Flashcards

Distributed Computing

Processing large data on multiple computers

Methods for processing large data sets by breaking them into smaller subsets and processing them on different computers in parallel.

5 minutes 5 Questions

Distributed Computing forms the backbone of most Big Data operations by spreading computational tasks across multiple machines. This approach enables processing massive datasets that would be impossible to handle on a single computer. At its core, distributed computing involves dividing a large pr…

Test mode:

Exam (Timed)

Practice (With explanations)

Start practice test

Big Data Scientist - Distributed Computing Example Questions

Test your knowledge of Distributed Computing

Question 1

What is the purpose of Spark's Resilient Distributed Datasets (RDDs)?

To collect and store data in-memory within a Spark job. To visualize data within Spark. To perform machine learning on data in Spark. To allow for fault-tolerant parallel processing of data by dividing the data into partitions across a cluster of machines. To transform data between different file formats in Spark. To model and evaluate data using statistical analysis in Spark.

Correct Answer: To allow for fault-tolerant parallel processing of data by dividing the data into partitions across a cluster of machines.

Spark's Resilient Distributed Datasets (RDDs) are a fundamental data structure of Spark, allowing for fault-tolerant, parallel processing of data by dividing data into partitions across a cluster of machines.

Question 2

What is the purpose of Apache ZooKeeper?

To visualize and analyze data. To manipulate and transform data. To manage and coordinate distributed applications by providing a centralized infrastructure and a set of API's. To perform stream processing on large datasets. To store and manage large amounts of data. To run distributed computations.

Correct Answer: To manage and coordinate distributed applications by providing a centralized infrastructure and a set of API's.

Apache ZooKeeper is a centralized service for maintaining configuration information, naming, providing distributed synchronization, and providing group services.

Question 3

What is the purpose of a data node in HDFS?

To execute the NameNode in HDFS. To process computation jobs in MapReduce in HDFS. To monitor the health of the HDFS cluster. To manage data backup and restore in HDFS. To store data in HDFS by allocating the storage space and managing data on the node. To run as a client library for accessing HDFS.

Correct Answer: To store data in HDFS by allocating the storage space and managing data on the node.

A data node stores data by allocating the storage space and managing data on the node. DataNodes also remove blocks when they are no longer needed.

🎓 Unlock Premium Access

Big Data Scientist + ALL Certifications

🎓 Access to ALL Certifications: Study for any certification on our platform with one subscription
898 Superior-grade Big Data Scientist practice questions
Unlimited practice tests across all certifications
Detailed explanations for every question
100% Satisfaction Guaranteed: Full refund if unsatisfied
Risk-Free: 7-day free trial with all premium features!