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GAQM CBDFS-001 Braindumps - in .pdf Free Demo

  • Exam Code: CBDFS-001
  • Exam Name: Certified Big Data Foundation Specialist (CBDFS)
  • Last Updated: Aug 09, 2026
  • Q & A: 0 Questions and Answers
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  • Exam Code: CBDFS-001
  • Exam Name: Certified Big Data Foundation Specialist (CBDFS)
  • Last Updated: Aug 09, 2026
  • Q & A: 0 Questions and Answers
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GAQM CBDFS-001 Exam Syllabus Topics:

SectionObjectives
Data Management and Governance- Data governance and quality
  • 1. Metadata and data cataloging
    • 2. Data quality management
      - Data integration
      • 1. ETL processes
        • 2. Data pipelines and ingestion methods
          Big Data Processing Frameworks- Batch processing
          • 1. Distributed computation models
            • 2. MapReduce concepts
              - In-memory and stream processing
              • 1. Stream processing concepts (e.g., Kafka overview)
                • 2. Apache Spark basics
                  Security and Cloud in Big Data- Cloud platforms
                  • 1. Cloud-based big data services overview
                    • 2. Scalability and distributed computing in cloud
                      - Data security
                      • 1. Data privacy considerations
                        • 2. Access control and encryption basics
                          Big Data Fundamentals- Introduction to Big Data concepts
                          • 1. Traditional data vs Big Data systems
                            • 2. Characteristics of Big Data (Volume, Velocity, Variety, Veracity)
                              - Big Data architecture overview
                              • 1. Batch vs streaming processing
                                • 2. Data pipelines and data lifecycle
                                  Big Data Storage Technologies- Distributed storage systems
                                  • 1. HDFS fundamentals
                                    • 2. Data replication and fault tolerance
                                      - NoSQL databases
                                      • 1. Key-value stores
                                        • 2. Document, column-family, and graph databases
                                          Big Data Analytics and Applications- Analytical techniques
                                          • 1. Basic machine learning concepts
                                            • 2. Descriptive and predictive analytics
                                              - Use cases
                                              • 1. Real-time analytics use cases
                                                • 2. Business intelligence applications

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