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Practice Questions
Test your understanding with targeted questions related to the topic.
Question 1
Easy
What is MapReduce used for?
💡 Hint: Think about the big data context.
Question 2
Easy
What does RDD stand for?
💡 Hint: What does the term 'RDD' represent in Spark?
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 a key characteristic of MapReduce?
- Real-time processing
- Distributed batch processing
- In-memory computation
💡 Hint: Think about the type of tasks it is meant to handle.
Question 2
True or False: Apache Spark can only handle batch processing.
- True
- False
💡 Hint: Consider Spark's flexibility in handling data.
Solve 1 more question and get performance evaluation
Challenge Problems
Push your limits with challenges.
Question 1
Consider a scenario where you have a massive log file. Design a MapReduce job that would count the unique IP addresses visiting a website. Mention each phase and how you would implement the Mapper and Reducer.
💡 Hint: Think about how the data is structured in the log file.
Question 2
In a data processing application, you are tasked with analyzing customer purchase patterns from large datasets. Discuss how you would use Spark's RDDs to manage this, and why it would be advantageous over using MapReduce.
💡 Hint: Consider the advantages of processing speed and data retrieval.
Challenge and get performance evaluation