Certified Big Data and Apache Hadoop Practice Exam
1. Which of the following best describes the “5 V’s” of Big Data?
A. Volume, Velocity, Variety, Veracity, and Value
B. Volume, Variability, Variation, Veracity, and Visuals
C. Volume, Velocity, Variety, Variability, and Value
D. Velocity, Variety, Visuals, Value, and Veracity
Answer: A
Explanation: The “5 V’s” of Big Data are Volume, Velocity, Variety, Veracity, and Value.
2. How does Big Data differ from traditional data processing?
A. Big Data is always structured while traditional data is unstructured
B. Big Data relies on high volume, speed, and variety, unlike traditional systems
C. Traditional data requires distributed systems while Big Data does not
D. There is no significant difference between Big Data and traditional data
Answer: B
Explanation: Big Data involves handling large volumes of rapidly changing and varied data,
unlike traditional systems.
3. Which characteristic of Big Data indicates the reliability and accuracy of the data?
A. Volume
B. Velocity
C. Veracity
D. Variety
Answer: C
Explanation: Veracity refers to the quality, reliability, and accuracy of the data.
4. What is one of the key benefits of Apache Hadoop?
A. It requires expensive hardware
B. It supports only structured data
C. It allows distributed storage and parallel processing
D. It is a proprietary software solution
Answer: C
Explanation: Apache Hadoop’s main benefit is its ability to store data distributedly and process it
in parallel across clusters.
,5. Which of the following is NOT a core component of the Hadoop Ecosystem?
A. Hadoop Distributed File System (HDFS)
B. MapReduce
C. YARN
D. Apache Cassandra
Answer: D
Explanation: Apache Cassandra is a NoSQL database and is not part of the core Hadoop
components.
6. In a Hadoop cluster, what is the role of the NameNode?
A. Store actual data blocks
B. Manage file system metadata
C. Execute MapReduce jobs
D. Monitor network traffic
Answer: B
Explanation: The NameNode manages the metadata and directory structure of HDFS.
7. What is the primary function of a DataNode in HDFS?
A. Execute user queries
B. Maintain the file system namespace
C. Store actual data blocks
D. Allocate resources for jobs
Answer: C
Explanation: DataNodes are responsible for storing the actual data blocks in HDFS.
8. Which statement best explains data locality in Hadoop?
A. Data is always processed on a central server
B. Processing happens where the data is stored to reduce network load
C. Data is moved across nodes frequently
D. Data locality is unrelated to performance
Answer: B
Explanation: Data locality means processing is done on the node where the data resides,
minimizing network congestion.
9. How does Hadoop ensure fault tolerance?
A. By backing up data to a remote server once a day
,B. Through data replication across multiple nodes
C. By using high-end hardware that rarely fails
D. By executing all jobs on a single, powerful node
Answer: B
Explanation: Hadoop replicates data blocks across different nodes to ensure fault tolerance in
case of node failures.
10. Which of the following best describes the role of MapReduce in Hadoop?
A. A storage layer for big data
B. A programming model for parallel data processing
C. A graphical user interface for Hadoop clusters
D. A security module for data encryption
Answer: B
Explanation: MapReduce is the programming model used to process large data sets in parallel on
a Hadoop cluster.
11. In MapReduce, what is the primary responsibility of the Mapper function?
A. To merge the outputs of Reducers
B. To convert input data into key-value pairs
C. To allocate resources to nodes
D. To store the final output
Answer: B
Explanation: The Mapper function processes input data and transforms it into intermediate key-
value pairs.
12. What is the role of the Reducer in the MapReduce framework?
A. To divide data into smaller chunks
B. To aggregate and process the Mapper’s key-value pairs
C. To store the intermediate data
D. To validate data integrity
Answer: B
Explanation: The Reducer aggregates the intermediate key-value pairs generated by the Mappers
and produces the final output.
13. Which phase in MapReduce is responsible for redistributing data based on keys?
A. Map phase
B. Shuffle phase
C. Reduce phase
, D. Write phase
Answer: B
Explanation: The Shuffle phase redistributes data by key so that all values associated with the
same key go to the same Reducer.
14. In a MapReduce job, what purpose does a Combiner serve?
A. It functions as a mini-reducer to optimize data transfer
B. It schedules tasks on the cluster
C. It directly writes the final output to HDFS
D. It monitors job progress
Answer: A
Explanation: A Combiner function acts as a mini-reducer to combine intermediate data and
reduce data transfer between Map and Reduce phases.
15. Which of the following is a key feature of YARN in Hadoop?
A. It replaces HDFS as the primary storage system
B. It manages and schedules cluster resources
C. It directly executes MapReduce programs
D. It encrypts all data within the cluster
Answer: B
Explanation: YARN (Yet Another Resource Negotiator) is responsible for managing and
scheduling resources across the Hadoop cluster.
16. In YARN, what component is responsible for managing the resources of the entire
cluster?
A. NodeManager
B. ApplicationMaster
C. ResourceManager
D. DataNode
Answer: C
Explanation: The ResourceManager is the central authority that manages resources and
scheduling in a YARN-based Hadoop cluster.
17. Which YARN component runs on each node and manages the execution of tasks on that
node?
