only one or two variables from it.

B. You can combine several variables in one variable

C. You will randomly reduce the number of variables

D. You cannot discard any variable for creating clusters.

E. You will find the correlation among the variables and from their variables are not co-related will be discarded.

Correct Answer: A,B

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Question 8

Which of the following steps you will be using in the discovery phase?

A. What Unix server capacity required?

B. Analyze the Raw data and its format and structure.

C. What all tools are required, in the project?

D. What is the network capacity required

E. What all are the data sources for the project?

Correct Answer: A,B,C,D,E

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Question 9

Spam filtering of the emails is an example of

A. 1 and 3 are correct

B. Unsupervised learning

C. Supervised learning

D. Clustering

E. 2 and 3 are correct

Correct Answer: C

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Question 10

Which of the below best describe the Principal component analysis

A. Regression

B. Classification

C. Dimensionality reduction

D. Clustering

E. Collaborative filtering

Correct Answer: C

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Databricks Certified Professional Data Scientist (Databricks-Certified-Professional-Data-Scientist) Free Practice Test