IBM C2090-101 dumps - in .pdf

C2090-101 pdf
  • Exam Code: C2090-101
  • Exam Name: IBM Big Data Engineer
  • Updated: May 31, 2026
  • Q & A: 108 Questions and Answers
  • PDF Price: $59.99
  • Free Demo

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  • Exam Code: C2090-101
  • Exam Name: IBM Big Data Engineer
  • Updated: May 31, 2026
  • Q & A: 108 Questions and Answers
  • PDF Version + PC Test Engine + Online Test Engine
  • Value Pack Total: $119.98  $79.99
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IBM C2090-101 dumps - Testing Engine

C2090-101 Testing Engine
  • Exam Code: C2090-101
  • Exam Name: IBM Big Data Engineer
  • Updated: May 31, 2026
  • Q & A: 108 Questions and Answers
  • Software Price: $59.99
  • Testing Engine

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What is the duration of the C2090-101 Exam

  • Number of Questions: 53
  • Length of Examination: 75 minutes
  • Format: Multiple choices, multiple answers

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

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 Security-Keep data secure within PCI standards
-Uses masking (e.g. Optim, Big SQL), and redaction to protect sensitive data
8%
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%
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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