IBM Big Data Engineer : C2090-101

C2090-101 real exams

Exam Code: C2090-101

Exam Name: IBM Big Data Engineer

Updated: Aug 12, 2026

Q & A: 108 Questions and Answers

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

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What is the duration of the C2090-101 Exam

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

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.

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.

IBM C2090-101 Exam Syllabus Topics:

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