IBM Big Data Engineer : C2090-101

C2090-101 real exams

Exam Code: C2090-101

Exam Name: IBM Big Data Engineer

Updated: Aug 05, 2026

Q & A: 108 Questions and Answers

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About IBM C2090-101 Exam

Getting certified is really a good way to advance your career in the IT industry. So which IT certification do you want to get? Maybe IBM Analytics: Platform Analytics IBM Big Data Engineer exam certification is right certification you are looking for. Maybe you are still confused about how to prepare for it. Thus you can consider finding an accountable and reliable IT exam training provider for IBM Big Data Engineer actual exam test. Here, C2090-101 examkiller practice dumps may be a good study reference for you. Our IBM Big Data Engineer test training reviews can ensure you pass the exam at first attempt.

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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.

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

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IBM C2090-101 Exam topics

Candidates must know the exam topics before they start of preparation. Because it will really help them in hitting the core. Our C2090-101 exam dumps will include the following topics:

  • Data Preparation, Transformation, and Export: 26%
  • Architecture and Integration: 17%
  • Data Security: 8%
  • Performance and Scalability: 15%
  • Data Loading: 34%

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IBM C2090-101 Exam Syllabus Topics:

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

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