Exam Name: IBM Big Data Engineer
Certification Provider: IBM
Corresponding Certification: IBM Analytics: Platform Analytics
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IBM C2090-101 Exam topics
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:
- Data Loading: 34%
- Data Preparation, Transformation, and Export: 26%
- Performance and Scalability: 15%
- Data Security: 8%
- Architecture and Integration: 17%
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Reference: https://www.ibm.com/certify/exam?id=C2090-101
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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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The IBM Certified Specialist Certification certification includes only one exam IBM C2090-101 exam.
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IBM C2090-101 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Performance and Scalability | 15% | - Ensure high availability and scalability - Tune Hive, HBase, and Big SQL performance - Apply best practices for latency and throughput - Optimize query execution and workload management |
| Data Security | 8% | - Monitor and secure sensitive data - Implement access control and LDAP security - Apply data masking and redaction techniques - Comply with security standards (PCI, PII protection) |
| Data Preparation, Transformation, and Export | 26% | - Use Pig for data transformation - Perform entity resolution and data cleansing - Create BigSheets workbooks - Apply Jaql for data manipulation - Export results from Hadoop environment |
| Data Loading | 34% | - Import streaming data using InfoSphere Streams - Create Big SQL tables and load data into Hadoop - Ingest data into HBase and Hive - Load unstructured data into InfoSphere BigInsights - Load log files with Flume and command-line tools - Extract data from relational sources using Sqoop |
| Architecture and Integration | 17% | - Implement MapReduce and RDD concepts - Integrate GPFS and distributed file systems - Ensure consistent processing with InfoSphere Streams - Evaluate use cases for Hive, Big SQL, and HBase - Design cluster architecture and storage layers |
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