Understand The Concept Of Map Reduce - Programming Quiz

1. MapReduce use large number of computers (nodes), collectively referred to as

Pack

Litter

Cluster

Group

MapReduce is a programming model and algorithm used for processing and generating large sets of data in a distributed computing environment. It relies on dividing the workload into smaller tasks that can be executed simultaneously on multiple computers, known as nodes. These nodes are organized into a cluster, which refers to a group of interconnected computers working together to perform a common task. Therefore, the correct answer is "Cluster."

Explanation

MapReduce is a programming model and algorithm used for processing and generating large sets of data in a distributed computing environment. It relies on dividing the workload into smaller tasks that can be executed simultaneously on multiple computers, known as nodes. These nodes are organized into a cluster, which refers to a group of interconnected computers working together to perform a common task. Therefore, the correct answer is "Cluster."

2. MapReduce is a programming and an associated implementation for processing and generating_____

Big data sets

Small data sets

Infinite data sets

Finite data sets

MapReduce is a programming and implementation framework specifically designed for processing and generating big data sets. It allows for the parallel processing of large amounts of data across multiple nodes in a cluster, making it ideal for handling the massive volumes of data typically associated with big data sets. The framework breaks down the data processing tasks into smaller, more manageable chunks that can be distributed across the nodes for efficient processing and analysis. Therefore, the correct answer is "Big data sets".

Explanation

MapReduce is a programming and implementation framework specifically designed for processing and generating big data sets. It allows for the parallel processing of large amounts of data across multiple nodes in a cluster, making it ideal for handling the massive volumes of data typically associated with big data sets. The framework breaks down the data processing tasks into smaller, more manageable chunks that can be distributed across the nodes for efficient processing and analysis. Therefore, the correct answer is "Big data sets".

3. The name MapReduce originally referred to the proprietary of_______

Apple Technology

Microsoft Technology

Amazon Technology

Google Technology

The correct answer is Google Technology. The term "MapReduce" was originally used to describe the proprietary technology developed by Google. This technology allowed for the processing and analysis of large datasets by dividing them into smaller parts (mapping) and then combining the results (reducing). Google's implementation of MapReduce was a key component in the development of their search engine and data processing capabilities.

Explanation

The correct answer is Google Technology. The term "MapReduce" was originally used to describe the proprietary technology developed by Google. This technology allowed for the processing and analysis of large datasets by dividing them into smaller parts (mapping) and then combining the results (reducing). Google's implementation of MapReduce was a key component in the development of their search engine and data processing capabilities.

4. MapReduce allows for distributed processing of the map and reduction operations

Seldom true

Not true

Always true

Sometimes true

MapReduce is a programming model used for processing and generating large data sets. It allows for distributed processing, meaning that the tasks can be divided and processed across multiple machines in parallel. This enables scalability and efficient utilization of resources. Therefore, it is always true that MapReduce allows for distributed processing of the map and reduction operations.

Explanation

MapReduce is a programming model used for processing and generating large data sets. It allows for distributed processing, meaning that the tasks can be divided and processed across multiple machines in parallel. This enables scalability and efficient utilization of resources. Therefore, it is always true that MapReduce allows for distributed processing of the map and reduction operations.

5. The Map and Reduce functions of MapReduce are both defined with respect to

Trios

Pairs

Single

Serials

The Map and Reduce functions of MapReduce are both defined with respect to pairs. In the Map phase, the input data is divided into key-value pairs, where the key represents the intermediate result and the value represents the corresponding data. The Map function processes each pair independently and generates a set of intermediate key-value pairs. In the Reduce phase, the intermediate key-value pairs are grouped by key, and the Reduce function processes each group to produce the final output. Therefore, the Map and Reduce functions are designed to work with pairs of data.

Explanation

The Map and Reduce functions of MapReduce are both defined with respect to pairs. In the Map phase, the input data is divided into key-value pairs, where the key represents the intermediate result and the value represents the corresponding data. The Map function processes each pair independently and generates a set of intermediate key-value pairs. In the Reduce phase, the intermediate key-value pairs are grouped by key, and the Reduce function processes each group to produce the final output. Therefore, the Map and Reduce functions are designed to work with pairs of data.

6. Methods of processing data in MapReduce include all except

Map step

Shuffle step

Reduce step

Adjustment step

The MapReduce framework is used for processing large datasets in a distributed computing environment. It consists of three main steps: Map, Shuffle, and Reduce. In the Map step, the input data is divided into smaller chunks and processed independently. In the Shuffle step, the intermediate results from the Map step are sorted and grouped based on a specified key. In the Reduce step, the grouped data is combined and processed to produce the final output. However, there is no Adjustment step in the MapReduce framework, making it the correct answer.

Explanation

The MapReduce framework is used for processing large datasets in a distributed computing environment. It consists of three main steps: Map, Shuffle, and Reduce. In the Map step, the input data is divided into smaller chunks and processed independently. In the Shuffle step, the intermediate results from the Map step are sorted and grouped based on a specified key. In the Reduce step, the grouped data is combined and processed to produce the final output. However, there is no Adjustment step in the MapReduce framework, making it the correct answer.

7. The model is the specialization of spilt-supply-combine strategy for data analysis

Not true

Often true

Always true

Seldom true

The given statement is always true because the model mentioned is a specialization of the split-supply-combine strategy for data analysis. This means that the model is specifically designed to implement and utilize this strategy in order to analyze data effectively. Therefore, it can be concluded that the statement is always true.

Explanation

The given statement is always true because the model mentioned is a specialization of the split-supply-combine strategy for data analysis. This means that the model is specifically designed to implement and utilize this strategy in order to analyze data effectively. Therefore, it can be concluded that the statement is always true.

8. Another way to look at MapReduce is as a_____ step parallel

4

5

6

3

MapReduce is a parallel processing technique used in big data processing. It involves breaking down a large dataset into smaller chunks and processing them in parallel across multiple nodes or machines. In this context, the statement "Another way to look at MapReduce is as a 5 step parallel" suggests that MapReduce can be seen as a process that involves five parallel steps. However, without further context or information, it is difficult to determine the specific nature of these steps.

Explanation

MapReduce is a parallel processing technique used in big data processing. It involves breaking down a large dataset into smaller chunks and processing them in parallel across multiple nodes or machines. In this context, the statement "Another way to look at MapReduce is as a 5 step parallel" suggests that MapReduce can be seen as a process that involves five parallel steps. However, without further context or information, it is difficult to determine the specific nature of these steps.

9. MapReduce program is composed of a

Mapping method

Map reading method

Map reduction method

Map procedure method

The correct answer is "Mapping method" because in a MapReduce program, the mapping method is responsible for taking input data and converting it into key-value pairs. This method is applied to each input record independently and generates intermediate key-value pairs. These intermediate pairs are then passed to the reducing method for further processing. The mapping method plays a crucial role in the MapReduce framework as it helps in distributing the workload across multiple nodes and enables parallel processing of data.

Explanation

The correct answer is "Mapping method" because in a MapReduce program, the mapping method is responsible for taking input data and converting it into key-value pairs. This method is applied to each input record independently and generates intermediate key-value pairs. These intermediate pairs are then passed to the reducing method for further processing. The mapping method plays a crucial role in the MapReduce framework as it helps in distributing the workload across multiple nodes and enables parallel processing of data.