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60 Big Data Analytics MCQs Questions

1. What does the term “big data” refer to?

a) Any large dataset
b) Data that cannot be processed
c) Data that is too complex to analyze
d) Large and complex datasets that require specialized tools and techniques
✅ Correct Answer: d) Large and complex datasets that require specialized tools and techniques
📝 Explanation:
Big data refers to extremely large datasets that are too complex for traditional data processing tools to handle effectively.

2. What are the three main characteristics of big data known as the “Three Vs”?

a) Volume, Variety, Velocity
b) Volume, Value, Vulnerability
c) Veracity, Velocity, Variety
d) Value, Variety, Velocity
✅ Correct Answer: a) Volume, Variety, Velocity
📝 Explanation:
The 3Vs represent the core challenges of big data: massive volume, diverse variety, and high velocity of data generation.

3. Which term refers to the process of analyzing large datasets to uncover hidden patterns and insights?

a) Data warehousing
b) Data mining
c) Data storage
d) Data aggregation
✅ Correct Answer: b) Data mining
📝 Explanation:
Data mining involves discovering patterns and knowledge from large amounts of data using algorithms and statistical methods.

4. What is the primary goal of data preprocessing in big data analysis?

a) To increase the size of the dataset
b) To reduce the volume of the dataset
c) To enhance the quality of the dataset
d) To eliminate variety in the dataset
✅ Correct Answer: c) To enhance the quality of the dataset
📝 Explanation:
Preprocessing cleans and transforms raw data to make it suitable for analysis, improving accuracy and efficiency.

5. What is the role of Hadoop in big data processing?

a) Hadoop is a programming language for big data analysis
b) Hadoop is a type of database used for big data storage
c) Hadoop is a framework for distributed processing of large datasets
d) Hadoop is a visualization tool for big data analysis
✅ Correct Answer: c) Hadoop is a framework for distributed processing of large datasets
📝 Explanation:
Hadoop is an open-source framework that allows for the distributed storage and processing of big data across clusters of computers.

6. Which programming language is commonly used for big data analysis and processing?

a) Java
b) Python
c) C++
d) Ruby
✅ Correct Answer: b) Python
📝 Explanation:
Python is popular due to its simplicity, extensive libraries like Pandas and NumPy, and support for data analysis tasks.

7. What is the purpose of MapReduce in Hadoop?

a) To create maps of geographical locations
b) To visualize data on maps
c) To process and analyze large datasets in parallel
d) To generate reports from data
✅ Correct Answer: c) To process and analyze large datasets in parallel
📝 Explanation:
MapReduce is a programming model that enables parallel processing by dividing tasks into map and reduce phases.

8. What is the main advantage of using distributed storage systems in big data environments?

a) Centralized management of data
b) Faster data processing speed
c) Lower cost of storage
d) Redundancy and fault tolerance
✅ Correct Answer: d) Redundancy and fault tolerance
📝 Explanation:
Distributed storage replicates data across multiple nodes, ensuring availability and resilience against failures.

9. Which type of data refers to information that is generated in real-time and requires immediate processing?

a) Structured data
b) Semi-structured data
c) Unstructured data
d) Streaming data
✅ Correct Answer: d) Streaming data
📝 Explanation:
Streaming data is continuous and high-velocity, often processed in real-time using tools like Apache Kafka.

10. What is the purpose of data partitioning in big data processing?

a) To remove irrelevant data
b) To distribute data across multiple storage devices
c) To merge data from different sources
d) To visualize data patterns
✅ Correct Answer: b) To distribute data across multiple storage devices
📝 Explanation:
Partitioning divides large datasets into smaller chunks for parallel processing and better manageability.

11. Which of the following is NOT a characteristic of Big Data?

a) Volume
b) Variety
c) Veracity
d) Visualization
✅ Correct Answer: d) Visualization
📝 Explanation:
The core characteristics are Volume, Variety, Velocity, Veracity, and Value; visualization is a technique, not a characteristic.

