Big Data MCQ's




Question 31 :
Following is based on grid like street geography of the New York:

  1. Manhattan Distance
  2. Edit Distance
  3. Hamming distance
  4. Lp distance
  

Question 32 :
Which of the following statements about standard Bloom filters is correct?

  1. It is possible to delete an element from a Bloom filter.
  2. A Bloom filter always returns the correct result.
  3. It is possible to alter the hash functions of a full Bloom filter to create more space.
  4. A Bloom filter always returns TRUE when testing for a previously added element.
  

Question 33 :
The hardware term used to describe Hadoop hardware requirements is

  1. Commodity firmware
  2. Commodity software
  3. Commodity hardware
  4. Cluster hardware
  

Question 34 :
A ________________ query Q is a query that is issued once over a database D, and then logically runs continuously over the data in D until Q is terminated.

  1. One-time Query
  2. Standing Query
  3. Adhoc Query
  4. General Query
  

Question 35 :
Find Hamming Distance for vectors A=100101011 B=100010010

  1. 2
  2. 4
  3. 3
  4. 1
  

Question 36 :
Which of the following statements about data streaming is true?

  1. Stream data is always unstructured data.
  2. Stream data often has a high velocity.
  3. Stream elements cannot be stored on disk.
  4. Stream data is always structured data.
  

Question 37 :
Sliding window operations typically fall in the category

  1. OLTP Transactions
  2. Big Data Batch Processing
  3. Big Data Real Time Processing
  4. Small Batch Processing
  

Question 38 :
In Bloom filter an array of n bits is initialized with

  1. all 0s
  2. all 1s
  3. half 0s and half 1s
  4. all -1
  

Question 39 :
The Jaccard similarity of two non-binary sets A and B, is defined by__________

  1. Jaccard Index
  2. Primary Index
  3. Secondary Index
  4. Clustered Index
  

Question 40 :
Find the L1 and L2 distances between the points (5, 6, 7) and (8, 2, 4).

  1. L1 =10 , L2 = 5.83
  2. L1 =10 , L2 = 5
  3. L1 =11 , L2 = 4.9
  4. L1 =9 , L2 = 5.83
  

Question 41 :
Which of the following is a NoSQL Database Type ?

  1. SQL
  2. JSON
  3. Document databases
  4. CSV
  

Question 42 :
Which of the following is responsible for managing the cluster resources and use them for scheduling users’ applications?

  1. Hadoop Common
  2. YARN
  3. HDFS
  4. MapReduce
  

Question 43 :
What do you mean by sampling of stream data?

  1. Sampling reduces the amount of data fed to a subsequent data mining algorithm.
  2. Sampling reduces the diversity of the data stream
  3. Sampling aims to keep statistical properties of the data intact.
  4. Sampling algorithms often doesn't need multiple passes over the data
  

Question 44 :
_____________is a batch-based, distributed computing framework modeled after Google’s paper.

  1. MapCompute
  2. MapReuse
  3. MapCluster
  4. MapReduce
  

Question 45 :
If size of file is 4 GB and block size is 64 MB then number of mappers required for MapReduce task is

  1. 8
  2. 16
  3. 32
  4. 64
  

Question 46 :
Which of the following is not the class of points in BFR algorithm

  1. Discard Set (DS)
  2. Compression Set (CS)
  3. Isolation Set (IS)
  4. Retained Set (RS)
  

Question 47 :
Which of the following decides the number of partitions that are created on the local file system of the worker nodes?

  1. Number of map tasks
  2. Number of reduce tasks
  3. Number of file input splits
  4. Number of distinct keys in the intermediate key-value pairs
  

Question 48 :
During start up, the ___________ loads the file system state from the fsimage and the edits log file.

  1. Datanode
  2. Namenode
  3. Secondary Namenode
  4. Rack awereness policy
  

Question 49 :
which of the following is not the characterstic of stream data?

  1. Continuous
  2. ordered
  3. persistant
  4. huge
  

Question 50 :
What is the finally produced by Hierarchical Agglomerative Clustering?

  1. final estimate of cluster centroids
  2. assignment of each point to clusters
  3. tree showing how close things are to each other
  4. Group of clusters
  
Pages