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IBM C2090-101 certification is recognized globally as a valuable credential for big data engineers. It demonstrates that the certified professional has the knowledge and skills required to design and implement big data solutions using IBM technologies. IBM Big Data Engineer certification also enhances the career prospects of the certified professional by providing them with a competitive edge in the job market and opening up new career opportunities in the field of big data engineering.
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IBM C2090-101 exam is an advanced-level certification exam designed for professionals who want to become IBM Big Data Engineers. C2090-101 exam is designed to test the candidate's knowledge and skills in various areas of big data engineering, including data processing, data integration, data warehousing, and data analytics. Candidates who pass C2090-101 exam will be qualified to work as IBM Big Data Engineers and will be able to demonstrate their skills and knowledge to potential employers.
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The IBM C2090-101 exam consists of 60 multiple-choice questions and candidates are given 90 minutes to complete it. The questions are designed to test the candidate's understanding of various IBM technologies, including IBM InfoSphere BigInsights, IBM InfoSphere Streams, IBM InfoSphere DataStage, and IBM Cloud Pak for Data. To succeed in C2090-101 exam, candidates should have a strong understanding of big data concepts and technologies, as well as experience working with IBM technologies.
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: Architecture and Integration | 17% | - Design cluster architecture and storage layers - Integrate GPFS and distributed file systems - Evaluate use cases for Hive, Big SQL, and HBase - Ensure consistent processing with InfoSphere Streams - Implement MapReduce and RDD concepts |
| Topic 2: Data Security | 8% | - Implement access control and LDAP security - Apply data masking and redaction techniques - Monitor and secure sensitive data - Comply with security standards (PCI, PII protection) |
| Topic 3: Data Loading | 34% | - Load log files with Flume and command-line tools - Load unstructured data into InfoSphere BigInsights - Ingest data into HBase and Hive - Import streaming data using InfoSphere Streams - Create Big SQL tables and load data into Hadoop - Extract data from relational sources using Sqoop |
| Topic 4: Data Preparation, Transformation, and Export | 26% | - Apply Jaql for data manipulation - Use Pig for data transformation - Export results from Hadoop environment - Perform entity resolution and data cleansing - Create BigSheets workbooks |
| Topic 5: Performance and Scalability | 15% | - Apply best practices for latency and throughput - Tune Hive, HBase, and Big SQL performance - Optimize query execution and workload management - Ensure high availability and scalability |
