IBM C2090-101 - IBM Big Data Engineer

IBM C2090-101 Actual PDF
  • Exam Code: C2090-101
  • Exam Name: IBM Big Data Engineer
  • Updated: Aug 26, 2026
  • Q & A: 108 Questions and Answers
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About IBM C2090-101 Exam

How to book the C2090-101 Exam

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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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Reference: https://www.ibm.com/certify/exam?id=C2090-101

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IBM C2090-101 Exam Syllabus Topics:

TopicDetailsWeights
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%
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 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%
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%

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