IBM C2090-101 Q&A - in .pdf

  • Exam Code: C2090-101
  • Exam Name: IBM Big Data Engineer
  • Q & A: 108 Questions and Answers
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  • Exam Code: C2090-101
  • Exam Name: IBM Big Data Engineer
  • Q & A: 108 Questions and Answers
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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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C2090-101 Exam Reference

IBM C2090-101 Exam Syllabus Topics:

SectionObjectives
Big Data Foundations- Data storage systems
  • 1. HDFS concepts and architecture
    • 2. NoSQL databases overview
      - Big data concepts and architecture
      • 1. Distributed computing fundamentals
        • 2. Characteristics of big data (volume, velocity, variety)
          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
                  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. Authentication and authorization basics
                                • 2. Data privacy considerations
                                  - Data quality and management
                                  • 1. Data cleansing principles
                                    • 2. Metadata management concepts

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