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IBM 000-N32 Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Topic 1: Analytics and Business Applications | - Business intelligence and decision support scenarios - Real-time and streaming analytics - Predictive analytics fundamentals |
| Topic 2: Big Data Architecture | - Distributed storage concepts - Data acquisition and ingestion - Data processing and analytics pipeline |
| Topic 3: Big Data Fundamentals | - Big Data challenges and opportunities - Business drivers and value of Big Data - Definition and characteristics of Big Data |
| Topic 4: Core Big Data Technologies | - Hadoop ecosystem fundamentals - MapReduce processing model - NoSQL database concepts |
| Topic 5: IBM Big Data Solutions | - Integration with IBM analytics and information management products - IBM InfoSphere BigInsights overview - IBM InfoSphere Streams overview |
IBM Big Data Fundamentals Technical Mastery Test v1 Sample Questions:
Question #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 files or splits, while IBM?s Adaptive MapReduce is optimized for operating on large files stored in individual blocks.
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 #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. Text Analytics.
B. General Parallel File System.
C. Adaptive MapReduce.
D. BigSheets.
Question #3
What is HADOOP?
A. Hadoop is a framework that allows for the distributed processing of large data sets across clusters of computers using a simple programming model.
B. Hadoop is a single-node file system used as a base for storing traditional formatted data.
C. Hadoop is framework capable of transforming raw, unstructured data into plain, regular data readable by traditional data warehouses.
D. Hadoop is a universal Big Data programming language used to query large datasets.
Question #4
What are the ?4 Vs? that characterize IBM?s Big Data initiative?
A. Velocity, Volatility, Variety, Veracity
B. Volume, Volatility, Velocity, Variety
C. Veracity, Variety, Volume, Velocity
D. Variety, Versions, Velocity, Volatility
Solutions:
| Question #1 Correct Answer: A | Question #2 Correct Answer: A | Question #3 Correct Answer: A | Question #4 Correct Answer: C |
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