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Real GAQM CBDFS-001 practice exam questions for easy pass!

Last Updated: Sep 13, 2026

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GAQM CBDFS-001 Exam Syllabus Topics:

SectionObjectives
Topic 1: Big Data Storage Technologies- Distributed storage systems
  • 1. Data replication and fault tolerance
    • 2. HDFS fundamentals
      - NoSQL databases
      • 1. Document, column-family, and graph databases
        • 2. Key-value stores
          Topic 2: Data Management and Governance- Data integration
          • 1. ETL processes
            • 2. Data pipelines and ingestion methods
              - Data governance and quality
              • 1. Metadata and data cataloging
                • 2. Data quality management
                  Topic 3: Big Data Analytics and Applications- Analytical techniques
                  • 1. Basic machine learning concepts
                    • 2. Descriptive and predictive analytics
                      - Use cases
                      • 1. Business intelligence applications
                        • 2. Real-time analytics use cases
                          Topic 4: Security and Cloud in Big Data- Cloud platforms
                          • 1. Scalability and distributed computing in cloud
                            • 2. Cloud-based big data services overview
                              - Data security
                              • 1. Data privacy considerations
                                • 2. Access control and encryption basics
                                  Topic 5: 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
                                          Topic 6: Big Data Processing Frameworks- Batch processing
                                          • 1. MapReduce concepts
                                            • 2. Distributed computation models
                                              - In-memory and stream processing
                                              • 1. Stream processing concepts (e.g., Kafka overview)
                                                • 2. Apache Spark basics
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