EMT Practice Test

1. Question Content...

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Question1: What are the key outcomes of the successful analytical projects?

Question2: A bio-scientist is working on the analysis of the cancer cells. To identify whether the cell is cancerous or not, there has been hundreds of tests are done with small variations to say yes to the problem. Given the test result for a sample of healthy and cancerous cells, which of the following technique you will use to determine whether a cell is healthy?

Question3: Reducing the data from many features to a small number so that we can properly visualize it in two or three dimensions. It is done in_______

Question4: 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).

Question5: RMSE is a useful metric for evaluating which types of models?

Question6: Suppose there are three events then which formula must always be equal to P(E1|E2,E3)?

Question7: What describes a true limitation of Logistic Regression method?

Question8: Consider flipping a coin for which the probability of heads is p, where p is unknown, and our goa is to estimate p. The obvious approach is to count how many times the coin came up heads and divide by the total number of coin flips. If we flip the coin 1000 times and it comes up heads 367 times, it is very reasonable to estimate p as approximately 0.367. However, suppose we flip the coin only twice and we get heads both times.
Is it reasonable to estimate p as 1.0? Intuitively, given that we only flipped the coin twice, it seems a bit rash to conclude that the coin will always come up heads, and____________is a way of avoiding such rash conclusions.

Question9: Which of the following is not a correct application for the Classification?

Question10: Which of the below best describe the Principal component analysis

Question11: 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
?

Question12: 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?

Question13: Select the correct option from the below

Question14: You are working on a problem where you have to predict whether the claim is done valid or not. And you find that most of the claims which are having spelling errors as well as corrections in the manually filled claim forms compare to the honest claims. Which of the following technique is suitable to find out whether the claim is valid or not?

Question15: What type of output generated in case of linear regression?

Question16: In which of the scenario you can use the linear regression model?

Question17: 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?

Question18: In unsupervised learning which statements correctly applies

Question19: 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-of-squares (wss) data as shown in the exhibit.
How many customer groups should you specify?

Question20: What is one modeling or descriptive statistical function in MADlib that is typically not provided in a standard relational database?

Question21: Find out the classifier which assumes independence among all its features?

Question22: Question-18. What is the best way to ensure that the k-means algorithm will find a good clustering of a collection of vectors?

Question23: Question-26. There are 5000 different color balls, out of which 1200 are pink color. What is the maximum likelihood estimate for the proportion of "pink" items in the test set of color balls?

Question24: Select the correct statement which applies to Supervised learning

Question25: Which of the following problem you can solve using binomial distribution

Question26: What are the advantages of the Hashing Features?

Question27: Which activity is performed in the Operationalize phase of the Data Analytics Lifecycle?

Question28: Which of the following statement true with regards to Linear Regression Model?

Question29: You are doing advanced analytics for the one of the medical application using the regression and you have two variables which are weight and height and they are very important input variables, which cannot be ignored and they are also highly co-related. What is the best solution for that?

Question30: In which phase of the data analytics lifecycle do Data Scientists spend the most time in a project?

Question31: Select the choice where Regression algorithms are not best fit

Question32: Select the correct statement which applies to logistic regression

Question33: Which of the following could be features?

Question34: Which method is used to solve for coefficients bO, b1, ... bn in your linear regression model:

Question35: Which of the following true with regards to the K-Means clustering algorithm?

Question36: Logistic regression is a model used for prediction of the probability of occurrence of an event. It makes use of several variables that may be......

Question37: You are using k-means clustering to classify heart patients for a hospital. You have chosen Patient Sex, Height, Weight, Age and Income as measures and have used 3 clusters. When you create a pair-wise plot of the clusters, you notice that there is significant overlap between the clusters. What should you do?

Question38: 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?

Question39: You have modeled the datasets with 5 independent variables called A,B,C,D and E having relationships which is not dependent each other, and also the variable A,B and C are continuous and variable D and E are discrete (mixed mode).
Now you have to compute the expected value of the variable let say A, then which of the following computation you will prefer

Question40: Refer to exhibit

You are asked to write a report on how specific variables impact your client's sales using a data set provided to you by the client. The data includes 15 variables that the client views as directly related to sales, and you are restricted to these variables only. After a preliminary analysis of the data, the following findings were made: 1.
Multicollinearity is not an issue among the variables 2. Only three variables-A, B, and C-have significant correlation with sales You build a linear regression model on the dependent variable of sales with the independent variables of A, B, and C.
The results of the regression are seen in the exhibit. You cannot request additional data. what is a way that you could try to increase the R2 of the model without artificially inflating it?

Question41: You are working on a email spam filtering assignment, while working on this you find there is new word e.g.
HadoopExam comes in email, and in your solutions you never come across this word before, hence probability of this words is coming in either email could be zero. So which of the following algorithm can help you to avoid zero probability?

Question42: In statistics, maximum-likelihood estimation (MLE) is a method of estimating the parameters of a statistical model. When applied to a data set and given a statistical model, maximum-likelihood estimation provides estimates for the model's parameters and the normalizing constant usually ignored in MLEs because

Question43: Classification and regression are examples of___________.

Question44: You are creating a regression model with the input income, education and current debt of a customer, what could be the possible output from this model.

Question45: 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?

Question46: While working with Netflix the movie rating websites you have developed a recommender system that has produced ratings predictions for your data set that are consistently exactly 1 higher for the user-item pairs in your dataset than the ratings given in the dataset. There are n items in the dataset. What will be the calculated RMSE of your recommender system on the dataset?

Question47: A data scientist is asked to implement an article recommendation feature for an on-line magazine.
The magazine does not want to use client tracking technologies such as cookies or reading history. Therefore, only the style and subject matter of the current article is available for making recommendations. All of the magazine's articles are stored in a database in a format suitable for analytics.
Which method should the data scientist try first?

Question48: Which analytical method is considered unsupervised?

may have a trend component that is quadratic in nature. Which pattern of data will indicate that the trend in the time series data is quadratic in nature?

Question49: Select the correct problems which can be solved using SVMs