Practice Handling task failures - 1.4.2.2.4 | Week 8: Cloud Applications: MapReduce, Spark, and Apache Kafka | Distributed and Cloud Systems Micro Specialization
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1.4.2.2.4 - Handling task failures

Learning

Practice Questions

Test your understanding with targeted questions related to the topic.

Question 1

Easy

What does task re-execution entail in MapReduce?

πŸ’‘ Hint: Think about what happens if a task stops unexpectedly.

Question 2

Easy

Explain why intermediate data durability is important.

πŸ’‘ Hint: Consider what would happen if a task crashed.

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 purpose of task re-execution?

  • To cancel the job
  • To execute failed tasks on different nodes
  • To increase network latency

πŸ’‘ Hint: Consider why tasks might need to be run again.

Question 2

True or False: Speculative execution allows for the duplication of slow tasks to speed up job completion.

  • True
  • False

πŸ’‘ Hint: Think about what happens when tasks take longer than their peers.

Solve 1 more question and get performance evaluation

Challenge Problems

Push your limits with challenges.

Question 1

You are managing a large MapReduce framework. How would you address a situation where multiple tasks are failing across several nodes? Outline your strategy.

πŸ’‘ Hint: Think about both short-term solutions and long-term strategies.

Question 2

Explain how the architectural changes from a JobTracker to YARN affect task failure handling.

πŸ’‘ Hint: Consider the implications of resource management and scheduling in distributed environments.

Challenge and get performance evaluation