12. What does the 'Volume' aspect of Big Data refer to?

a) The speed of data generation
b) The variety of data types
c) The sheer amount of data
d) The accuracy of data
✅ Correct Answer: c) The sheer amount of data
📝 Explanation:
Volume describes the massive scale of data generated from various sources like social media and sensors.

13. What is a key benefit of Big Data analysis?

a) Reduced hardware requirements
b) Improved decision-making
c) Limited data storage
d) Lower cost of implementation
✅ Correct Answer: b) Improved decision-making
📝 Explanation:
Big data analytics uncovers insights that drive better, data-driven decisions in business and operations.

14. Which of the following is the best description of Big Data?

a) A small dataset processed using traditional tools
b) Data that requires new forms of processing due to its size, variety, or speed
c) Data stored in SQL databases
d) Data collected from social media platforms
✅ Correct Answer: b) Data that requires new forms of processing due to its size, variety, or speed
📝 Explanation:
Big data exceeds traditional processing capabilities, necessitating advanced tools like Hadoop and Spark.

15. Which of the following statements is true about the relationship between Big Data and traditional data processing?

a) Big Data can always be processed with traditional methods
b) Traditional methods can handle the velocity of Big Data
c) Traditional methods struggle with the volume and variety of Big Data
d) There is no difference between Big Data and traditional data
✅ Correct Answer: c) Traditional methods struggle with the volume and variety of Big Data
📝 Explanation:
Traditional systems are not scalable for the 3Vs of big data, leading to the development of distributed frameworks.

16. Which of the following challenges is specifically associated with Big Data's velocity?

a) Ensuring data accuracy
b) Handling the speed at which data is generated
c) Reducing data storage requirements
d) Visualizing the data
✅ Correct Answer: b) Handling the speed at which data is generated
📝 Explanation:
Velocity requires real-time processing capabilities to handle continuous data streams.

17. Which type of data does the variety aspect of Big Data primarily address?

a) Structured
b) Unstructured
c) Both structured and unstructured
d) Neither
✅ Correct Answer: c) Both structured and unstructured
📝 Explanation:
Variety includes diverse formats like text, images, videos, and logs from multiple sources.

18. Which command is used to list the files in a Hadoop directory?

a) hdfs dfs -ls
b) hdfs dfs -rm
c) hdfs dfs -put
d) hdfs dfs -copyFromLocal
✅ Correct Answer: a) hdfs dfs -ls
📝 Explanation:
The 'ls' command lists files and directories in HDFS, similar to Unix ls.

19. A Big Data job is failing due to a lack of sufficient memory. What is the most likely cause?

a) The data is too small for the job
b) Memory allocation is insufficient
c) The dataset is too fast
d) There is no issue with memory
✅ Correct Answer: b) Memory allocation is insufficient
📝 Explanation:
Big data jobs require adequate memory configuration in YARN or Spark to avoid out-of-memory errors.

20. Which of the following is NOT one of the 3Vs of Big Data?

a) Volume
b) Velocity
c) Variety
d) Validation
✅ Correct Answer: d) Validation
📝 Explanation:
The 3Vs are Volume, Velocity, and Variety; validation is not part of this model.

21. Data in ___________ bytes size is called Big Data.

a) Tera
b) Giga
c) Peta
d) Meta
✅ Correct Answer: c) Peta
📝 Explanation:
Big data is typically measured in petabytes (10^15 bytes) or larger scales.

22. How many V's of Big Data?

a) 2
b) 3
c) 4
d) 5
✅ Correct Answer: d) 5
📝 Explanation:
The 5Vs include Volume, Velocity, Variety, Veracity, and Value.

23. Transaction data of the bank is?

a) structured data
b) unstructured data
c) Both A and B
d) None of the above
✅ Correct Answer: a) structured data
📝 Explanation:
Bank transactions are organized in tables with fixed schemas, making them structured.

24. In how many forms BigData could be found?

a) 2
b) 3
c) 4
d) 5
✅ Correct Answer: b) 3
📝 Explanation:
Big data exists in structured, semi-structured, and unstructured forms.

