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EMC E20-065 Exam Overview:

Certification Vendor:Dell EMC
Exam Name:EMC E20-065 Advanced Analytics Specialist Exam for Data Scientists
Exam Number:E20-065
Related Certifications:EMC Data Science Specialist
EMC Data Science Associate
Available Languages:English
Exam Format:Scenario-based questions, Multiple choice
Sample Questions: DOWNLOAD DEMO
Exam Way:Computer-based exam delivered via proctored testing centers or online proctoring.
Pre Condition:Basic understanding of statistics, programming, and data analysis concepts is recommended.

EMC E20-065 Exam Syllabus Topics:

SectionObjectives
Topic 1: Machine Learning Methods- Unsupervised learning
  • 1. Clustering techniques
    • 2. Dimensionality reduction
      - Supervised learning
      • 1. Classification models
        • 2. Regression models
          Topic 2: Data Analysis and Exploration- Data visualization techniques
          - Exploratory data analysis (EDA)
          Topic 3: Model Evaluation and Deployment- Model evaluation metrics
          - Model validation and tuning
          Topic 4: Data Science Foundations- Data types and data preparation principles
          - Statistical concepts for analytics
          Topic 5: Big Data and Processing Technologies- Hadoop and Spark ecosystem overview
          - Distributed data processing concepts

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          The EMC Advanced Analytics Specialist Exam for Data Scientists blueprint covers these main domains:

          • Data Science Foundations
          • Machine Learning Methods
          • Model Evaluation and Deployment

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          EMC lists the following prerequisites for the EMC Advanced Analytics Specialist Exam for Data Scientists: Basic understanding of statistics, programming, and data analysis concepts is recommended..

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          EMC Advanced Analytics Specialist Exam for Data Scientists Sample Questions:

          Question #1

          What is a random subspace of features, as used by Random Forests?

          • A. A random subset of features that are chosen at each split in the decision tree
          • B. Filtration of data that does not meet a pre-defined weighting thrsehold
          • C. Removal of highly correlated variables to randomize the features
          • D. The creation of out-of-bag (OOB) data that is used to select features
          Reveal Solution  Discussion  0

          Correct Answer: A  🗳️

          Question #2

          What elements are needed to determine the time complexity of finding all the cliques of size k in social network analysis?

          • A. Eigenvector centrality and betwenness
          • B. Clique size and betweenness centrality
          • C. Number of edges in the network and centrality measure of the cliques
          • D. Clique size and total number of nodes in the network
          Reveal Solution  Discussion  0

          Correct Answer: D  🗳️

          Question #3

          What is a characteristic of lemmatization?

          • A. Reduces words of variant forms to their base forms based on a set of heuristics
          • B. Can be performed by calling the synset () function on a lemma in LNTK
          • C. Can be performed by calling the lemma() function on a synset in LNTK
          • D. Reduces words of variant forms to their base forms based on a dictionary
          Reveal Solution  Discussion  0

          Correct Answer: D  🗳️

          Question #4

          In a connected, undirected graph of 5 nodes with 10 edges, how many more edges need to be added to make the clustering coefficient of every node equal 1 ?

          • A. 5
          • B. 0
          • C. 10
          • D. 15
          Reveal Solution  Discussion  0

          Correct Answer: B  🗳️

          Question #5

          How does Latent Dinchlet Allocation (LDA) interpret a document?

          • A. As a single-predefined topic
          • B. As having a mixture of sentiments
          • C. As having a single pre-defined sentiment
          • D. As a mixture of pre-defined topics
          Reveal Solution  Discussion  0

          Correct Answer: D  🗳️

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