Dell EMC Data Science and BigData Certification Questions and Answers

Question : Refer to the exhibit.
You have scored your Naive bayesian classifier model on a hold out test data for cross validation
and determined the way the samples scored and tabluated them as shown in the exhibit.
What are the Precision and Recall rate of the model?


1. Precision = 262/277
Recall = 262/288
2. Precision =262/288
Recall = 262/277
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Recall = 288/262
4. Precision = 288/262
Recall = 277/262



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Question : Which ROC curve represents a perfect model fit?

1. A
2. B
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4. D



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Question : Refer to the exhibit.
You have scored your Naive bayesian classifier model on a hold out test data for cross validation
and determined the way the samples scored and tabulated them as shown in the exhibit.
What are the the False Positive Rate (FPR) and the False Negative Rate (FNR) of the model?

1. FPR = 15/262
FNR = 26/288
2. FPR = 26/288
FNR = 15/262
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FNR = 288/26
4. FPR = 288/26
FNR = 262/15



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Related Questions

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Question : What is required in a presentation for business analysts?


1. Operational process changes
2. Budgetary considerations and requests
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4. The presentation author's credentials




Question : What is LOESS used for?

1. It plots a continuous variable versus a discrete variable, to compare distributions across classes.
2. It is a significance test for the correlation between two variables.
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4. It is run after a one-way ANOVA, to determine which population has the highest mean value.




Question : Which word or phrase completes the statement? Mahout is to Hadoop as MADlib is to
____________ .


1. R
2. PostgreSQL
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4. SAS




Question : In linear regression modeling, which action can be taken to improve the linearity of the relationship
between the dependent and independent variables?


1. Apply a transformation to a variable
2. Use a different statistical package
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4. Change the units of measurement on the independent variable




Question : Data visualization is used in the final presentation of an analytics project. For what else is this
technique commonly used?


1. ETLT
2. Descriptive statistics
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4. Model selection




Question : You have been assigned to do a study of the daily revenue effect of a pricing model of online
transactions. All the data currently available to you has been loaded into your analytics database;
revenue data, pricing data, and online transaction data. You find that all the data comes in
different levels of granularity. The transaction data has timestamps (day, hour, minutes, seconds),
pricing is stored at the daily level, and revenue data is only reported monthly. What is your next
step?



1. Interpolate a daily model for revenue from the monthly revenue data.
2. Aggregate all data to the monthly level in order to create a monthly revenue model.
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question.
4. Disregard revenue as a driver in the pricing model, and create a daily model based on pricing
and transactions only.