EMC DEA-7TT2 Exam covers a wide range of topics, including data science and analytics fundamentals, data exploration and visualization, data preparation, machine learning, big data technologies, and data science ethics. DEA-7TT2 exam consists of 60 questions, and candidates have 90 minutes to complete it. DEA-7TT2 exam is available in multiple languages, including English, Japanese, Simplified Chinese, and Korean.
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EMC DEA-7TT2 Exam comprises 60 multiple-choice questions that are designed to test the candidate's knowledge of Big Data Analytics concepts, techniques, tools, and technology. DEA-7TT2 exam is conducted through Pearson VUE, a leading technology provider that offers online testing services. DEA-7TT2 exam duration is 90 minutes, and it's mandatory to complete the exam within the given time frame. DEA-7TT2 exam is available in the English language and can be taken globally.
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EMC DEA-7TT2 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Advanced Analytics - Theory, Application, and Interpretation | 40% | - Model evaluation and validation methods - Unsupervised and supervised learning concepts - Association rules and time series analysis - Classification and clustering techniques - Linear and logistic regression |
| Topic 2: Data Analytics Lifecycle | 8% | - Discovery phase activities and roles - Purpose and phases of the lifecycle - Communicating results and operationalization - Data preparation phase activities and roles - Model building phase activities and roles - Model planning phase activities and roles |
| Topic 3: Initial Analysis of the Data | 15% | - Hypothesis testing concepts and application - Statistical measures and data visualization - Basic R commands for data exploration |
| Topic 4: Operationalizing an Analytics Project and Data Visualization | 10% | - Visualization best practices and tools - Guidelines for communicating findings - Deployment and integration of models |
| Topic 5: Big Data, Analytics, and the Data Scientist Role | 5% | - Role and required skills of a Data Scientist - Characteristics of Big Data - Business drivers for Big Data analytics |
| Topic 6: Advanced Analytics for Big Data - Technology and Tools | 22% | - Spark and distributed processing frameworks - Advanced SQL: window functions, aggregates, MADlib - In-database analytics and SQL fundamentals - Hadoop ecosystem and its components |
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