Practice Resilient (Fault-Tolerant) - 2.1.1 | Week 8: Cloud Applications: MapReduce, Spark, and Apache Kafka | Distributed and Cloud Systems Micro Specialization
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2.1.1 - Resilient (Fault-Tolerant)

Learning

Practice Questions

Test your understanding with targeted questions related to the topic.

Question 1

Easy

What is fault tolerance in the context of data processing systems?

💡 Hint: Think about why continuity is essential in operations.

Question 2

Easy

Name one mechanism used in MapReduce to ensure fault tolerance.

💡 Hint: Which method reschedules tasks when they fail?

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 primary mechanism for fault tolerance in MapReduce?

  • Data replication
  • Task re-execution
  • Intermediate storage

💡 Hint: Which method allows a failed task to continue processing elsewhere?

Question 2

True or False: RDDs allow lossless recovery of data without requiring data duplication.

  • True
  • False

💡 Hint: What feature of RDDs supports this recovery?

Solve 1 more question and get performance evaluation

Challenge Problems

Push your limits with challenges.

Question 1

Design a fault-tolerant data pipeline using MapReduce. Discuss the components and mechanisms you'd employ.

💡 Hint: Think about each component's role in keeping the pipeline running smoothly.

Question 2

Critically analyze the differences in fault tolerance strategies between Spark and MapReduce. Provide examples for clarity.

💡 Hint: Consider how each framework responds to task failures.

Challenge and get performance evaluation