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Welcome to the Big Data Processing with MapReduce MCQs Page

Dive deep into the fascinating world of Big Data Processing with MapReduce with our comprehensive set of Multiple-Choice Questions (MCQs). This page is dedicated to exploring the fundamental concepts and intricacies of Big Data Processing with MapReduce, a crucial aspect of Big Data Computing. In this section, you will encounter a diverse range of MCQs that cover various aspects of Big Data Processing with MapReduce, from the basic principles to advanced topics. Each question is thoughtfully crafted to challenge your knowledge and deepen your understanding of this critical subcategory within Big Data Computing.

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Check out the MCQs below to embark on an enriching journey through Big Data Processing with MapReduce. Test your knowledge, expand your horizons, and solidify your grasp on this vital area of Big Data Computing.

Note: Each MCQ comes with multiple answer choices. Select the most appropriate option and test your understanding of Big Data Processing with MapReduce. You can click on an option to test your knowledge before viewing the solution for a MCQ. Happy learning!

Big Data Processing with MapReduce MCQs | Page 4 of 8

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Q31.
What is the primary advantage of using MapReduce for log analysis?

a.

It simplifies log file storage.

b.

It allows real-time analysis of logs.

c.

It enables the analysis of logs en masse.

d.

It automatically structures log data.

Discuss
Answer: (c).It enables the analysis of logs en masse. Explanation:MapReduce's primary advantage in log analysis is its ability to efficiently analyze logs en masse, making it suitable for processing large volumes of log data.
Q32.
How does MapReduce handle logs that are not entirely structured?

a.

It discards unstructured log entries.

b.

It converts unstructured logs into structured data.

c.

It requires changes to the database structure.

d.

It processes unstructured logs as-is.

Discuss
Answer: (d).It processes unstructured logs as-is. Explanation:MapReduce processes unstructured logs as they are, without requiring prior conversion or discarding unstructured log entries.
Q33.
In the context of MapReduce, what is meant by "embarrassingly parallel problems"?

a.

Problems that are extremely challenging and require advanced algorithms

b.

Problems with dependencies or communication requirements

c.

Problems that can be easily parallelized and show linear speedup

d.

Problems that are too simple to be solved using MapReduce

Discuss
Answer: (c).Problems that can be easily parallelized and show linear speedup Explanation:"Embarrassingly parallel problems" in the context of MapReduce refer to problems that can be easily parallelized and show linear speedup when parallelized.
Q34.
Which major search engines are known to use MapReduce for various tasks?

a.

Google and Bing

b.

Yahoo! and Bing

c.

Google and Yahoo!

d.

Google, Yahoo!, and Bing

Discuss
Answer: (c).Google and Yahoo! Explanation:Both Google and Yahoo! are known to use MapReduce for various tasks, including web indexing and search engine operations.
Q35.
What distinguishes Grid computing from MapReduce in terms of data processing?

a.

Grid computing focuses on data-intensive jobs.

b.

Grid computing emphasizes computation near the data.

c.

Grid computing provides a simple interface for programmers.

d.

Grid computing distributes data across multiple computers.

Discuss
Answer: (b).Grid computing emphasizes computation near the data. Explanation:Grid computing emphasizes computation near the data, while MapReduce focuses on parallel computation and is less concerned with data location.
Q36.
Why is MapReduce not perfectly suited for all graph problems?

a.

It lacks the ability to process graphs.

b.

It cannot handle large graphs.

c.

It cannot walk through vertices.

d.

It requires too much bandwidth.

Discuss
Answer: (c).It cannot walk through vertices. Explanation:MapReduce is not perfectly suited for all graph problems because some graph problems require walking through the vertices, which may not be possible using the MapReduce model.
Q37.
How can MapReduce be used to work around the limitations in processing large graphs?

a.

By receiving the entire graph as input

b.

By using custom optimized graph representations

c.

By implementing custom graph algorithms

d.

By increasing the number of reducers

Discuss
Answer: (b).By using custom optimized graph representations Explanation:MapReduce can work around the limitations in processing large graphs by using custom optimized graph representations such as sparse adjacency matrices and by iterating through multiple maps and reduce iterations.
Q38.
What is PageRank, and how is it typically implemented in a MapReduce application?

a.

PageRank is a sorting algorithm used in MapReduce.

b.

PageRank is a graph problem that cannot be solved using MapReduce.

c.

PageRank is an algorithm for ranking interlinked elements, typically implemented as a chained MapReduce application that iterates over elements to calculate their PageRank values.

d.

PageRank is a file compression algorithm used in MapReduce.

Discuss
Answer: (c).PageRank is an algorithm for ranking interlinked elements, typically implemented as a chained MapReduce application that iterates over elements to calculate their PageRank values. Explanation:PageRank is an algorithm for ranking interlinked elements, and it is typically implemented as a chained MapReduce application that iterates over elements to calculate their PageRank values.
Q39.
Which company originally designed and implemented the Google MapReduce framework?

a.

Microsoft

b.

Amazon

c.

Facebook

d.

Google

Discuss
Answer: (d).Google Explanation:Google originally designed and implemented the Google MapReduce framework.
Q40.
How did Google's MapReduce implementation evolve in terms of job numbers, job completion times, and output data size?

a.

The number of jobs decreased, job completion times increased, and output data size decreased.

b.

The number of jobs increased, job completion times decreased, and output data size increased.

c.

The number of jobs remained constant, job completion times remained the same, and output data size remained constant.

d.

The number of jobs fluctuated, job completion times fluctuated, and output data size fluctuated.

Discuss
Answer: (b).The number of jobs increased, job completion times decreased, and output data size increased. Explanation:Over time, Google's MapReduce implementation saw an increase in the number of jobs, a decrease in job completion times, and a significant increase in output data size, as reported by Jeff Dean.
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