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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.
Who should take the C2090-101 exam
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
How much C2090-101 Exam Cost
The price of the C2090-101 exam is $200 USD.
What is the duration of the C2090-101 Exam
- Number of Questions: 53
- Format: Multiple choices, multiple answers
- Length of Examination: 75 minutes
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IBM C2090-101 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Performance and Scalability | 15% | - Optimize query execution and workload management - Apply best practices for latency and throughput - Ensure high availability and scalability - Tune Hive, HBase, and Big SQL performance |
| Data Loading | 34% | - Load unstructured data into InfoSphere BigInsights - Load log files with Flume and command-line tools - Create Big SQL tables and load data into Hadoop - Import streaming data using InfoSphere Streams - Extract data from relational sources using Sqoop - Ingest data into HBase and Hive |
| Data Preparation, Transformation, and Export | 26% | - Create BigSheets workbooks - Export results from Hadoop environment - Use Pig for data transformation - Perform entity resolution and data cleansing - Apply Jaql for data manipulation |
| 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 |
| Architecture and Integration | 17% | - Design cluster architecture and storage layers - Implement MapReduce and RDD concepts - Integrate GPFS and distributed file systems - Evaluate use cases for Hive, Big SQL, and HBase - Ensure consistent processing with InfoSphere Streams |
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