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

DEA-7TT2 real exams

Exam Code: DEA-7TT2

Exam Name: Associate - Data Science and Big Data Analytics v2 Exam

Updated: Sep 12, 2026

Q & A: 233 Questions and Answers

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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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As Dell DEA-7TT2 certification is contracting rapidly in the IT world, the need of its certification is growing day by day. This certification is really helpful for an individual to excel in the field of IT. If you have this certification in hand, then you can stand out among your peers and colleagues.

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What are the prerequisites for Dell DEA-7TT2 Certification Exam?

There are no formal prerequisites for Dell EMC DEA-7TT2 exam. However, it's recommended that you have:

  • 3 months hands-on experience with data science tools

  • 6 months experience in the IT industry and knowledge of data science fundamentals

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EMC DEA-7TT2 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: 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
Topic 2: Advanced Analytics for Big Data - Technology and Tools22%- Big Data technologies and analytics tools
  • 1. In-database analytics and SQL essentials
  • 2. MapReduce and Apache Hadoop
  • 3. Hadoop ecosystem and product use cases
  • 4. Advanced SQL methods and MADlib
  • 5. Big Data technology challenges
Topic 3: Initial Analysis of the Data15%- Data exploration and statistical analysis
  • 1. Basic R commands for data analysis
  • 2. Statistical measures and data visualization
  • 3. Hypothesis testing and model evaluation
Topic 4: 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
Topic 5: Data Analytics Lifecycle8%- Data Analytics Lifecycle phases
  • 1. Discovery phase activities and roles
  • 2. Model building phase activities and roles
  • 3. Model planning phase activities and roles
  • 4. Data preparation phase activities and roles
Topic 6: Advanced Analytics - Theory, Application, and Interpretation of Results for Eight Methods40%- Advanced analytics methods
  • 1. Logistic regression
  • 2. Decision trees
  • 3. Naïve Bayesian classifiers
  • 4. Time Series Analysis
  • 5. Association rules
  • 6. Linear regression
  • 7. Text Analytics
  • 8. K-means clustering

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