IBM C2090-101 exam dumps : IBM Big Data Engineer

  • Exam Code: C2090-101
  • Exam Name: IBM Big Data Engineer
  • Updated: May 28, 2026     Q & A: 108 Questions and Answers

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IBM C2090-101 Exam Syllabus Topics:

TopicDetails
Topic 1
  • Evaluate use cases for selecting potential file formats
  • Use Resilient Distributed Dataset (RDD) to improve MapReduce performance
Topic 2
  • Utilize Apache Hue for search visualization
  • Extract data from a relational source using Sqoop
Topic 3
  • Analyze performance considerations when using Apache Spark
  • Keep data secure within PCI standards
Topic 4
  • Implement entity resolution within a Big Data platform
  • Load unstructured data into InfoSphere BigInsights
Topic 5
  • Make specific performance tuning decisions for Hive and HBase
  • Import streaming data into Hadoop using InfoSphere Streams
Topic 6
  • Insert data via IBM General Parallel File System (GPFS) Posix file system API
  • Use Jaql query methods to transform data in InfoSphere BigInsights
Topic 7
  • Evaluate use cases for selecting Hive, Big SQL, or HBase
  • Create a BigSheets workbook
Topic 8
  • Utilize consistent regions in InfoSphere Streams to ensure at least once processing
  • Load log data into Hadoop using Flume
Topic 9
  • Import data into Hadoop and create Big SQL table definitions
  • Utilize Pig for data transformation and data manipulation

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

To be eligible for the IBM C2090-101 exam, candidates must have a strong foundation in database concepts and programming languages such as SQL, Python, and Java. They must also have experience working with big data technologies and tools such as Hadoop, Spark, and Kafka. As the demand for big data engineers continues to grow, the IBM C2090-101 certification serves as a valuable credential for individuals seeking to advance their careers in this field.

IBM C2090-101: IBM Big Data Engineer Exam is a challenging exam that requires a lot of preparation and practice. It is important for candidates to have a deep understanding of the technologies used in Big Data Engineering and to be familiar with the latest trends and developments in the field. Candidates should also have hands-on experience working with Big Data technologies and be able to apply their knowledge to real-world scenarios.

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