Practice Input Processing (1.1.1.1) - Cloud Applications: MapReduce, Spark, and Apache Kafka
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Input Processing

Practice - Input Processing

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Practice Questions

Test your understanding with targeted questions

Question 1 Easy

What are the two primary phases of MapReduce?

💡 Hint: Think about how MapReduce processes data.

Question 2 Easy

What does RDD stand for in Spark?

💡 Hint: Focus on the main data structure used by Spark.

4 more questions available

Interactive Quizzes

Quick quizzes to reinforce your learning

Question 1

What are the two main phases of MapReduce?

Map and Reduce
Shuffle and Sort
Input and Output

💡 Hint: Think about the structure of the processing workflow.

Question 2

True or False: Kafka allows consumers to re-read messages even after they have been consumed.

True
False

💡 Hint: Consider the characteristics of message storage.

1 more question available

Challenge Problems

Push your limits with advanced challenges

Challenge 1 Hard

You have a large dataset of sales transactions. How would you implement a MapReduce job to calculate total sales for each product?

💡 Hint: Focus on the flow of data from mapping to reducing.

Challenge 2 Hard

Using Spark, explain how you would optimize a job initially written for MapReduce that relies heavily on disk reads.

💡 Hint: Think about how in-memory processing affects efficiency.

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Reference links

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