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IBM C2090-101 exam : IBM Big Data Engineer

C2090-101 Exam Simulator
  • Exam Code: C2090-101
  • Exam Name: IBM Big Data Engineer
  • Updated: Jul 16, 2026
  • Q & A: 108 Questions and Answers
  • IBM C2090-101 Q&A - in .pdf

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About IBM C2090-101 Exam Simulator

IBM Big Data Engineer C2090-101 Exam

IBM Big Data Engineer C2090-101 Exam is requisite to obtain IBM Analytics Platform Certification. This IBM C2090-101 Exam validates and measures the candidate knowledge in extract data from relational source using Sqoop, Utilize Apache Hue for search Visualization, Export Processing Results out of Hadoop and utilize Consistent Regions in infosphere Streams. Big Data Engineers and Data Security Consultants usually hold or pursue this certification and you can expect the same job roles after completion of IBM Analytics Platform certification.

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

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IBM C2090-101 Exam Syllabus Topics:

SectionObjectives
Big Data Ecosystem Tools- Data storage and query tools
  • 1. Data querying strategies
    • 2. HBase overview
      - Hadoop ecosystem
      • 1. YARN resource management
        • 2. Hive and Pig basics
          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
                  Data Ingestion and Integration- Data ingestion tools
                  • 1. ETL concepts
                    • 2. Streaming ingestion pipelines
                      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
                              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

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