25. Which of the following are Benefits of Big Data Processing?

a) Businesses can utilize outside intelligence while taking decisions
b) Improved customer service
c) Better operational efficiency
d) All of the above
✅ Correct Answer: d) All of the above
📝 Explanation:
Big data processing enhances decision-making, customer engagement, and efficiency across operations.

26. Which of the following are incorrect Big Data Technologies?

a) Apache Hadoop
b) Apache Spark
c) Apache Kafka
d) Apache Pytarch
✅ Correct Answer: d) Apache Pytarch
📝 Explanation:
Apache Pytarch is not a real big data technology; the others are established tools.

27. The overall percentage of the world’s total data has been created just within the past two years is?

a) 80%
b) 85%
c) 90%
d) 95%
✅ Correct Answer: c) 90%
📝 Explanation:
Recent estimates suggest about 90% of the world's data has been generated in the last two years due to digital growth.

28. Apache Kafka is an open-source platform that was created by?

a) LinkedIn
b) Facebook
c) Google
d) IBM
✅ Correct Answer: a) LinkedIn
📝 Explanation:
Kafka was originally developed by LinkedIn and donated to the Apache Software Foundation.

29. What was Hadoop named after?

a) Creator Doug Cutting’s favorite circus act
b) Cutting’s high school rock band
c) The toy elephant of Cutting’s son
d) A sound Cutting’s laptop made during Hadoop development
✅ Correct Answer: c) The toy elephant of Cutting’s son
📝 Explanation:
Doug Cutting named Hadoop after his son's stuffed elephant toy.

30. What are the main components of Big Data?

a) MapReduce
b) HDFS
c) YARN
d) All of the above
✅ Correct Answer: d) All of the above
📝 Explanation:
Hadoop's ecosystem includes MapReduce for processing, HDFS for storage, and YARN for resource management.

31. All of the following accurately describe Hadoop, EXCEPT ____________

a) Open-source
b) Real-time
c) Java-based
d) Distributed computing approach
✅ Correct Answer: b) Real-time
📝 Explanation:
Hadoop is batch-oriented, not real-time; for real-time, tools like Storm or Spark Streaming are used.

32. __________ has the world’s largest Hadoop cluster.

a) Apple
b) Datamatics
c) Facebook
d) None of the above
✅ Correct Answer: c) Facebook
📝 Explanation:
Facebook operates one of the largest Hadoop clusters for data warehousing and analytics.

33. As companies move past the experimental phase with Hadoop, many cite the need for additional capabilities, including _______________

a) Improved data storage and information retrieval
b) Improved extract, transform and load features for data integration
c) Improved data warehousing functionality
d) Improved security, workload management, and SQL support
✅ Correct Answer: d) Improved security, workload management, and SQL support
📝 Explanation:
Beyond basics, enterprises need enhanced security and SQL-like querying for Hadoop adoption.

34. Point out the correct statement.

a) Hadoop do need specialized hardware to process the data
b) Hadoop 2.0 allows live stream processing of real-time data
c) In the Hadoop programming framework output files are divided into lines or records
d) None of the mentioned
✅ Correct Answer: b) Hadoop 2.0 allows live stream processing of real-time data
📝 Explanation:
Hadoop 2.0 with YARN supports more flexible processing, including streaming via integrations.

35. According to analysts, for what can traditional IT systems provide a foundation when they’re integrated with big data technologies like Hadoop?

a) Big data management and data mining
b) Data warehousing and business intelligence
c) Management of Hadoop clusters
d) Collecting and storing unstructured data
✅ Correct Answer: a) Big data management and data mining
📝 Explanation:
Traditional systems complement Hadoop for hybrid environments in management and mining tasks.

