Question No. 1

What are the advantages of the Hashing Features?

ARequires the less memory

BLess pass through the training data

CEasily reverse engineer vectors to determine which original feature mapped to a vector location
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. This approach is known as feature hashing. The shoehorning is done by picking one or more locations by using a hash of the name of the variable for continuous variables or a hash of the variable name and the category name or word for categorical, textlike, or word-like data.
This hashed feature approach has the distinct advantage of requiring less memory and one less pass through the training data, but it can make it much harder to reverse engineer vectors to determine which original feature mapped to a vector location. This is because multiple features may hash to the same location. With large vectors or with multiple locations per feature, this isn't a problem for accuracy but it can make it hard to understand what a classifier is doing.
An additional benefit of feature hashing is that the unknown and unbounded vocabularies typical of word-like variables aren't a problem.

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Correct Answer: A, B

Question No. 2

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.

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Correct Answer: C

Question No. 3

A fruit may be considered to be an apple if it is red, round, and about 3" in diameter. A naive Bayes classifier considers each of these features to contribute independently to the probability that this fruit is an apple, regardless of the

APresence of the other features.

BAbsence of the other features.

CPresence or absence of the other features

DNone of the above
In simple terms, a naive Bayes classifier assumes that the value of a particular feature is unrelated to the presence or absence of any other feature, given the class variable. For example, a fruit may be considered to be an apple if it is red, round, and about 3' in diameter A naive Bayes classifier considers each of these features to contribute independently to the probability that this fruit is an apple, regardless of the presence or absence of the other features.

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Correct Answer: C

Question No. 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

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.

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Correct Answer: B

Question No. 5

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

AWhat all are the data sources for the project?

BAnalyze the Raw data and its format and structure.

CWhat all tools are required, in the project?

DWhat is the network capacity required

EWhat Unix server capacity required?
During the discovery phase you need to find how much resources are required as early as possible and for that even you can involve various stakeholders like Software engineering team, DBAs,
Network engineers, System administrators etc. for your requirement and these resources are already available or you need to procure them. Also, what would be source of the data? What all tools and software's are required to execute the same?

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

Free Databricks Databricks-Certified-Professional-Data-Scientist Exam Actual Questions

The questions for Databricks-Certified-Professional-Data-Scientist were last updated On Nov 17, 2024