- Exam Code: C2090-101
- Exam Name: IBM Big Data Engineer
- Updated: Jul 30, 2026
- Q & A: 108 Questions and Answers
IBM C2090-101 Exam : IBM Big Data Engineer
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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.
IBM C2090-101 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Architecture and Integration | 17% | - Design cluster architecture and storage layers - Evaluate use cases for Hive, Big SQL, and HBase - Integrate GPFS and distributed file systems - Implement MapReduce and RDD concepts - Ensure consistent processing with InfoSphere Streams |
| Topic 2: Data Security | 8% | - Comply with security standards (PCI, PII protection) - Implement access control and LDAP security - Apply data masking and redaction techniques - Monitor and secure sensitive data |
| Topic 3: Data Loading | 34% | - Import streaming data using InfoSphere Streams - Load log files with Flume and command-line tools - Load unstructured data into InfoSphere BigInsights - Extract data from relational sources using Sqoop - Ingest data into HBase and Hive - Create Big SQL tables and load data into Hadoop |
| Topic 4: Data Preparation, Transformation, and Export | 26% | - Apply Jaql for data manipulation - Create BigSheets workbooks - Export results from Hadoop environment - Use Pig for data transformation - Perform entity resolution and data cleansing |
| Topic 5: Performance and Scalability | 15% | - Apply best practices for latency and throughput - Ensure high availability and scalability - Tune Hive, HBase, and Big SQL performance - Optimize query execution and workload management |
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