IBM 000-N32 : IBM Big Data Fundamentals Technical Mastery Test v1

000-N32 real exams

Exam Code: 000-N32

Exam Name: IBM Big Data Fundamentals Technical Mastery Test v1

Updated: Jul 27, 2026

Q & A: 34 Questions and Answers

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IBM 000-N32 Exam Syllabus Topics:

SectionObjectives
Topic 1: Big Data Fundamentals- Core Big Data Concepts
  • 1. Big data ecosystems overview
    • 2. Characteristics of Big Data (Volume, Velocity, Variety, Veracity)
      • 3. Structured vs unstructured vs semi-structured data
        - Big Data Technologies
        • 1. Hadoop ecosystem (HDFS, MapReduce)
          • 2. NoSQL databases (key-value, document, columnar, graph)
            • 3. Distributed computing concepts
              Topic 2: Data Processing & Analytics- Analytics Concepts
              • 1. Data visualization basics
                • 2. Descriptive, predictive, and prescriptive analytics
                  - Data Processing Frameworks
                  • 1. Batch vs streaming processing
                    • 2. Apache Spark fundamentals
                      Topic 3: Big Data Applications- Industry Use Cases
                      • 1. Retail and recommendation systems
                        • 2. Healthcare data analytics
                          • 3. Finance and risk analytics
                            Topic 4: Data Management- Data Storage and Warehousing
                            • 1. Data lakes vs data warehouses
                              • 2. ETL/ELT processes

                                IBM Big Data Fundamentals Technical Mastery Test v1 Sample Questions:

                                1. 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 large files, while IBM?s Adaptive MapReduce is configurable to operate optimized on large or small files or splits.
                                B) 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.
                                C) 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.
                                D) 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.


                                2. Which of the following options is CORRECT?

                                A) InfoSphere Streams is only able to process traditional structured data from a variety of sources.
                                B) InfoSphere Streams high availability feature allows for processing elements (PEs) on failing
                                nodes to be moved and automatically restarted,
                                with communications re-routed, to a healthy node.
                                C) InfoSphere Streams does not allow you to dynamically add hosts and jobs
                                D) InfoSphere Streams optimizes its workload by aggregating an entire job into a single node.


                                3. Which of the following options is CORRECT regarding InfoSphere Data Explorer?s annotators?

                                A) InfoSphere Data Explorer?s annotators is an add-on feature capable of handling of a variety of
                                data formats and types, including structured,
                                semi-structured and unstructured, as well as the special demands of rich media and transactional
                                data.
                                B) InfoSphere Data Explorer?s annotators allow users to interact with search results by providing
                                feedback about the result's value, and by
                                adding useful information and communication with other users.
                                C) InfoSphere Data Explorer?s annotators allow users to save results in a private/public folder for
                                later review or sharing.
                                D) InfoSphere Data Explorer?s annotators allow users to create groups of search results.


                                4. How do enterprises leverage big data platforms?

                                A) By storing all of the data in its native business object format, so the enterprise can get value out of it through massive parallelism using readily available components.
                                B) By sitting on top of a large data warehouse solution acting as transparent abstract conversion layer allowing enterprises to query unstructured data.
                                C) By isolating workloads into a single node, and further processing all the data in sequence.
                                D) By modifying the data being streamed using pre-existing ETL transformations, and storing the final formatted data into a data warehouse for further enterprise analysis.


                                Solutions:

                                Question # 1
                                Answer: A
                                Question # 2
                                Answer: B
                                Question # 3
                                Answer: B
                                Question # 4
                                Answer: A

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