1. Which of the following is a primary feature of the Hadoop framework?
A. In-memory processing for real-time data
B. Distributed storage and processing of large-scale datasets
C. Built-in machine learning algorithms
D. Interactive data visualization
Answer: B
2. What is the core component of Hadoop that is responsible for storing large datasets?
A. Hadoop MapReduce
B. Hadoop Distributed File System (HDFS)
C. Apache Hive
D. Apache Pig
Answer: B
3. Which of the following is the main advantage of using Apache Spark over Hadoop MapReduce?
A. Spark is faster due to in-memory processing
B. Hadoop MapReduce processes data in memory
C. Spark only works with small datasets
D. Spark does not support real-time processing
Answer: A
4. In Hadoop, what is the role of the JobTracker?
A. It schedules and manages tasks for distributed processing
B. It manages the file system operations
C. It stores the data in a distributed environment
D. It processes the data in parallel
Answer: A
5. Which of the following frameworks is best suited for performing iterative algorithms on large datasets?
A. Hadoop MapReduce
B. Apache Spark
C. Apache Hive
D. Apache Flink
Answer: B
6. What is the function of the ResourceManager in the Hadoop YARN architecture?
A. It allocates resources and schedules tasks on the cluster
B. It handles data storage and retrieval in HDFS
C. It runs MapReduce jobs
D. It performs data preprocessing operations
Answer: A
7. Which of the following is a data processing model used by Apache Spark for parallel processing?
A. MapReduce
B. Directed Acyclic Graph (DAG)
C. Entity-Relationship Model
D. Dataflow Model
Answer: B
8. Which of the following is a feature of Apache Spark’s Resilient Distributed Dataset (RDD)?
A. RDDs are immutable and can be recomputed in case of failure
B. RDDs are stored in memory on a single machine
C. RDDs cannot be processed in parallel
D. RDDs are only used for data storage, not processing
Answer: A
9. In the context of Hadoop, what is MapReduce used for?
A. Storing large datasets across clusters
B. Writing complex SQL queries for data analysis
C. Processing large datasets in a distributed manner
D. Visualizing results of data analysis
Answer: C
10. What is Apache Hive primarily used for in a Hadoop ecosystem?
A. Real-time data processing
B. Data warehousing and querying with SQL-like syntax
C. Machine learning model development
D. Data visualization and reporting
Answer: B
11. Which of the following is an advantage of using Apache Spark over Hadoop MapReduce?
A. Spark supports only batch processing, unlike Hadoop
B. Spark is easier to set up and manage
C. Spark offers faster performance due to its in-memory computing
D. Spark requires fewer resources than Hadoop
Answer: C
12. In Apache Hadoop, what is the purpose of the NameNode?
A. It stores the actual data blocks in HDFS
B. It manages and tracks the metadata of files in HDFS
C. It performs parallel data processing
D. It executes the MapReduce jobs
Answer: B
13. Which of the following is the primary language used for writing Spark applications?
A. Java
B. Python
C. Scala
D. All of the above
Answer: D
14. In Spark, what is the purpose of the driver program?
A. It controls the execution of a Spark application by coordinating tasks across the cluster
B. It processes data stored in HDFS
C. It manages data storage and retrieval in Spark
D. It is responsible for handling incoming data streams
Answer: A
15. Which of the following frameworks is best suited for stream processing of real-time data?
A. Apache Hadoop
B. Apache Flink
C. Apache Kafka
D. Apache Hive
Answer: B