A. ResourceManager
B. NameNode
C. NodeManager
1. Which of the following best describes the “5 V’s” of Big Data?
A. Volume, Velocity, Variety, Veracity, and Value
B. Volume, Variability, Variation, Veracity, and Visuals
C. Volume, Velocity, Variety, Variability, and Value
D. Velocity, Variety, Visuals, Value, and Veracity
Answer: A
Explanation: The “5 V’s” of Big Data are Volume, Velocity, Variety, Veracity, and Value.
2. How does Big Data differ from traditional data processing?
A. Big Data is always structured while traditional data is unstructured
B. Big Data relies on high volume, speed, and variety, unlike traditional systems
C. Traditional data requires distributed systems while Big Data does not
D. There is no significant difference between Big Data and traditional data
Answer: B
Explanation: Big Data involves handling large volumes of rapidly changing and varied data,
unlike traditional systems.
3. Which characteristic of Big Data indicates the reliability and accuracy of the data?
A. Volume
B. Velocity
C. Veracity
D. Variety
Answer: C
Explanation: Veracity refers to the quality, reliability, and accuracy of the data.
4. What is one of the key benefits of Apache Hadoop?
A. It requires expensive hardware
B. It supports only structured data
C. It allows distributed storage and parallel processing
D. It is a proprietary software solution
Answer: C
Explanation: Apache Hadoop’s main benefit is its ability to store data distributedly and process it
in parallel across clusters.
,5. Which of the following is NOT a core component of the Hadoop Ecosystem?
A. Hadoop Distributed File System (HDFS)
B. MapReduce
C. YARN
D. Apache Cassandra
Answer: D
Explanation: Apache Cassandra is a NoSQL database and is not part of the core Hadoop
components.
6. In a Hadoop cluster, what is the role of the NameNode?
A. Store actual data blocks
B. Manage file system metadata
C. Execute MapReduce jobs
D. Monitor network traffic
Answer: B
Explanation: The NameNode manages the metadata and directory structure of HDFS.
7. What is the primary function of a DataNode in HDFS?
A. Execute user queries
B. Maintain the file system namespace
C. Store actual data blocks
D. Allocate resources for jobs
Answer: C
Explanation: DataNodes are responsible for storing the actual data blocks in HDFS.
8. Which statement best explains data locality in Hadoop?
A. Data is always processed on a central server
B. Processing happens where the data is stored to reduce network load
C. Data is moved across nodes frequently
D. Data locality is unrelated to performance
Answer: B
Explanation: Data locality means processing is done on the node where the data resides,
minimizing network congestion.
9. How does Hadoop ensure fault tolerance?
A. By backing up data to a remote server once a day
,B. Through data replication across multiple nodes
C. By using high-end hardware that rarely fails
D. By executing all jobs on a single, powerful node
Answer: B
Explanation: Hadoop replicates data blocks across different nodes to ensure fault tolerance in
case of node failures.
10. Which of the following best describes the role of MapReduce in Hadoop?
A. A storage layer for big data
B. A programming model for parallel data processing
C. A graphical user interface for Hadoop clusters
D. A security module for data encryption
Answer: B
Explanation: MapReduce is the programming model used to process large data sets in parallel on
a Hadoop cluster.
11. In MapReduce, what is the primary responsibility of the Mapper function?
A. To merge the outputs of Reducers
B. To convert input data into key-value pairs
C. To allocate resources to nodes
D. To store the final output
Answer: B
Explanation: The Mapper function processes input data and transforms it into intermediate key-
value pairs.
12. What is the role of the Reducer in the MapReduce framework?
A. To divide data into smaller chunks
B. To aggregate and process the Mapper’s key-value pairs
C. To store the intermediate data
D. To validate data integrity
Answer: B
Explanation: The Reducer aggregates the intermediate key-value pairs generated by the Mappers
and produces the final output.
13. Which phase in MapReduce is responsible for redistributing data based on keys?
A. Map phase
B. Shuffle phase
C. Reduce phase
, D. Write phase
Answer: B
Explanation: The Shuffle phase redistributes data by key so that all values associated with the
same key go to the same Reducer.
14. In a MapReduce job, what purpose does a Combiner serve?
A. It functions as a mini-reducer to optimize data transfer
B. It schedules tasks on the cluster
C. It directly writes the final output to HDFS
D. It monitors job progress
Answer: A
Explanation: A Combiner function acts as a mini-reducer to combine intermediate data and
reduce data transfer between Map and Reduce phases.
15. Which of the following is a key feature of YARN in Hadoop?
A. It replaces HDFS as the primary storage system
B. It manages and schedules cluster resources
C. It directly executes MapReduce programs
D. It encrypts all data within the cluster
Answer: B
Explanation: YARN (Yet Another Resource Negotiator) is responsible for managing and
scheduling resources across the Hadoop cluster.
16. In YARN, what component is responsible for managing the resources of the entire
cluster?
A. NodeManager
B. ApplicationMaster
C. ResourceManager
D. DataNode
Answer: C
Explanation: The ResourceManager is the central authority that manages resources and
scheduling in a YARN-based Hadoop cluster.
17. Which YARN component runs on each node and manages the execution of tasks on that
node?
A. ResourceManager
B. NameNode
C. NodeManager
