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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IBM Big Data Engineer Exam Certification Details:
| Exam Name | IBM Certified Data Engineer - Big Data |
| Exam Code | C2090-101 |
| Books / Training | IBM Knowledge Center |
| Number of Questions | 53 |
| Exam Price | $200 (USD) |
| Schedule Exam | Pearson VUE |
| Passing Score | 64% |
| Duration | 75 mins |
| Sample Questions | IBM Big Data Engineer Sample Questions |
Certification Path
The IBM Certified Specialist Certification certification includes only one exam IBM C2090-101 exam.
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% | - Optimize query execution and workload management - Ensure high availability and scalability - Tune Hive, HBase, and Big SQL performance - Apply best practices for latency and throughput |
| Topic 2: Data Preparation, Transformation, and Export | 26% | - Export results from Hadoop environment - Create BigSheets workbooks - Perform entity resolution and data cleansing - Apply Jaql for data manipulation - Use Pig for data transformation |
| Topic 3: Data Loading | 34% | - Ingest data into HBase and Hive - Load log files with Flume and command-line tools - Extract data from relational sources using Sqoop - Import streaming data using InfoSphere Streams - Create Big SQL tables and load data into Hadoop - Load unstructured data into InfoSphere BigInsights |
| Topic 4: Architecture and Integration | 17% | - Design cluster architecture and storage layers - Evaluate use cases for Hive, Big SQL, and HBase - Implement MapReduce and RDD concepts - Integrate GPFS and distributed file systems - Ensure consistent processing with InfoSphere Streams |
| Topic 5: Data Security | 8% | - Apply data masking and redaction techniques - Comply with security standards (PCI, PII protection) - Monitor and secure sensitive data - Implement access control and LDAP security |
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