IBM Big Data Engineer: C2090-101 Exam

"IBM Big Data Engineer", also known as C2090-101 exam, is a IBM Certification. With the complete collection of questions and answers, iPassleader has assembled to take you through 108 Q&As to your C2090-101 Exam preparation. In the C2090-101 exam resources, you will cover every field and category in IBM Analytics: Platform Analytics Certification helping to ready you for your successful IBM Certification.

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  • Updated on: Aug 07, 2026
  • No. of Questions: 108 Questions & Answers

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  • Updated on: Aug 07, 2026
  • No. of Questions: 108 Questions & Answers

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C2090-101 Exam Reference

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

Who should take the C2090-101 exam

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.

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IBM C2090-101 Exam Syllabus Topics:

SectionWeightObjectives
Data Collection and Ingestion14%- Data acquisition technologies
  • 1. Sqoop
  • 2. Kafka
  • 3. Flume
Data Governance, Security and Administration22%- Operational management
  • 1. Monitoring and troubleshooting
  • 2. Security concepts
  • 3. Data governance
  • 4. Performance tuning
Hadoop Distributed Processing28%- Hadoop ecosystem
  • 1. Cluster management
  • 2. MapReduce
  • 3. YARN
  • 4. HDFS architecture
Big Data Foundations12%- Big Data concepts
  • 1. Big Data architecture fundamentals
  • 2. Volume, Velocity, Variety, Veracity
  • 3. Big Data use cases
Data Transformation and Analytics24%- Data processing frameworks
  • 1. Apache Spark fundamentals
  • 2. Apache Hive
  • 3. Data querying and transformation
  • 4. Apache Pig

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