Databricks Certified Professional Data Scientist Exam - Databricks-Certified-Professional-Data-Scientist Exam Practice Test

Question 1

Feature Hashing approach is "SGD-based classifiers avoid the need to predetermine vector size by simply picking a reasonable size and shoehorning the training data into vectors of that size" now with large vectors or with multiple locations per feature in Feature hashing?

A. It is easy to understand what classifier is doing

B. Is a problem with accuracy as well as hard to understand what classifier us doing

C. Is a problem with accuracy

D. It is hard to understand what classifier is doing

Correct Answer: D

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Question 2

You have used k-means clustering to classify behavior of 100, 000 customers for a retail store. You decide to use household income, age, gender and yearly purchase amount as measures. You have chosen to use 8 clusters and notice that 2 clusters only have 3 customers assigned. What should you do?

A. Identify additional measures to add to the analysis

B. Decrease the number of measures used

C. Increase the number of clusters

D. Decrease the number of clusters

Correct Answer: D

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Question 3

Which of the following metrics are useful in measuring the accuracy and quality of a recommender system?

A. Mean Absolute Error

B. Cluster Density

C. Sum of Absolute Errors

D. Support Vector Count

Correct Answer: A

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Question 4

Which of the following is not a correct application for the Classification?

A. credit scoring

B. image recognition

C. tumor detection

D. drug discovery

Correct Answer: D

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Question 5

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. Linear Regression

B. Naive Bayes Classifier

C. Association Rules

D. K-means Clustering

E. Grouping

Correct Answer: D

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Question 6

Question-3: In machine learning, feature hashing, also known as the hashing trick (by analogy to the kernel trick), is a fast and space-efficient way of vectorizing features (such as the words in a language), i.e., turning arbitrary features into indices in a vector or matrix. It works by applying a hash function to the features and using their hash values modulo the number of features as indices directly, rather than looking the indices up in an associative array. So what is the primary reason of the hashing trick for building classifiers?

A. It requires the lesser memory to store the coefficients for the model

B. It reduces the non-significant features e.g. punctuations

C. Noisy features are removed

D. It creates the smaller models

Correct Answer: A

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Question 7

Select the sequence of the developing machine learning applications
A) Analyze the input data
B) Prepare the input data
C) Collect data
D) Train the algorithm
E) Test the algorithm
F) Use It

A. C, B, A, D, E, F

B. C, B, A, D, E, F

C. A, B, C, D, E, F

D. C, A, B, D, E, F

Correct Answer: B

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Question 8

Which of the following skills a data scientists required?

A. Web designing to represent best visuals of its results from algorithm.

B. Should possess good programming skills

C. Should be very good at mathematics and statistic

D. He should possess database administrative skills.

E. He should be creative

Correct Answer: B,C,E

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Question 9

Select the correct problems which can be solved using SVMs

A. SVMs are also useful in medical science to classify proteins with up to 90% of the compounds classified correctly

B. Classification of images can also be performed using SVMs

C. SVMs are helpful in text and hypertext categorization

D. Hand-written characters can be recognized using SVM

Correct Answer: A,B,C,D

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Databricks Certified Professional Data Scientist - Databricks-Certified-Professional-Data-Scientist Exam Practice Test

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