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IBM Big Data Engineer : C2090-101

C2090-101

Exam Code: C2090-101

Exam Name: IBM Big Data Engineer

Updated: Jul 21, 2026

Q & A: 108 Questions and Answers

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About IBM Big Data Engineer certification

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The IBM Big Data Engineer C2090-101 Exam certification is an internationally-recognized validation that identifies persons who earn it as possessing skilled in IBM Analytics Platform Certification. If a candidate wants significant improvement in career growth needs enhanced knowledge, skills, and talents. The IBM Big Data Engineer C2090-101 Exam certification provides proof of this advanced knowledge and skill. If a candidate has knowledge of associated technologies and skills that are required to pass IBM Big Data Engineer C2090-101 Exam then he should take this exam.

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

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IBM C2090-101 Exam Syllabus Topics:

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

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