About IBM Big Data Engineer exam torrent
IBM C2090-101 exam is designed to challenge and evaluate the abilities of experienced big data professionals. IBM recommends that candidates have at least two years of hands-on experience working with big data, including knowledge of cloud-based big data environments, before attempting the exam. Candidates who pass the exam will be recognized as experts in the field of big data and will be eligible to work on IBM Cognos Analytics projects.
IBM C2090-101 exam measures the candidate's ability to design, develop, and maintain big data solutions using IBM technologies such as IBM InfoSphere BigInsights, IBM InfoSphere Streams, and IBM InfoSphere DataStage. C2090-101 exam is divided into multiple sections that cover various topics related to big data engineering, including data ingestion, data transformation, data storage, data analysis, and data visualization.
As the demand for big data engineers is rapidly increasing, IBM C2090-101 certification has become one of the most sought-after certifications in the industry. IBM Big Data Engineer certification not only validates a candidate's understanding of big data concepts but also their ability to design and implement big data solutions using IBM technologies such as InfoSphere BigInsights, IBM Watson Studio, and IBM Cloud Pak for Data.
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Reference: https://www.ibm.com/certify/exam?id=C2090-101
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IBM C2090-101 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Performance and Scalability | 15% | - Tune Hive, HBase, and Big SQL performance - Optimize query execution and workload management - Ensure high availability and scalability - Apply best practices for latency and throughput |
| Data Security | 8% | - Apply data masking and redaction techniques - Implement access control and LDAP security - Monitor and secure sensitive data - Comply with security standards (PCI, PII protection) |
| Data Loading | 34% | - 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 - Load log files with Flume and command-line tools - Load unstructured data into InfoSphere BigInsights |
| Architecture and Integration | 17% | - Implement MapReduce and RDD concepts - 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 |
| Data Preparation, Transformation, and Export | 26% | - Export results from Hadoop environment - Perform entity resolution and data cleansing - Create BigSheets workbooks - Use Pig for data transformation - Apply Jaql for data manipulation |
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