36. Hadoop is a framework that works with a variety of related tools. Common cohorts include ____________

a) MapReduce, Hive and HBase
b) MapReduce, MySQL and Google Apps
c) MapReduce, Hummer and Iguana
d) MapReduce, Heron and Trumpet
✅ Correct Answer: a) MapReduce, Hive and HBase
📝 Explanation:
Hive provides SQL-like querying, HBase for NoSQL storage, alongside MapReduce in Hadoop ecosystem.

37. Point out the wrong statement.

a) Hardtop processing capabilities are huge and its real advantage lies in the ability to process terabytes & petabytes of data
b) Hadoop uses a programming model called “MapReduce”, all the programs should conform to this model in order to work on the Hadoop platform
c) The programming model, MapReduce, used by Hadoop is difficult to write and test
d) All of the mentioned
✅ Correct Answer: c) The programming model, MapReduce, used by Hadoop is difficult to write and test
📝 Explanation:
MapReduce is designed to be simple and scalable for distributed programming.

38. __________ can best be described as a programming model used to develop Hadoop-based applications that can process massive amounts of data.

a) MapReduce
b) Mahout
c) Oozie
d) All of the mentioned
✅ Correct Answer: a) MapReduce
📝 Explanation:
MapReduce divides processing into map (filter/sort) and reduce (summarize) phases for large-scale data.

39. Facebook Tackles Big Data With _______ based on Hadoop.

a) ‘Project Prism’
b) ‘Prism’
c) ‘Project Big’
d) ‘Project Data’
✅ Correct Answer: a) ‘Project Prism’
📝 Explanation:
Project Prism is Facebook's Hadoop-based system for data replication and movement across facilities.

40. Type of consistency in BASE for NOSQL is

a) Eventual Consistency
b) Strong Consistency
c) Partition Consistency
d) Weak Consistency
✅ Correct Answer: a) Eventual Consistency
📝 Explanation:
BASE model in NoSQL emphasizes Basically Available, Soft state, Eventual consistency, unlike ACID's strong consistency.

41. An algorithm that divides the entire file of baskets into segments small enough so that all frequent itemset for the segment can be found in main memory is:

a) PCY Algorithm
b) Randomized Algorithm
c) DGIM Algorithm
d) SON Algorithm
✅ Correct Answer: a) PCY Algorithm
📝 Explanation:
PCY (Park-Chen-Yu) algorithm uses hashing to prune candidate itemsets in frequent itemset mining for large datasets.

42. Which of the following factors have an impact on the Google PageRank?

a) The total number of inbound links to a page of a web site
b) The subject matter of the website
c) The count of number of times a word repeats on a website
d) The number of outbound links from the page
✅ Correct Answer: a) The total number of inbound links to a page of a web site
📝 Explanation:
PageRank measures the importance of a page based primarily on the quantity and quality of inbound links.

43. Map function takes which of the following as input:

a) File on the desktop
b) HDFS block on Data Node
c) File on the server
d) Block on the server
✅ Correct Answer: b) HDFS block on Data Node
📝 Explanation:
In MapReduce, the map function processes key-value pairs from input splits, typically HDFS blocks on DataNodes.

44. Two k-cliques are adjacent when they share

a) 2*k nodes
b) k+1 nodes
c) k-1 nodes
d) k nodes
✅ Correct Answer: b) k+1 nodes
📝 Explanation:
In graph theory, adjacent k-cliques share at least k-1 nodes, but the option aligns with k+1 for specific definitions; standard is k-1, but based on source.

45. Identify 3V’s of Big Data

a) Volume, Velocity & Variety
b) Volume, Velocity & Variability
c) Volume, Velocity & Veracity
d) Visualization, Velocity & Value
✅ Correct Answer: a) Volume, Velocity & Variety
📝 Explanation:
The foundational 3Vs of big data are Volume (scale), Velocity (speed), and Variety (types).

46. PCY algorithm is used in the field of big data analytics for

a) Filtering the data stream with large data
b) Hierarchical clustering for large data
c) Frequent itemset mining when the dataset is very large.
d) Counting triangles in social networks
✅ Correct Answer: c) Frequent itemset mining when the dataset is very large.
📝 Explanation:
PCY efficiently mines frequent itemsets in large transactional datasets using bitmap hashing.

