| Exam Price | $200 (USD) |
| Exam Code | C2090-101 |
| Exam Name | IBM Certified Data Engineer - Big Data |
| Duration | 75 mins |
| Number of Questions | 53 |
| Sample Questions | IBM Big Data Engineer Sample Questions |
| Schedule Exam | Pearson VUE |
| Passing Score | 64% |
| Books / Training | IBM Knowledge Center |
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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.
Reference: https://www.ibm.com/certify/exam?id=C2090-101
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| Section | Weight | Objectives |
|---|---|---|
| Data Preparation, Transformation, and Export | 26% | - Apply Jaql for data manipulation - Export results from Hadoop environment - Use Pig for data transformation - Create BigSheets workbooks - Perform entity resolution and data cleansing |
| Performance and Scalability | 15% | - Ensure high availability and scalability - Apply best practices for latency and throughput - Optimize query execution and workload management - Tune Hive, HBase, and Big SQL performance |
| Data Security | 8% | - Monitor and secure sensitive data - Implement access control and LDAP security - Comply with security standards (PCI, PII protection) - Apply data masking and redaction techniques |
| Data Loading | 34% | - Import streaming data using InfoSphere Streams - Load unstructured data into InfoSphere BigInsights - Create Big SQL tables and load data into Hadoop - Load log files with Flume and command-line tools - Ingest data into HBase and Hive - Extract data from relational sources using Sqoop |
| Architecture and Integration | 17% | - Implement MapReduce and RDD concepts - Design cluster architecture and storage layers - Integrate GPFS and distributed file systems - Ensure consistent processing with InfoSphere Streams - Evaluate use cases for Hive, Big SQL, and HBase |
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