Apache Spark For Big Data Processing (pyspark)

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About the Quiz

Quiz will ask 20 randomly selected questions with allotted time of . You can take the quiz more than once. Once you submit the quiz, you can review how you have done, the correct the answers for each questions and the explanation for the correct the answer.

Quiz Topics

8 Modules

Integration and Ecosystem

5 topics
1.

Deployment Strategies (JAR, Docker, etc.)

10 questions
2.

Integrating with Hadoop Ecosystem (HDFS, Hive)

10 questions
3.

Interoperability with Other Languages (Python, R, Scala)

10 questions
4.

Using Spark in Cloud Environments (AWS, Azure, GCP)

10 questions
5.

Using Spark with Apache Kafka

10 questions

Performance Tuning

5 topics

Graph Processing with GraphX

5 topics

Machine Learning with Spark MLlib

5 topics

Spark Streaming

5 topics

Spark SQL

5 topics

Spark Core

5 topics

Apache Spark Architecture

5 topics
Sample questions

Which of the following components is responsible for converting Spark applications into a directed acyclic graph (DAG)?

Driver

Executor

Cluster Manager

Task Scheduler

What role does the Cluster Manager play in the Apache Spark architecture?

It manages the resources across the cluster.

It executes the tasks in parallel.

It schedules the jobs submitted to Spark.

It handles data storage and retrieval.

In a Spark application, which component is responsible for executing the tasks assigned to it?

Driver

Executor

Cluster Manager

Job Server

Which of the following statements about the Driver in Spark is true?

It runs on the worker nodes.

It can be run in local mode.

It is responsible for fault tolerance.

It maintains the SparkContext.

What is the purpose of the Task Scheduler in Spark?

To manage the execution of tasks on Executors.

To optimize the execution plan.

To handle data shuffling.

To monitor resource usage.

Quiz Topics

8 Modules

Integration and Ecosystem

5 topics
1.

Deployment Strategies (JAR, Docker, etc.)

10 questions
2.

Integrating with Hadoop Ecosystem (HDFS, Hive)

10 questions
3.

Interoperability with Other Languages (Python, R, Scala)

10 questions
4.

Using Spark in Cloud Environments (AWS, Azure, GCP)

10 questions
5.

Using Spark with Apache Kafka

10 questions

Performance Tuning

5 topics

Graph Processing with GraphX

5 topics

Machine Learning with Spark MLlib

5 topics

Spark Streaming

5 topics

Spark SQL

5 topics

Spark Core

5 topics

Apache Spark Architecture

5 topics