C2090-101 exam dumps

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  • Exam Code: C2090-101
  • Exam Name: IBM Big Data Engineer
  • No. of Questions: 108 Questions and Answers
  • Updated: Sep 11, 2026

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

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