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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.
How to study the C2090-101 Exam
PassCollection expert team recommends you to prepare some notes on these topics along with it don't forget to practice IBM C2090-101 Exam exam dumps which been written by our expert team, Both these will help you a lot to clear this exam with good marks.
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% | - Ensure high availability and scalability - Optimize query execution and workload management - Apply best practices for latency and throughput - Tune Hive, HBase, and Big SQL performance |
| Topic 2: Data Preparation, Transformation, and Export | 26% | - Apply Jaql for data manipulation - Perform entity resolution and data cleansing - Use Pig for data transformation - Export results from Hadoop environment - Create BigSheets workbooks |
| Topic 3: Data Security | 8% | - Monitor and secure sensitive data - Apply data masking and redaction techniques - Implement access control and LDAP security - Comply with security standards (PCI, PII protection) |
| Topic 4: Architecture and Integration | 17% | - Ensure consistent processing with InfoSphere Streams - Implement MapReduce and RDD concepts - Design cluster architecture and storage layers - Integrate GPFS and distributed file systems - Evaluate use cases for Hive, Big SQL, and HBase |
| Topic 5: Data Loading | 34% | - Create Big SQL tables and load data into Hadoop - Import streaming data using InfoSphere Streams - Load unstructured data into InfoSphere BigInsights - Ingest data into HBase and Hive - Load log files with Flume and command-line tools - Extract data from relational sources using Sqoop |
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