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- Exam Code: C2090-101
- Exam Name: IBM Big Data Engineer
- Q & A: 108 Questions and Answers
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IBM C2090-101 dumps - Testing Engine
- Exam Code: C2090-101
- Exam Name: IBM Big Data Engineer
- Q & A: 108 Questions and Answers
- Software Price: $59.99
- Testing Engine
About IBM Big Data Engineer : C2090-101 Exam Braindumps
IBM C2090-101: IBM Big Data Engineer Exam is a challenging exam that requires a lot of preparation and practice. It is important for candidates to have a deep understanding of the technologies used in Big Data Engineering and to be familiar with the latest trends and developments in the field. Candidates should also have hands-on experience working with Big Data technologies and be able to apply their knowledge to real-world scenarios.
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: Data Security | 8% | - Monitor and secure sensitive data - Apply data masking and redaction techniques - Comply with security standards (PCI, PII protection) - Implement access control and LDAP security |
| Topic 2: Data Preparation, Transformation, and Export | 26% | - Create BigSheets workbooks - Export results from Hadoop environment - Use Pig for data transformation - Apply Jaql for data manipulation - Perform entity resolution and data cleansing |
| Topic 3: Data Loading | 34% | - Load log files with Flume and command-line tools - Load unstructured data into InfoSphere BigInsights - Import streaming data using InfoSphere Streams - Extract data from relational sources using Sqoop - Create Big SQL tables and load data into Hadoop - Ingest data into HBase and Hive |
| Topic 4: Architecture and Integration | 17% | - Design cluster architecture and storage layers - Evaluate use cases for Hive, Big SQL, and HBase - Integrate GPFS and distributed file systems - Ensure consistent processing with InfoSphere Streams - Implement MapReduce and RDD concepts |
| Topic 5: Performance and Scalability | 15% | - Tune Hive, HBase, and Big SQL performance - Apply best practices for latency and throughput - Ensure high availability and scalability - Optimize query execution and workload management |
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