Real IBM C2090-101 practice exam questions for easy pass!
Updated: Oct 16, 2026
No. of Questions: 108 Questions & Answers with Testing Engine
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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.
Candidates must know the exam topics before they start of preparation. Because it will really help them in hitting the core. Our C2090-101 exam dumps will include the following topics:
Reference: https://www.ibm.com/certify/exam?id=C2090-101
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| Certification Vendor: | IBM |
|---|---|
| Exam Name: | IBM Big Data Engineer |
| Exam Number: | C2090-101 |
| Available Languages: | English |
| Passing Score: | 65% |
| Exam Format: | Single/multiple answer, Multiple-choice questions |
| Related Certifications: | IBM Analytics: Platform Analytics |
| Exam Duration: | 75 minutes |
| Real Exam Qty: | 53 |
| Exam Price: | 200 USD |
| Certificate Validity Period: | 3 years |
| Recommended Training: | IBM BigInsights Foundations Hadoop and Big Data Essentials |
| Exam Registration: | IBM Certification Portal Pearson VUE Registration |
| Sample Questions: | IBM C2090-101 Sample Questions |
| Exam Way: | Proctored online or onsite at Pearson VUE test centers |
| Pre Condition: | No formal prerequisites; recommended 1-2 years of hands-on experience with Hadoop, IBM BigInsights, Hive, Pig, HBase, and SQL |
| Official Syllabus URL: | https://www.ibm.com/certify/exams/C2090-101 |
The IBM Certified Specialist Certification certification includes only one exam IBM C2090-101 exam.
| Section | Weight | Objectives |
|---|---|---|
| Architecture and Integration | 17% | - Implement MapReduce and RDD concepts - Design cluster architecture and storage layers - Ensure consistent processing with InfoSphere Streams - Integrate GPFS and distributed file systems - Evaluate use cases for Hive, Big SQL, and HBase |
| Data Loading | 34% | - Create Big SQL tables and load data into Hadoop - Extract data from relational sources using Sqoop - Load unstructured data into InfoSphere BigInsights - Load log files with Flume and command-line tools - Import streaming data using InfoSphere Streams - Ingest data into HBase and Hive |
| Performance and Scalability | 15% | - Tune Hive, HBase, and Big SQL performance - Optimize query execution and workload management - Apply best practices for latency and throughput - Ensure high availability and scalability |
| Data Security | 8% | - Monitor and secure sensitive data - Apply data masking and redaction techniques - Comply with security standards (PCI, PII protection) - Implement access control and LDAP security |
| Data Preparation, Transformation, and Export | 26% | - Perform entity resolution and data cleansing - Apply Jaql for data manipulation - Use Pig for data transformation - Export results from Hadoop environment - Create BigSheets workbooks |
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