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To prepare for the EMC DES-7DE1 certification exam, candidates should have a good understanding of the fundamentals of data science and data engineering. They should have experience with programming languages like Python, R, and SQL. Candidates should also have experience with data visualization tools like Tableau, Power BI, and QlikView. DES-7DE1 exam is a multiple-choice exam that consists of 60 questions that are to be answered within 90 minutes.

Reference: https://education.dellemc.com/content/dam/dell-emc/documents/en-us/DS_DataEng_SpecExamDesc_APR1020_Current.pdf

To take the EMC DES-7DE1 exam, candidates must have a strong background in data engineering and be familiar with the latest trends and technologies in the industry. They must also be able to work with data at scale and have experience with big data platforms such as Hadoop and Spark. DES-7DE1 exam is intended for individuals who are looking to advance their careers in data engineering or for those who are seeking to pursue a career in this field.

The DES-7DE1 exam focuses on the core concepts of data science and engineering, including data modeling, data visualization, data integration, and data governance. It also covers advanced topics such as machine learning, deep learning, natural language processing, and big data technologies. DES-7DE1 exam is designed to test the knowledge and skills of data scientists and data engineers in these areas.

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EMC DES-7DE1 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: The Role of the Data Engineer5%- Role in analytics and data science projects
- Skills and responsibilities of a data engineer
Topic 2: Data Warehousing with SQL and NoSQL17%- SQL querying and performance considerations
- NoSQL database types, features and use cases
- Relational databases, schemas and normalization
Topic 3: ETL/ELT with Hadoop and Spark18%- Apache Spark architecture and processing
- ETL vs ELT concepts and scheduling tools
- Hadoop ecosystem, HDFS and ingestion tools
Topic 4: Data Governance, Security and Privacy20%- Metadata management and MDM
- Big data security: Apache Atlas, Ranger, Knox
- Governance frameworks, roles and policies
- Data privacy regulations and ethics
Topic 5: Processing Streaming and IoT Data20%- Apache Kafka architecture and messaging
- Apache Storm and real-time processing
- Streaming data concepts and IoT use cases
Topic 6: Building Data Pipelines with Python20%- Python libraries for data processing
- Pipeline monitoring and error handling
- Designing and automating data pipelines

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