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**Frequently Asked Questions**

#### Question # 1

**Projecting a multi-dimensional dataset onto which vector has the greatest variance? **

A. first principal component

B. first eigenvector

C. not enough information given to answer

D. second eigenvector

E. second principal component

#### Question # 2

**You are creating a Classification process where input is the income, education and current
debt of a customer, what could be the possible output of this process. **

A. Probability of the customer default on loan repayment

B. Percentage of the customer loan repayment capability

C. Percentage of the customer should be given loan or not

D. The output might be a risk class, such as "good", "acceptable", "average", or "unacceptable".

#### Question # 3

**Suppose you have been given two Random Variables X and Y, whose joint distribution is
already known, the marginal distribution of X is simply the probability distribution of X
averaging over information about Y. It is the probability distribution of X when the value of Y
is not known. So how do you calculate the marginal distribution of X**

A. This is typically calculated by summing the joint probability distribution over Y.

B. This is typically calculated by integrating the joint probability distribution over Y

C. This is typically calculated by summing (In case of discrete variable) the joint probability distribution over Y

D. This is typically calculated by integrating(ln case of continuous variable) the joint probability distribution over Y.

#### Question # 4

**What are the advantages of the mutual information over the Pearson correlation for text
classification problems?**

A. The mutual information has a meaningful test for statistical significance.

B. The mutual information can signal non-linear relationships between the dependent and independent variables.

C. The mutual information is easier to parallelize.

D. The mutual information doesn't assume that the variables are normally distributed.

#### Question # 5

**In which of the scenario you can use the linear regression model?**

A. Predicting Home Price based on the location and house area

B. Predicting demand of the goods and services based on the weather

C. Predicting tumor size reduction based on input as number of radiation treatment

D. Predicting sales of the text book based on the number of students in state

#### Question # 6

**Digit recognition, is an example of..... **

A. Classification

B. Clustering

C. Unsupervised learning

D. None of the above

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

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

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

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

#### Question # 8

**Google Adwords studies the number of men, and women, clicking the advertisement on
search
engine during the midnight for an hour each day.
Google find that the number of men that click can be modeled as a random variable with
distribution
Poisson(X), and likewise the number of women that click as Poisson(Y).
What is likely to be the best model of the total number of advertisement clicks during the
midnight for an hour ?**

A. Binomial(X+Y,X+Y)

B. Poisson(X/Y)

C. Normal(X+Y(M+Y)1/2)

D. Poisson(X+Y)

#### Question # 9

**Which of the below best describe the Principal component analysis **

A. Dimensionality reduction

B. Collaborative filtering

C. Classification

D. Regression

E. Clustering

#### Question # 10

**What are the key outcomes of the successful analytical projects? **

A. Code of the model

B. Technical specifications

C. Presentations for the Analysts

D. Presentation for Project Sponsors

#### Question # 11

**You are working on a Data Science project and during the project you have been gibe a
responsibility to interview all the stakeholders in the project. In which phase of the project
you are? **

A. Discovery

B. Data Preparations

C. Creating Models

D. Executing Models

E. Creating visuals from the outcome

F. Operationnalise the models

#### Question # 12

**Select the correct option from the below**

A. If you're trying to predict or forecast a target value^ then you need to look into supervised learning.

B. If you've chosen supervised learning, with discrete target value like Yes/No. 1/2/3, A/B/C: or Red/Yellow/Black, then look into classification.

C. If the target value can take on a number of values, say any value from 0.00 to 100.00, or
-999 to 999: or +_to -_, then you need to look unsupervised learning

D. If you're not trying to predict a target value, then you need to look into unsupervised learning

E. Are you trying to fit your data into some discrete groups? If so and that's all you need, you should look into clustering.

#### Question # 13

**Classification and regression are examples of___________. **

A. supervised learning

B. un-supervised learning

C. Clustering

D. Density estimation

#### Question # 14

**Select the correct statement regarding the naive Bayes classification **

A. it only requires a small amount of training data to estimate the parameters

B. Independent variables can be assumed

C. only the variances of the variables for each class need to be determined

D. for each class entire covariance matrix need to be determined

#### Question # 15

**You are creating a model for the recommending the book at Amazon.com, so which of the
following recommender system you will use you don't have cold start problem?**

A. Naive Bayes classifier

B. Item-based collaborative filtering

C. User-based collaborative filtering

D. Content-based filtering

#### Question # 16

**Suppose you have made a model for the rating system, which rates between 1 to 5 stars.
And you calculated that RMSE value is 1.0 then which of the following is correct **

A. It means that your predictions are on average one star off of what people really think

B. It means that your predictions are on average two star off of what people really think

C. It means that your predictions are on average three star off of what people really think

D. It means that your predictions are on average four star off of what people really think

#### Question # 17

**Spam filtering of the emails is an example of**

A. Supervised learning

B. Unsupervised learning

C. Clustering

D. 1 and 3 are correct

E. 2 and 3 are correct

#### Question # 18

**Refer to the exhibit.
You are using K-means clustering to classify customer behavior for a large retailer. You
need to determine the optimum number of customer groups. You plot the within-sum-ofsquares (wss) data as shown in the exhibit. How many customer groups should you
specify?**

A. 2

B. 3

C. 4

D. 8

#### Question # 19

**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. Decrease the number of measures used

B. Increase the number of clusters

C. Decrease the number of clusters

D. Identify additional measures to add to the analysis

#### Question # 20

**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

#### Question # 21

**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. 1/3

B. 2/3

C. 1/6

D. 2/6

#### Question # 22

**Select the correct statement which applies to logistic regression **

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

B. May have low accuracy

C. Works with Numeric values

D. Only 1 and 3 are correct

E. All 1, 2 and 3 are correct

#### Question # 23

**Suppose A, B , and C are events. The probability of A given B , relative to P(|C), is the
same as the probability of A given B and C (relative to P ). That is, **

A. P(A,B|C) P(B|C) =P(A|B,C)

B. P(A,B|C) P(B|C) =P(B|A,C)

C. P(A,B|C) P(B|C) =P(C|B,C)

D. P(A,B|C) P(B|C) =P(A|C,B)

#### Question # 24

**You are working in an ecommerce organization, where you are designing and evaluating a
recommender system, you need to select which of the following metric wilt always have the largest value?**

A. Root Mean Square Error

B. Sum of Errors

C. Mean Absolute Error

D. Both land 2

E. Information is not good enough.

#### Question # 25

**You are using one approach for the classification where to teach the agent not by giving
explicit categorizations, but by using some sort of reward system to indicate success,
where agents might be rewarded for doing certain actions and punished for doing others.
Which kind of this learning **

A. Supervised

B. Unsupervised

C. Regression

D. None of the above

#### Question # 26

**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

#### Question # 27

**Which of the following problem you can solve using binomial distribution**

A. A manufacturer of metal pistons finds that on the average: 12% of his pistons are
rejected because they are either oversize or undersize. What is the probability that a batch
of 10 pistons will contain no more than 2 rejects?

B. A life insurance salesman sells on the average 3 life insurance policies per week. Use Poisson's law to calculate the probability that in a given week he will sell Some policies

C. Vehicles pass through a junction on a busy road at an average rate of 300 per hour Find the probability that none passes in a given minute.

D. It was found that the mean length of 100 parts produced by a lathe was 20.05 mm with a standard deviation of 0.02 mm. Find the probability that a part selected at random would have a length between 20.03 mm and 20.08 mm

#### Question # 28

**Select the correct option which applies to L2 regularization **

A. Computational efficient due to having analytical solutions

B. Non-sparse outputs

C. No feature selection

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