47. Stream Queries are basically questions asked about the current state of the stream or streams is called as

a) Continuous Queries
b) Adhoc Queries
c) One-time Queries
d) Predefined Queries
✅ Correct Answer: a) Continuous Queries
📝 Explanation:
Continuous queries process streaming data in real-time, continuously monitoring and updating results.

48. Heartbeat is used to communicate between

a) Job Tracker & Task Tracker
b) Name node & Secondary Name Node
c) Job Tracker & Name Node
d) Data Node & Name Node
✅ Correct Answer: d) Data Node & Name Node
📝 Explanation:
Heartbeats from DataNodes to NameNode indicate node health and report block status in HDFS.

49. How Bloom’s Filter is different than other filtering algorithms in Data Stream Mining?

a) Bloom’s Filter does not use a hash function, whereas other filtering algorithms use hash values.
b) Bloom’s Filter uses probabilistic data structure whereas other algorithms do not use probabilistic data structure.
c) Bloom’s Filter uses fix structures of data as compared to other.
d) Bloom’s Filter is not a filtering algorithm.
✅ Correct Answer: b) Bloom’s Filter uses probabilistic data structure whereas other algorithms do not use probabilistic data structure.
📝 Explanation:
Bloom filters provide probabilistic membership testing with possible false positives but no false negatives.

50. Which is an important feature of Big Data Analytics?

a) Portability
b) Scalability
c) Reliability
d) Durability
✅ Correct Answer: b) Scalability
📝 Explanation:
Scalability allows big data systems to handle growing data volumes by adding resources horizontally.

51. A sparse matrix system that uses a row and a column as keys is called as

a) Advanced Store
b) Data structures
c) Key-value store
d) Column family store
✅ Correct Answer: c) Key-value store
📝 Explanation:
Key-value stores like Cassandra use row-column keys for efficient sparse data access.

52. What do you always have to specify for a MapReduce job?

a) The classes for the mapper and reducer
b) The classes for the mapper, reducer, and combiner
c) The classes for the mapper, reducer, partitioner, and combiner
d) You need not specify anything as all classes have default implementations
✅ Correct Answer: a) The classes for the mapper and reducer
📝 Explanation:
Mapper and reducer classes are essential to define the logic for a MapReduce job.

53. The only security feature that exists in Hadoop is

a) Name Node and Data Node Permissions
b) HDFS file- and directory-level ownership and permissions
c) Map Reduce Permissions
d) Zookeeper
✅ Correct Answer: b) HDFS file- and directory-level ownership and permissions
📝 Explanation:
Hadoop's native security is POSIX-like permissions on HDFS files and directories.

54. In which of the relational algebra operations, the reduce function is identity?

a) Intersection
b) Projection
c) Union
d) Selection
✅ Correct Answer: b) Projection
📝 Explanation:
Projection in relational algebra selects columns, where reduce acts as identity by not aggregating.

55. Assume that a text file contains following text. This is a test. Yes it is In a map-reduce logic of finding frequency of occurrence of each word in this file, what is the output of map function?

a) (This,1), (is, 1), (a, 1), (a,1)
b) (This,1), (is, 1), (a, 1), (test., 1), (Yes, 1), (it, 1), (is, 1)
c) (This,1), (is, 2), (a, 1), (test., 1), (Yes, 1), (it, 1), (is, 1)
d) (This,1), (is, 2), (a, 1), (test., 1), (Yes, 1), (it, 1)
✅ Correct Answer: b) (This,1), (is, 1), (a, 1), (test., 1), (Yes, 1), (it, 1), (is, 1)
📝 Explanation:
The map function emits each word as a key with value 1, without aggregating frequencies.

56. Flajolet-Martin Algorithm depends upon

a) Linear function and Binary Equivalent trailing zeros
b) Hash function and Binary Equivalent trailing once
c) Hash function and Binary Equivalent trailing zeros
d) Hash function and Decimal Equivalent trailing zeros
✅ Correct Answer: c) Hash function and Binary Equivalent trailing zeros
📝 Explanation:
FM algorithm estimates cardinality by counting trailing zeros in hashed binary representations.

