IBM C2090-101 exam - in .pdf

C2090-101 pdf
  • Exam Code: C2090-101
  • Exam Name: IBM Big Data Engineer
  • Updated: Aug 13, 2026
  • Q & A: 108 Questions and Answers
  • PDF Price: $59.99
  • PDF Demo

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  • Exam Code: C2090-101
  • Exam Name: IBM Big Data Engineer
  • Updated: Aug 13, 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 exam - Testing Engine

C2090-101 Testing Engine
  • Exam Code: C2090-101
  • Exam Name: IBM Big Data Engineer
  • Updated: Aug 13, 2026
  • Q & A: 108 Questions and Answers
  • Software Price: $59.99
  • Testing Engine

About IBM Big Data Engineer : C2090-101 Exam Torrent

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IBM Big Data Engineer C2090-101 Exam

IBM Big Data Engineer C2090-101 Exam is requisite to obtain IBM Analytics Platform Certification. This IBM C2090-101 Exam validates and measures the candidate knowledge in extract data from relational source using Sqoop, Utilize Apache Hue for search Visualization, Export Processing Results out of Hadoop and utilize Consistent Regions in infosphere Streams. Big Data Engineers and Data Security Consultants usually hold or pursue this certification and you can expect the same job roles after completion of IBM Analytics Platform certification.

IBM Big Data Engineer Exam Certification Details:

Schedule ExamPearson VUE
Sample QuestionsIBM Big Data Engineer Sample Questions
Number of Questions53
Passing Score64%
Exam Price$200 (USD)
Books / TrainingIBM Knowledge Center
Exam CodeC2090-101
Duration75 mins
Exam NameIBM Certified Data Engineer - Big Data

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

What is the duration of the C2090-101 Exam

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

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IBM C2090-101 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Data Collection and Ingestion14%- Data acquisition technologies
  • 1. Sqoop
  • 2. Flume
  • 3. Kafka
Topic 2: Hadoop Distributed Processing28%- Hadoop ecosystem
  • 1. YARN
  • 2. MapReduce
  • 3. Cluster management
  • 4. HDFS architecture
Topic 3: Data Transformation and Analytics24%- Data processing frameworks
  • 1. Apache Pig
  • 2. Apache Hive
  • 3. Data querying and transformation
  • 4. Apache Spark fundamentals
Topic 4: Data Governance, Security and Administration22%- Operational management
  • 1. Performance tuning
  • 2. Data governance
  • 3. Security concepts
  • 4. Monitoring and troubleshooting
Topic 5: Big Data Foundations12%- Big Data concepts
  • 1. Big Data architecture fundamentals
  • 2. Big Data use cases
  • 3. Volume, Velocity, Variety, Veracity

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