E20-026 Exam Details

  • Exam Code
    :E20-026
  • Exam Name
    :Enterprise Storage Networking Specialist Exam
  • Certification
    :EMC Certifications
  • Vendor
    :EMC
  • Total Questions
    :163 Q&As
  • Last Updated
    :Jul 12, 2026

EMC E20-026 Online Questions & Answers

  • Question 161:

    You are asked to create a model to predict the total number of monthly subscribers for a specific magazine. You are provided with 1 year's worth of subscription and payment data, user demographic data, and 10 years worth of content of the magazine (articles and pictures). Which algorithm is the most appropriate for building a predictive model for subscribers?

    A. Linear regression
    B. Logistic regression
    C. Decision trees
    D. TF-IDF

  • Question 162:

    Before you build an ARMA model, how can you tell if your time series is weakly stationary?

    A. There appears to be a constant variance around a constant mean.
    B. The mean of the series is close to 0.
    C. The series is normally distributed.
    D. There appears to be no apparent trend component.

  • Question 163:

    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

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