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IBM Big Data Fundamentals Technical Mastery Test v1 Sample Questions:

1. 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.

2. Which of the following options is CORRECT?

A) InfoSphere BigInsights is a distributed file system used as base for Hadoop distributions.
B) InfoSphere BigInsights is based on a branched Hadoop distribution, and therefore backwards compatibility is not guaranteed.
C) InfoSphere BigInsights is based on the nonforked core Hadoop distribution, but backwards compatibility with the Apache Hadoop project is not guaranteed, therefore applications written for Hadoop might not run on BigInsights.
D) InfoSphere BigInsights is based on the nonforked core Hadoop distribution, and backwards compatibility with the Apache Hadoop project will always be maintained. Therefore, all applications written for Hadoop will run on BigInsights.

3. What does ?Big Data? represent?

A) Large amounts of unstructured, semi-structured, and structured raw data that cannot be stored, processed or analyzed using traditional relational data warehouses.
B) A Hadoop feature capable of processing vast amounts of data in-parallel on large clusters of commodity hardware in a reliable, fault-tolerant manner.
C) Only data stored in the BIGDATA table in any relational database.
D) A database feature capable of converting pre-existing structured data into unstructured raw data.

4. Which of the following options best describes the proper usage of MapReduce jobs in Hadoop environments?

A) MapReduce jobs are used to process vast amounts of data in-parallel on large clusters of commodity hardware in a reliable, fault-tolerant manner.
B) MapReduce jobs are used to process structured data in sequence, with fault-tolerance.
C) MapReduce jobs are used to execute sequential search outside the Hadoop environment using a built-in UDF to access information stored in non-relational databases.
D) MapReduce jobs are used to process small amounts of data in-parallel on expensive hardware, without fault-tolerance.

Solutions: