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EMC Associate - Data Science and Big Data Analytics v2 - DEA-7TT2 real prep

DEA-7TT2
  • Exam Code: DEA-7TT2
  • Exam Name: Associate - Data Science and Big Data Analytics v2 Exam
  • Updated: Jul 30, 2026
  • Q & A: 233 Questions and Answers
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Get to know about the topics of the Dell DEA-7TT2 Certification Exam

The topics of the Dell DEA-7TT2 Certification Exam are following:

  • Big Data, Analytics, and the Data Scientist Role (5%)

  • Initial Analysis of the Data (15%)

  • Advanced Analytics - Theory, Application, and Interpretation of Results for

  • Data Analytics Lifecycle (8%)

Eight Methods (40%)

  • Operationalizing an Analytics Project and Data Visualization Techniques

  • Advanced Analytics for Big Data - Technology and Tools (22%)

(10%)

Reference: https://education.emc.com/content/dam/dell-emc/documents/en-us/DEA-7TT2_Associate-Data_Science_and_Big_Data_Analytics_v2_Exam.pdf

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Who is this Dell DEA-7TT2 Certification Exam for?

Dell DEA-7TT2 Exam provides user with the opportunity to prove their skills and knowledge of Data Science and Big Data Analytics v2. The test is available for individuals who are interested in assessing their ability to analyze data with tools, develop and implement models, design experiments, and use methods such as machine learning. This certification is suitable for those who have a solid understanding of the concepts related to data science and big data analytics; these may include linear regression and logistic regression, modeling approaches, distributions, random variables, probability theory, fundamentals of statistics, hypothesis testing, exploratory data analysis, clustering techniques, predictive modeling, classification algorithm, and others. Dell DEA-7TT2 exam dumps will help you to clear your concepts.

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EMC DEA-7TT2 Exam Syllabus Topics:

SectionWeightObjectives
Advanced Analytics for Big Data - Technology and Tools22%- Big Data technologies and analytics tools
  • 1. MapReduce and Apache Hadoop
  • 2. In-database analytics and SQL essentials
  • 3. Hadoop ecosystem and product use cases
  • 4. Big Data technology challenges
  • 5. Advanced SQL methods and MADlib
Big Data, Analytics, and the Data Scientist Role5%- Characteristics of Big Data
  • 1. Business drivers for Big Data analytics and data science
  • 2. Data Scientist role and related skills
Operationalizing an Analytics Project and Data Visualization Techniques10%- Analytics project deployment and visualization
  • 1. Planning and creating effective data visualizations
  • 2. Building project presentations for audiences
  • 3. Communicating findings and operationalizing analytics projects
Advanced Analytics - Theory, Application, and Interpretation of Results for Eight Methods40%- Advanced analytics methods
  • 1. Text Analytics
  • 2. Decision trees
  • 3. Logistic regression
  • 4. Time Series Analysis
  • 5. Association rules
  • 6. Naïve Bayesian classifiers
  • 7. K-means clustering
  • 8. Linear regression
Initial Analysis of the Data15%- Data exploration and statistical analysis
  • 1. Hypothesis testing and model evaluation
  • 2. Statistical measures and data visualization
  • 3. Basic R commands for data analysis
Data Analytics Lifecycle8%- Data Analytics Lifecycle phases
  • 1. Data preparation phase activities and roles
  • 2. Discovery phase activities and roles
  • 3. Model planning phase activities and roles
  • 4. Model building phase activities and roles

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