Exam Name: IBM Big Data Engineer
Certification Provider: IBM
Corresponding Certification: IBM Analytics: Platform Analytics
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Check Updated on: Sep 15, 2026
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IBM C2090-101 certification exam is designed to test the skills of a Big Data Engineer. IBM Big Data Engineer certification validates the expertise of the candidate in designing, building, and maintaining big data solutions using IBM technologies. C2090-101 exam is intended for professionals who have experience working with big data and are looking to take their skills to the next level.
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To be eligible for the IBM C2090-101 exam, candidates must have a strong foundation in database concepts and programming languages such as SQL, Python, and Java. They must also have experience working with big data technologies and tools such as Hadoop, Spark, and Kafka. As the demand for big data engineers continues to grow, the IBM C2090-101 certification serves as a valuable credential for individuals seeking to advance their careers in this field.
The IBM Big Data Engineer certification exam consists of 60 multiple-choice questions that must be answered within 90 minutes. C2090-101 exam is available in English and Japanese language, and the passing score is 70%. C2090-101 exam fee is $200, and candidates can register for the exam through Pearson VUE, a global leader in computer-based testing.
IBM C2090-101 exam consists of 60 multiple-choice questions that must be completed within 90 minutes. C2090-101 exam is available in English and Japanese languages. Candidates must achieve a minimum score of 64% to pass the exam. C2090-101 exam can be taken online from any location with a stable internet connection.
Reference: https://www.ibm.com/certify/exam?id=C2090-101
IBM C2090-101 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Big Data Foundations | 12% | - Big Data concepts
|
| Topic 2: Data Governance, Security and Administration | 22% | - Operational management
|
| Topic 3: Data Transformation and Analytics | 24% | - Data processing frameworks
|
| Topic 4: Data Collection and Ingestion | 14% | - Data acquisition technologies
|
| Topic 5: Hadoop Distributed Processing | 28% | - Hadoop ecosystem
|
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