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IBM 000-N32 real answers - IBM Big Data Fundamentals Technical Mastery Test v1

000-N32
  • Exam Code: 000-N32
  • Exam Name: IBM Big Data Fundamentals Technical Mastery Test v1
  • Updated: Aug 27, 2026
  • Q & A: 34 Questions and Answers
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  • Value Pack Total: $69.98

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IBM 000-N32 Exam Syllabus Topics:

SectionObjectives
Big Data Technologies- MapReduce concepts
- NoSQL database fundamentals
- Hadoop ecosystem fundamentals
Big Data Architecture- Data sources and data ingestion
- Data processing and analytics workflow
- Storage and distributed computing concepts
IBM Big Data Platform- Integration with IBM Information Management products
- IBM InfoSphere BigInsights overview
- IBM InfoSphere Streams overview
Analytics and Business Applications- Predictive analytics concepts
- Business intelligence and decision support
- Real-time analytics scenarios
Big Data Fundamentals- Business value and common use cases
- Big Data challenges and opportunities
- Definition and characteristics of Big Data

IBM Big Data Fundamentals Technical Mastery Test v1 Sample Questions:

Question 1

Which of the following statements is NOT CORRECT?

A. InfoSphere Streams supports communications to Internet Protocol version 6 (IPv6) networks.
B. InfoSphere Streams supports both command line and graphical interfaces to administer the
C. InfoSphere Streams jobs must be coded using either HiveQL or Jaql languages.
D. InfoSphere Streams provides support for reuse of existing Java or C++ code, as well as Predictive Model Markup Language (PMML) models.


Question 2

Which of the following InfoSphere BigInsights features provides a vast library of extractors enabling actionable insights from large amounts of native textual data?

A. General Parallel File System.
B. Text Analytics.
C. Adaptive MapReduce.
D. BigSheets.


Question 3

What is the difference between Hadoop?s MapReduce and IBM?s Adaptive MapReduce feature available in InfoSphere BigInsights?

A. Hadoop?s MapReduce is optimized for operating on small files or splits, while IBM?s Adaptive MapReduce is optimized for operating on large files stored in individual blocks.
B. Hadoop?s MapReduce is optimized for operating on large files, while IBM?s Adaptive MapReduce is configurable to operate optimized on large or small files or splits.
C. Hadoop?s MapReduce is optimized for operating on small partitioned tables stored in the HBase component, while IBM?s Adaptive MapReduce is optimized for operating on large partitioned files.
D. Hadoop?s MapReduce is optimized for operating on small files or splits, while IBM?s Adaptive MapReduce is optimized for operating on large partitioned files.


Question 4

How is data stored in a Hadoop cluster?

A. The data converted into a single block, and the block is stored in just one of the servers in the Hadoop cluster.
B. The data is divided into blocks, and copies of these blocks are replicated across multiple servers in the Hadoop cluster.
C. The data is divided into blocks, each block is stored in a different server in the Hadoop cluster, but the blocks are not replicated.
D. The data is converted into a single block, and copies of this block are replicated across multiple servers in the Hadoop cluster.


Solutions:

Question 1
Answer: C
Question 2
Answer: B
Question 3
Answer: B
Question 4
Answer: B

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