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, PrepPDF 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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- Exam Code: C2090-101
- Exam Name: IBM Big Data Engineer
- Certification Provider: IBM
- Corresponding Certification: IBM Analytics: Platform Analytics
- Updated: Aug 07, 2026
- No. of Questions: 108 Questions & Answers with Testing Engine
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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.
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Certification Path
The IBM Certified Specialist Certification certification includes only one exam IBM C2090-101 exam.
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Reference: https://www.ibm.com/certify/exam?id=C2090-101
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IBM C2090-101 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Performance and Scalability | 15% | - Ensure high availability and scalability - Tune Hive, HBase, and Big SQL performance - Optimize query execution and workload management - Apply best practices for latency and throughput |
| Topic 2: Architecture and Integration | 17% | - Design cluster architecture and storage layers - Evaluate use cases for Hive, Big SQL, and HBase - Ensure consistent processing with InfoSphere Streams - Integrate GPFS and distributed file systems - Implement MapReduce and RDD concepts |
| Topic 3: Data Security | 8% | - Implement access control and LDAP security - Monitor and secure sensitive data - Comply with security standards (PCI, PII protection) - Apply data masking and redaction techniques |
| Topic 4: Data Preparation, Transformation, and Export | 26% | - Use Pig for data transformation - Export results from Hadoop environment - Create BigSheets workbooks - Perform entity resolution and data cleansing - Apply Jaql for data manipulation |
| Topic 5: Data Loading | 34% | - Load unstructured data into InfoSphere BigInsights - Ingest data into HBase and Hive - Create Big SQL tables and load data into Hadoop - Import streaming data using InfoSphere Streams - Extract data from relational sources using Sqoop - Load log files with Flume and command-line tools |
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