IBM C2090-101 exam - in .pdf

C2090-101 pdf
  • Exam Code: C2090-101
  • Exam Name: IBM Big Data Engineer
  • 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
  • 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
  • Q & A: 108 Questions and Answers
  • Software Price: $59.99
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About IBM Big Data Engineer : C2090-101 Exam Torrent

IBM C2090-101 Exam topics

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

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

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Who should take the C2090-101 exam

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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IBM C2090-101 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Data Security8%- Comply with security standards (PCI, PII protection)
- Implement access control and LDAP security
- Monitor and secure sensitive data
- Apply data masking and redaction techniques
Topic 2: Data Preparation, Transformation, and Export26%- Use Pig for data transformation
- Create BigSheets workbooks
- Export results from Hadoop environment
- Apply Jaql for data manipulation
- Perform entity resolution and data cleansing
Topic 3: Data Loading34%- Load log files with Flume and command-line tools
- Extract data from relational sources using Sqoop
- Ingest data into HBase and Hive
- Import streaming data using InfoSphere Streams
- Create Big SQL tables and load data into Hadoop
- Load unstructured data into InfoSphere BigInsights
Topic 4: Performance and Scalability15%- Apply best practices for latency and throughput
- Ensure high availability and scalability
- Optimize query execution and workload management
- Tune Hive, HBase, and Big SQL performance
Topic 5: Architecture and Integration17%- Integrate GPFS and distributed file systems
- Evaluate use cases for Hive, Big SQL, and HBase
- Design cluster architecture and storage layers
- Ensure consistent processing with InfoSphere Streams
- Implement MapReduce and RDD concepts

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