IBM C2090-101 Q&A - in .pdf

  • Exam Code: C2090-101
  • Exam Name: IBM Big Data Engineer
  • Updated: Aug 16, 2026
  • Q & A: 108 Questions and Answers
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IBM C2090-101 Q&A - Testing Engine

  • Exam Code: C2090-101
  • Exam Name: IBM Big Data Engineer
  • Updated: Aug 16, 2026
  • Q & A: 108 Questions and Answers
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About IBM Big Data Engineer - C2090-101 Exam Actual Tests

IBM C2090-101 exam measures the candidate's ability to design, develop, and maintain big data solutions using IBM technologies such as IBM InfoSphere BigInsights, IBM InfoSphere Streams, and IBM InfoSphere DataStage. C2090-101 exam is divided into multiple sections that cover various topics related to big data engineering, including data ingestion, data transformation, data storage, data analysis, and data visualization.

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Reference: https://www.ibm.com/certify/exam?id=C2090-101

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IBM C2090-101 Exam Syllabus Topics:

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

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