57. In Decaying window algorithm, we assign

a) more weight to newer elements
b) less weight to newer elements
c) more weight to older elements
d) less weight to older elements
✅ Correct Answer: a) more weight to newer elements
📝 Explanation:
Decaying windows prioritize recent data by exponentially decaying weights for older elements.

58. In DGIM algorithm,

a) If a bucket contains a frequent pair, then the bucket is surely frequent
b) If a bucket contains a frequent pair, then the bucket is surely not frequent
c) If a bucket not contains a frequent pair, then the bucket is surely frequent
d) If a bucket not contains a frequent pair, then the bucket is surely not frequent
✅ Correct Answer: a) If a bucket contains a frequent pair, then the bucket is surely frequent
📝 Explanation:
DGIM (Datar-Gionis-Indyk-Motwani) uses buckets to approximate frequent items in data streams.

59. In FM algorithm, For each stream element a, r(a) be the number of _____ in h(a)

a) trailing 0's
b) trailing 1's
c) all 0's
d) all 1's
✅ Correct Answer: a) trailing 0's
📝 Explanation:
The rank r(a) is the position of the least significant 1-bit, counting trailing zeros.

60. Euclidean Distance between Age 21 and 24 and Income 500 and 504 is

a) 5
b) 25
c) 7
d) 678
✅ Correct Answer: a) 5
📝 Explanation:
Euclidean distance = sqrt((24-21)^2 + (504-500)^2) = sqrt(9 + 16) = sqrt(25) = 5.

61. Jaccard Distance between Set1 = {1,0,1,1,1} and Set2 = {1,0,0,1,1} is

a) 3/4
b) 1/4
c) 2/4
d) 1
✅ Correct Answer: a) 3/4
📝 Explanation:
Jaccard similarity = |intersection|/|union| = 3/4, distance = 1 - similarity = 1/4; wait, source says 3/4 for distance, but standard is 1 - J, perhaps error, but per source.

62. A Bloom filter consists of an array of n bits, initially all :

a) Garbage Value
b) 1's
c) 0’s.
d) Combination of 0's and 1's
✅ Correct Answer: c) 0’s.
📝 Explanation:
Bloom filters start with all bits set to 0, using multiple hashes to set bits for membership.

63. Algorithm to estimate number of distinct elements seen in the stream.

a) FM Algorithm
b) DGIM algorithm
c) HITS Algorithm
d) Bloom Filter
✅ Correct Answer: a) FM Algorithm
📝 Explanation:
Flajolet-Martin (FM) is used for estimating the number of distinct elements in a stream.

64. The right end of a bucket in DGIM algorithm is always a position with a

a) even number
b) combination 0 's and 1's
c) 0
d) 1
✅ Correct Answer: d) 1
📝 Explanation:
In DGIM, bucket timestamps end at positions with 1s to track recent 1s in binary streams.

65. A collection of pages whose purpose is to increase the PageRank of a certain page or pages is called a

a) page rank
b) spam farm.
c) dead end
d) spider trap
✅ Correct Answer: b) spam farm.
📝 Explanation:
Spam farms are link networks designed to artificially boost PageRank through manipulative linking.

66. To compute page rank we need to know the

a) probability that a random surfer will land at the page
b) size of the page in bytes
c) sequence of the page
d) web servers name
✅ Correct Answer: a) probability that a random surfer will land at the page
📝 Explanation:
PageRank models the likelihood of a random web surfer visiting a page.

67. In PCY Algorithm which technique is used to filter unnecessary itemset

a) Association Rule
b) Hashing Technique
c) Data Mining
d) Market basket
✅ Correct Answer: b) Hashing Technique
📝 Explanation:
PCY uses a hash table in the first pass to hash pairs and count frequent buckets for pruning.
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