- Exam Code: DEA-7TT2
- Exam Name: Associate - Data Science and Big Data Analytics v2 Exam
- Updated: Oct 07, 2026
- Q & A: 233 Questions and Answers
The topics of the Dell DEA-7TT2 Certification Exam are following:
Big Data, Analytics, and the Data Scientist Role (5%)
Data Analytics Lifecycle (8%)
Initial Analysis of the Data (15%)
Advanced Analytics - Theory, Application, and Interpretation of Results for
Eight Methods (40%)
Advanced Analytics for Big Data - Technology and Tools (22%)
Operationalizing an Analytics Project and Data Visualization Techniques
(10%)
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Here are the steps you need to take to register for the DEA-7TT2 Associate - Data Science and Big Data Analytics v2 Certification Exam:
Schedule your test
Read the DEA-7TT2 Certification Candidate Agreement
Register for a Pearson VUE account
Create an account on the EMC Proven Professional website
| Section | Weight | Objectives |
|---|---|---|
| Initial Analysis of the Data | 15% | - Statistical measures and data visualization - Basic R commands for data exploration - Hypothesis testing concepts and application |
| Data Analytics Lifecycle | 8% | - Model planning phase activities and roles - Data preparation phase activities and roles - Model building phase activities and roles - Communicating results and operationalization - Purpose and phases of the lifecycle - Discovery phase activities and roles |
| Operationalizing an Analytics Project and Data Visualization | 10% | - Visualization best practices and tools - Deployment and integration of models - Guidelines for communicating findings |
| Big Data, Analytics, and the Data Scientist Role | 5% | - Business drivers for Big Data analytics - Role and required skills of a Data Scientist - Characteristics of Big Data |
| Advanced Analytics for Big Data - Technology and Tools | 22% | - In-database analytics and SQL fundamentals - Spark and distributed processing frameworks - Advanced SQL: window functions, aggregates, MADlib - Hadoop ecosystem and its components |
| Advanced Analytics - Theory, Application, and Interpretation | 40% | - Unsupervised and supervised learning concepts - Classification and clustering techniques - Linear and logistic regression - Association rules and time series analysis - Model evaluation and validation methods |
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