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Q3 Big Data Engineer M1 to M5

Total questions: 30

Worksheet time: 30mins

Name
Class
Date
1.

What defines Big Data in the context of the "3Vs"?

a)

Volume, Validate, and Velocity

b)

Volume, Variety, and Velocity

c)

Variety, Value, and Volume

d)

Volume, Velocity, and Verify

2.

What is the primary challenge in handling Big Data?

a)

Lack of storage solutions

b)

Slow data processing speed

c)

Managing large volumes of data

d)

Data accuracy and consistency

3.

In Big Data processing, what is the primary objective of data validation?

a)

Ensuring data accuracy and consistency

b)

Reducing storage costs

c)

Improving data processing speed

d)

Handling data variety

4.

Which of the following is a typical characteristic of Big Data workloads?

a)

Low data velocity

b)

Highly structured data

c)

High data variability

d)

Low data volume

5.

What is the primary purpose of Hortonworks Data Platform (HDP)?

a)

Data storage and retrieval

b)

Real-time data processing

c)

Providing an integrated platform for managing, processing, and analyzing Big Data

d)

Data visualization and reporting

6.

What is the core storage system used by Hadoop for distributed storage and processing of data?

a)

HBase

b)

Hive

c)

HDFS (Hadoop Distributed File System)

d)

Spark

7.

What component in Hadoop is responsible for resource management and job scheduling in the cluster?

a)

HBase

b)

Hive

c)

YARN (Yet Another Resource Negotiator)

d)

Pig

8.

What is the main function of the MapReduce framework in Hadoop?

a)

Data storage

b)

Data analysis

c)

Data cleansing

d)

Data visualization

9.

In Hadoop, what is the primary role of the ResourceManager in YARN (Yet Another Resource Negotiator)?

a)

Data storage

b)

Data replication

c)

Resource management and scheduling of jobs

d)

Data analysis

10.

What is the primary advantage of using a distributed file system like HDFS over traditional file systems?

a)

Lower cost

b)

Real-time processing

c)

Fault tolerance and scalability

d)

Simplicity

11.

In MapReduce, what is the function of the "Map" phase?

a)

Sorting and shuffling data

b)

Data reduction and aggregation

c)

Splitting the input data into key-value pairs

d)

Writing the output to HDFS

12.

What is the purpose of the "Reduce" phase in MapReduce?

a)

Data cleansing and validation

b)

Data visualization and reporting

c)

Data reduction and aggregation

d)

Data replication

13.

What does a MapReduce job typically take as input and produce as output?

a)

Takes structured data and produces unstructured data

b)

Takes unstructured data and produces structured data

c)

Takes key-value pairs as input and produces key-value pairs as output

d)

Takes raw binary data as input and produces text data as output

14.

In MapReduce, what is the role of the "Shuffle and Sort" phase?

a)

Data replication

b)

Sorting and shuffling data

c)

Data cleansing

d)

Data storage and retrieval

15.

What is a key feature of MapReduce that makes it suitable for processing large datasets?

a)

Real-time data processing

b)

Parallel processing and fault tolerance

c)

Interactive querying

d)

In-memory data processing

16.

What is the primary role of YARN (Yet Another Resource Negotiator) in Hadoop?

a)

Real-time data processing and analysis

b)

Data storage and retrieval

c)

Resource management and job scheduling

d)

Data cleansing and transformation

17.

What is the key improvement that YARN introduces over the classic MapReduce model?

a)

Support for batch processing

b)

Real-time data processing capabilities

c)

Improved resource management with support for various data processing frameworks

d)

Enhanced data security and encryption

18.

In YARN, what is the role of the Resource Manager?

a)

Real-time data processing

b)

Managing data storage

c)

Resource management and scheduling of jobs

d)

Data analysis

19.

In YARN, what is the role of the NodeManager?

a)

Data storage and retrieval

b)

Real-time data processing

c)

Data replication and recovery

d)

Managing resources on individual nodes in the cluster

20.

What is Apache Spark primarily used for in the Big Data ecosystem?

a)

Real-time data processing and analysis

b)

Data storage and retrieval

c)

Data governance and data quality

d)

Data visualization and reporting

21.

What is a key advantage of Spark over Hadoop MapReduce for data processing?

a)

Spark is slower but more reliable

b)

Spark is more efficient at data storage

c)

Spark is faster and better suited for iterative algorithms

d)

Spark is better at handling structured data

22.

What is the main purpose of Hadoop's HDFS (Hadoop Distributed File System)?

a)

Real-time data processing

b)

Data storage and retrieval

c)

Data visualization and reporting

d)

Data cleaning and transformation

23.

What is the primary role of the NameNode in HDFS?

a)

Storing actual data blocks

b)

Managing and storing metadata about data blocks and their locations

c)

Data processing and analysis

d)

Data replication

24.

In HDFS, what is the role of the DataNode?

a)

Data storage

b)

Resource management

c)

Real-time data processing

d)

Job scheduling

25.

What does the "Block Size" parameter in HDFS determine?

a)

The size of each data block

b)

The number of DataNodes in the cluster

c)

The number of files in the filesystem

d)

The size of the HDFS namespace

26.

What is the primary advantage of using HDFS for data storage in Big Data applications?

a)

Real-time processing

b)

Data governance and security

c)

Fault tolerance and scalability

d)

High-speed data processing

27.

The process of extracting valuable insights from Big Data is known as

(a)  

28.

Hortonworks Data Platform (HDP) provides an integrated platform for managing, processing, and analyzing Big Data through various (a)   tools.

29.

Hadoop's core storage system for distributed data storage and processing is called as

(a)  

30.

YARN is defined as

(a)