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IBM Big Data Fundamentals Technical Mastery Test v1 Sample Questions:

1. Which of the following options best describes the differences between a traditional data warehouse environment and a Hadoop environment?

A) Traditional data warehousing environments are mostly ideal for analyzing structured data from
various systems, while a Hadoop environment is
well suited to deal with semi-structured and unstructured data, as well as when a data discovery
process is needed.
B) Typically, data stored in Hadoop environments is cleaned up before storing in the distributed
file-system.
C) Typically, data stored in data warehousing environments is rarely filtered and pre-processed.
On the other hand, data injected into Hadoop
environments is always pre-processed and filtered.
D) Hadoop environments are mostly ideal for analyzing structured and semi-structured data from a
single system, while traditional data
warehousing environment is well suited to deal with unstructured data, as well as when a data
discovery process is needed.

2. Which of the following tools can be used to configure the InfoSphere Data Explorer environment (choose two) ?

A) Data Studio Web Console
B) InfoSphere Data Explorer?s web-based interface
C) Data Explorer Virtual Desktop
D) REST/SOAP APIs

3. 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, and backwards compatibility with the Apache Hadoop project will always be maintained. Therefore, all applications written for Hadoop will run on BigInsights.
D) 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.

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

A) MapReduce jobs are used to process structured data in sequence, with fault-tolerance.
B) MapReduce jobs are used to process small amounts of data in-parallel on expensive hardware, without 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 vast amounts of data in-parallel on large clusters of commodity hardware in a reliable, fault-tolerant manner.

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