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: Jul 19, 2026

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IBM C2090-101 exam is a multiple-choice exam that consists of 59 questions. Candidates have 90 minutes to complete the exam and must score at least 62% to pass. C2090-101 exam is available in English and Japanese and can be taken at an authorized testing center or online. Candidates who pass the exam will receive an IBM Certified Big Data Engineer certification, which is recognized by employers around the world as a mark of expertise in big data technology.

IBM C2090-101 certification is ideal for professionals who are looking to build a career in big data and analytics, particularly in IBM-specific tools and technologies. C2090-101 exam tests the candidate’s ability to manage data, design big data solutions, develop data analytics applications, and optimize big data workflows.

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IBM C2090-101: IBM Big Data Engineer Exam is a comprehensive exam that covers all aspects of Big Data Engineering. C2090-101 exam covers topics such as Big Data Architecture, Data Ingestion, Data Transformation, Data Storage, Data Analysis, and Data Visualization. C2090-101 exam is designed to test the candidate's knowledge and skills in all these areas and ensure that the candidate has the necessary expertise to work in the field of Big Data Engineering.

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

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IBM C2090-101 Exam Syllabus Topics:

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

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