Exam Name: Associate - Data Science and Big Data Analytics v2 Exam
Certification Provider: EMC
Corresponding Certification: Dell EMC Certification
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EMC DEA-7TT2 Certification Exam is a comprehensive exam that covers a wide range of topics. DEA-7TT2 exam is divided into four main sections: data science concepts, big data analytics, data mining, and machine learning. Each section contains a number of questions that are designed to test the candidate’s knowledge and skills in that particular area. DEA-7TT2 exam is multiple-choice and requires candidates to answer 60 questions in 90 minutes.
EMC DEA-7TT2 certification exam is designed to test the skills and knowledge of professionals working in the fields of data science and big data analytics. DEA-7TT2 exam is designed to test the proficiency of candidates in deploying and managing big data environments, identifying and analyzing data sets, and implementing high-performance algorithms. Associate - Data Science and Big Data Analytics v2 Exam certification provides candidates with a strong foundation in the use of big data analytics tools, techniques and frameworks, and enables them to work with big data in real-world environments.
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EMC DEA-7TT2 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Advanced Analytics for Big Data - Technology and Tools | 22% | - In-database analytics and SQL fundamentals - Hadoop ecosystem and its components - Spark and distributed processing frameworks - Advanced SQL: window functions, aggregates, MADlib |
| 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 |
| Operationalizing an Analytics Project and Data Visualization | 10% | - Deployment and integration of models - Guidelines for communicating findings - Visualization best practices and tools |
| Initial Analysis of the Data | 15% | - Statistical measures and data visualization - Hypothesis testing concepts and application - Basic R commands for data exploration |
| Data Analytics Lifecycle | 8% | - Model planning phase activities and roles - Discovery phase activities and roles - Communicating results and operationalization - Data preparation phase activities and roles - Purpose and phases of the lifecycle - Model building phase activities and roles |
| Advanced Analytics - Theory, Application, and Interpretation | 40% | - Association rules and time series analysis - Model evaluation and validation methods - Classification and clustering techniques - Unsupervised and supervised learning concepts - Linear and logistic regression |
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