EMC E20-026 dumps - 100% Pass Guarantee!

For which class of problem is MapReduce most suitable?

A. Embarrassingly parallel

B. Minimal result data

C. Simple marginalization tasks

D. Non-overlapping queries

You are given 10, 000, 000 user profile pages of an online dating site in XML files, and they are stored in HDFS. You are assigned to divide the users into groups based on the content of their profiles. You have been instructed to try K-means clustering on this data. How should you proceed?

A. Run MapReduce to transform the data,and find relevant key value pairs.

B. Divide the data into sets of 1,000 user profiles,and run K-means clustering in RHadoop iteratively.

C. Run a Naive Bayes classification as a pre-processing step in HDFS.

D. Partition the data by XML file size,and run K-means clustering in each partition.

The Marketing department of your company wishes to track opinion on a new product that was recently introduced. Marketing would like to know how many positive and negative reviews are appearing over a given period and potentially retrieve each review for more in-depth insight. They have identified several popular product review blogs that historically have published thousands of user reviews of your company's products. You have been asked to provide the desired analysis. You examine the RSS feeds for each blog and determine which fields are relevant. You then craft a regular expression to match your new product's name and extract the relevant text from each matching review. What is the next step you should take?

A. Convert the extracted text into a suitable document representation and index into a review corpus

B. Use the extracted text and your regular expression to perform a sentiment analysis based on mentions of the new product

C. Read the extracted text for each review and manually tabulate the results

D. Group the reviews using Na.ve Bayesian classification

Which key role for a successful analytic project can consult and advise the project team on the value of end results and how these will be used on a day-to-day basis?

A. Business User

B. Project Manager

C. Data Scientist

D. Business Intelligence Analyst

Refer to the exhibit.

You have created a density plot of purchase amounts from a retail website as shown. What should you do

next?

A. Recreate the plot using the barplot() function

B. Use the rug() function to add elements to the plot

C. Recreate the density plot using a log normal distribution of the purchase amount data

D. Reduce the sample size of the purchase amount data used to create the plot