C2090-101: IBM Big Data Engineer

Full Name: IBM Big Data Engineer

Exam Code: C2090-101

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IBM Big Data Engineer Exam Summary:

Exam Name	IBM Certified Data Engineer - Big Data
Exam Code	C2090-101
Exam Price	$200 (USD)
Duration	75 mins
Number of Questions	53
Passing Score	65%
Books / Training	IBM Knowledge Center
Sample Questions	IBM Big Data Engineer Sample Questions
Practice Exam	IBM C2090-101 Certification Practice Exam

IBM C2090-101 Exam Syllabus Topics:

Topic (Weights)	Details
Data Loading (34%)	- 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
Data Security (8%)	- Keep data secure within PCI standards - Uses masking (e.g. Optim, Big SQL), and redaction to protect sensitive data
Architecture and Integration (17%)	- 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
Performance and Scalability (15%)	- 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
Data Preparation, Transformation, and Export (26%)	- 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