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Exam Number : C2090-101
Exam Name : IBM Big Data Engineer
Vendor Name : IBM
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C2090-101 test Format | C2090-101 Course Contents | C2090-101 Course Outline | C2090-101 test Syllabus | C2090-101 test Objectives
Exam Title :
IBM Certified Data Engineer - Big Data
Exam ID :
C2090-101
Exam Duration :
75 mins
Questions in test
:
53
Passing Score :
65%
Official Training :
IBM Knowledge Center
Exam Center :
Pearson VUE
Real Questions :
IBM Big Data Engineer Real Questions
VCE Practice Test :
IBM C2090-101 Certification VCE Practice Test
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%
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%
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 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%
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