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.
The benefit in Obtaining the C2090-101 Exam Certification
- IBM Analytics Platform Certification provides practical experience to candidates from all the aspects to be a proficient worker in the organization.
- IBM Analytics Platform Certification has the knowledge to use the tools to complete the task efficiently and cost effectively than the other non-certified professionals lack in doing so.
- IBM Analytics Platform Certification has more useful and relevant networks that help them in setting career goals for themselves. IBM Certified Operational Decision Manager networks provide them with the right career direction than non certified usually are unable to get.
- IBM Analytics Platform Certification provide opportunities to get a job easily in which they are interested in instead of wasting years and ending without getting any experience.
- IBM Analytics Platform Certification will be confident and stand different from others as their skills are more trained than non-certified professionals.
- IBM Analytics Platform Certification is distinguished among competitors. IBM Analytics Platform Certification can give them an edge at that time easily when candidates appear for a job interview employers seek to notify something which differentiates the individual to another.
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Reference: https://www.ibm.com/certify/exam?id=C2090-101
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IBM C2090-101 Exam Syllabus Topics:
| Topic | Details | Weights |
|---|---|---|
| Data Preparation, Transformation, and Export | -Use Jaql query methods to transform data in InfoSphere BigInsights -Capture and prep social data for analytics -Integrating SPSS model scoring in InfoSphere Streams -Implement entity resolution within a Big Data platform (e.g. Big Match) -Utilize Pig for data transformation and data manipulation -Use Big SQL to transform data in InfoSphere BigInsights -Export processing results out of Hadoop (e.g. DataClick, DataStage, etc.) -Utilize consistent regions in InfoSphere Streams to ensure at least once processing | 26% |
| Data Loading | -Load unstructured data into InfoSphere BigInsights -Import streaming data into Hadoop using InfoSphere Streams -Create a BigSheets workbook -Import data into Hadoop and create Big SQL table definitions -Import data to HBase -Import data to Hive -Use Data Click to load from relational sources into InfoSphere BigInsights with a self-service process -Extract data from a relational source using Sqoop -Load log data into Hadoop using Flume -Insert data via IBM General Parallel File System (GPFS) Posix file system API -Load data with Hadoop command line utility | 34% |
| Performance and Scalability | -Use Resilient Distributed Dataset (RDD) to improve MapReduce performance -Choose file formats to optimize performance of Big SQL, JAQL, etc. -Make specific performance tuning decisions for Hive and HBase -Analyze performance considerations when using Apache Spark | 15% |
| Data Security | -Keep data secure within PCI standards -Uses masking (e.g. Optim, Big SQL), and redaction to protect sensitive data | 8% |
| Architecture and Integration | -Implement MapReduce -Evaluate use cases for selecting Hive, Big SQL, or HBase -Create and/or query a Solr index -Evaluate use cases for selecting potential file formats (e.g. JSON, CSV, Parquet, Sequence, etc..) -Utilize Apache Hue for search visualization | 17% |
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