This final evaluation exam will test your understanding and practical skills in Big Data concepts, the Hadoop Ecosystem, Apache Spark, and related technologies covered in the course. The exam includes theoretical questions, scenario-based problems, and hands-on labs to ensure a comprehensive assessment.

Exam Components:

Module 1 – What is Big Data?

1. Introduction to Big Data:
   • What is Big Data?
   • What is the impact of Big Data on various industries?
   • Explain parallel processing, scaling, and data parallelism.
   • Identify and describe the key tools used in Big Data.
   • Discuss practical Big Data use cases.
   • Evaluate different viewpoints about Big Data.

Module 2 – Introduction to the Hadoop Ecosystem

2. Hadoop Fundamentals:
   • What is Hadoop and why is it important for Big Data processing?
   • Explain the concept of MapReduce and its role in Hadoop.
3. Hadoop Ecosystem Components:
   • Describe the functionalities of HDFS, Hive, HBase, Spark, and other modules.
   • Lab: Demonstrate proficiency in MapReduce with hands-on tasks.

Module 3 – Introduction to Apache Spark

4. Spark Fundamentals:
   • Why use Apache Spark for Big Data processing?
   • What are the basics of functional programming?
5. Parallel Programming in Spark:
   • Describe the concept of Resilient Distributed Datasets (RDDs) and data parallelism.
   • Lab: Implement parallel programming tasks using PySpark.

Module 4 – DataFrames and SparkSQL

6. DataFrames and SparkSQL:
   • What are DataFrames and SparkSQL?
   • Explain the role of RDDs in parallel programming and Spark.
   • What are Catalyst and Tungsten?
   • How do you perform ETL with DataFrames?
   • Lab: Execute ETL tasks using DataFrames.
   • Discuss real-world usage of SparkSQL.
   • Lab: Perform queries using SparkSQL.

Module 5 – Development and Runtime Environment Options

7. Apache Spark Architecture and Deployment:
   • Describe the architecture of Apache Spark.
   • What are the different Apache Spark Cluster Modes?
   • How do you run an Apache Spark application?
   • What are the steps to use Apache Spark on IBM Cloud?
   • Lab: Scale-out on IBM Spark Environment in Watson Studio.
   • How do you configure Apache Spark settings?
   • What are the procedures for running Spark on Kubernetes?
   • Lab: Deploy and run Spark on Kubernetes.

Module 6 – Monitoring & Tuning

8. Monitoring and Tuning Apache Spark:
   • What are the key features of the Apache Spark User Interface?
   • How do you monitor jobs and debug parallel jobs?
   • Explain the importance of understanding memory and processor resources in Spark.
   • Lab: Monitor and tune the performance of Spark applications.