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IBM Big Data Engineer : C2090-101

C2090-101

Exam Code: C2090-101

Exam Name: IBM Big Data Engineer

Updated: Aug 17, 2026

Q & A: 108 Questions and Answers

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About IBM C2090-101 Exam

What is the duration of the C2090-101 Exam

  • Format: Multiple choices, multiple answers
  • Length of Examination: 75 minutes
  • Number of Questions: 53

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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.

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C2090-101 Exam Reference

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

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IBM C2090-101 Exam Syllabus Topics:

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