MapReduce Programming Model: Introduction and Core Mechanics

Which statement best defines MapReduce in the context of big data processing?

• A programming model for processing large datasets across multiple nodes using distributed and parallel computation

• A single-node database engine optimized for transactional queries

• A visualization framework for plotting distributed systems

• A network protocol for fault-tolerant data transfer

In the MapReduce workflow, what is the primary role of the Map phase?

• Merge values with the same key to produce the final output

• Convert input key/value pairs into intermediate key/value pairs

• Schedule tasks across the cluster

• Provide automatic replication of input data

Which combination correctly pairs the function with its executor?

• Mapper executes Reduce; Reducer executes Map

• Mapper executes Map; Reducer executes Reduce

• Cluster executes Map; Mapper executes Reduce

• Reducer executes Map; Cluster executes Reduce

Given the pseudocode: def map(key, value): for word in value.split(): emit(word, 1). What intermediate key/value pairs are emitted for the input value "data data scale"?

• (data, 2), (scale, 1)

• (data, 1), (data, 1), (scale, 1)

• (data, 3), (scale, 0)

• (data scale, 1)

Which feature contributes to MapReduce fault tolerance as described in the material?

• Manual retry by developers for failed tasks

• Replicating the entire cluster state after each job

• Automatically reassigning failed tasks to other machines

• Pausing the job until the failed node is repaired

Which advantage is explicitly stated for MapReduce?

• It guarantees real-time analytics for all workloads

• It handles massive data and provides parallelism automatically

• It eliminates the need for key/value pairs

• It replaces the need for clusters

In the Reduce phase pseudocode def reduce(key, values): emit(key, sum(values)), what output would be produced for key="word" and values=[1,1,3]?

• emit(word, 1)

• emit(word, 3)

• emit(word, 5)

• emit(word, [1,1,3])

Which statement best describes a limitation of MapReduce for small jobs?

• It processes small jobs with very low latency

• It is not suitable for real-time processing

• It has high latency for small jobs

• It cannot shuffle and sort intermediate results

According to the quick recap process flow, what immediately follows the Map phase?

• Final Output

• Shuffle & Sort

• Reduce

• Intermediate Results

What is the primary purpose of the Map function in MapReduce?

• Aggregate values across keys

• Filter out all duplicate records

• Transform input data into key–value pairs producing intermediate results

• Sort keys globally before reducing