E20-007 Dumps

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EMC E20-007 dumps - 100% Pass Guarantee!

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Vendor: EMC

Certifications: Data Scientist

Exam Name: Data Science and Big Data Analytics

Exam Code: E20-007

Total Questions: 198 Q&As

Last Updated: Feb 23, 2024

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E20-007 Online Practice Questions and Answers

Questions 1

For which class of problem is MapReduce most suitable?

A. Embarrassingly parallel

B. Minimal result data

C. Simple marginalization tasks

D. Non-overlapping queries

Show Answer
Questions 2

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?

A. K Means Clustering

B. Naive Bayesian

C. Logistic Regression

D. Association Rules

Show Answer
Questions 3

Assume you are performing an analysis to determine fraud detection on credit card usage. You will need to ensure higher-risk transactions. These may indicate that fraudulent credit card activity is retained in your data for analysis and not dropped as outliers during pre- processing.

What is the approach for loading data into the analytical sandbox for this analysis?





Show Answer
Questions 4

A data scientist is given an R data frame, "empdata", with the columns Age, Salary, Occupation, Education, and Gender. The data scientist would like to examine only the Salary and Occupation columns for ages greater than 40. Which command extracts the appropriate rows and columns from the data frame?

A. empdata[empdata$Age > 40, c("Salary", "Occupation")]

B. empdata[c("Salary", "Occupation"), empdata$Age > 40]

C. empdata[Age > 40, ("Salary", "Occupation")]

D. empdata[, c("Salary", "Occupation")]$Age > 40

Show Answer
Questions 5

In which lifecycle stage are test and training data sets created?

A. Model building

B. Model planning

C. Discovery

D. Data preparation

Show Answer More Questions

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