Practice Pagerank Algorithm With Spark (illustrative Example) (2.4) - Cloud Applications: MapReduce, Spark, and Apache Kafka
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PageRank Algorithm with Spark (Illustrative Example)

Practice - PageRank Algorithm with Spark (Illustrative Example)

Learning

Practice Questions

Test your understanding with targeted questions

Question 1 Easy

What does the PageRank algorithm calculate?

💡 Hint: Think about how webpages relate to each other.

Question 2 Easy

What is the damping factor typically set to in PageRank?

💡 Hint: It's a value between 0 and 1.

4 more questions available

Interactive Quizzes

Quick quizzes to reinforce your learning

Question 1

What is the primary goal of the PageRank algorithm?

To list all web pages
To rank web pages by importance
To store web pages data

💡 Hint: Think about why search engines use PageRank.

Question 2

True or False: PageRank uses RDDs for processing data.

True
False

💡 Hint: Consider the framework we are using.

2 more questions available

Challenge Problems

Push your limits with advanced challenges

Challenge 1 Hard

Devise a complete implementation plan for the PageRank algorithm using an example dataset of your choosing. Explain how you would optimize this process in Spark.

💡 Hint: Consider the dataset you are using and how to organize the links among pages.

Challenge 2 Hard

Critically evaluate the impact of varying the damping factor on the results of the PageRank algorithm. Create a hypothesis on how changes might affect the ranking of specific types of pages.

💡 Hint: Think about how users behave while browsing and how their actions might reflect on web page rankings.

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Reference links

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