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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.
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Candidates must know the exam topics before they start of preparation. Because it will really help them in hitting the core. Our C2090-101 exam dumps will include the following 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% |
| Data Security | -Keep data secure within PCI standards -Uses masking (e.g. Optim, Big SQL), and redaction to protect sensitive data | 8% |
| 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% |
| 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% |
Reference: https://www.ibm.com/certify/exam?id=C2090-101
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