IBM C2090-101 Q&A - in .pdf

  • C2090-101 pdf
  • Exam Code: C2090-101
  • Exam Name: IBM Big Data Engineer
  • Updated: Sep 11, 2026
  • Q & A: 108 Questions and Answers
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  • Exam Code: C2090-101
  • Exam Name: IBM Big Data Engineer
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  • Updated: Sep 11, 2026
  • Q & A: 108 Questions and Answers
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  • Exam Code: C2090-101
  • Exam Name: IBM Big Data Engineer
  • Updated: Sep 11, 2026
  • Q & A: 108 Questions and Answers
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Reference: https://www.ibm.com/certify/exam?id=C2090-101

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  • Performance and Scalability: 15%
  • Data Loading: 34%
  • Data Security: 8%
  • Data Preparation, Transformation, and Export: 26%
  • Architecture and Integration: 17%
IBM C2090-101 Exam Syllabus Topics:
SectionObjectives
Topic 1: Big Data Foundations- Data storage systems
  • 1. HDFS concepts and architecture
    • 2. NoSQL databases overview
      - Big data concepts and architecture
      • 1. Characteristics of big data (volume, velocity, variety)
        • 2. Distributed computing fundamentals
          Topic 2: Data Processing and Analytics- Batch and stream processing
          • 1. MapReduce concepts
            • 2. Real-time data processing fundamentals
              - Spark ecosystem
              • 1. Spark Core and RDDs
                • 2. Spark SQL and DataFrames
                  Topic 3: Big Data Ecosystem Tools- Hadoop ecosystem
                  • 1. YARN resource management
                    • 2. Hive and Pig basics
                      - Data storage and query tools
                      • 1. Data querying strategies
                        • 2. HBase overview
                          Topic 4: Data Ingestion and Integration- Data ingestion tools
                          • 1. ETL concepts
                            • 2. Streaming ingestion pipelines
                              Topic 5: Data Governance and Security- Security in big data systems
                              • 1. Data privacy considerations
                                • 2. Authentication and authorization basics
                                  - Data quality and management
                                  • 1. Data cleansing principles
                                    • 2. Metadata management concepts
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