Practice Implementation Overview (Apache Hadoop MapReduce) - 1.6 | Week 8: Cloud Applications: MapReduce, Spark, and Apache Kafka | Distributed and Cloud Systems Micro Specialization
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1.6 - Implementation Overview (Apache Hadoop MapReduce)

Learning

Practice Questions

Test your understanding with targeted questions related to the topic.

Question 1

Easy

What is the primary function of the Mapper in MapReduce?

πŸ’‘ Hint: Think about what happens to the input data first.

Question 2

Easy

Define input split in the context of MapReduce.

πŸ’‘ Hint: Consider how input data is managed and processed.

Practice 4 more questions and get performance evaluation

Interactive Quizzes

Engage in quick quizzes to reinforce what you've learned and check your comprehension.

Question 1

What is the main purpose of the Map phase in MapReduce?

  • To aggregate data
  • To transform input data into intermediate key-value pairs
  • To sort data

πŸ’‘ Hint: Focus on what the Mapper does to the input data.

Question 2

True or False: In the Shuffle and Sort phase, data is sorted to optimize the Reduce phase.

  • True
  • False

πŸ’‘ Hint: Think about organization of data between phases.

Solve 1 more question and get performance evaluation

Challenge Problems

Push your limits with challenges.

Question 1

In a given dataset of sales records, how would you implement a MapReduce job to find the total sales for each product?

πŸ’‘ Hint: Think about how to separate and aggregate sales data effectively.

Question 2

Explain how fault tolerance is achieved in MapReduce.

πŸ’‘ Hint: Consider the mechanisms in place for handling task failures.

Challenge and get performance evaluation