Bin Multiple Choice Questions and Answers (MCQs)

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This set of Data Structures & Algorithms Multiple Choice Questions & Answers (MCQs) focuses on “Bin”.

1. What is the use of the bin data structure?
a) to have efficient insertion
b) to have efficient deletion
c) to have efficient region query
d) to have efficient traversal
View Answer

Answer: c
Explanation: Bin data structure allows us to have efficient region queries. A frequency of bin is increased by one each time a data point falls into a bin.
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2. Bin is an example of a range query data structure.
a) true
b) false
View Answer

Answer: a
Explanation: Bin is an example of a range query data structure. It is because it efficiently answers any number of queries on any subset of the input.

3. What is the worst case time complexity of query operation(n is the no. of candidates)?
a) O(1)
b) O(n)
c) O(log n)
d) O(n log n)
View Answer

Answer: b
Explanation: The worst case in a bin query occurs when all the candidates are concentrated in one bin. So in this case the time complexity is O(n).
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4. What is the worst case time complexity of delete operation(n is the no. of candidates)?
a) O(1)
b) O(n)
c) O(log n)
d) O(n log n)
View Answer

Answer: b
Explanation: The worst case in a bin delete operation occurs when all the candidates are concentrated in one bin. So in this case the time complexity is O(n).

5. What is the worst case time complexity of insertion operation(n =no. of candidates)?
a) O(1)
b) O(n)
c) O(log n)
d) O(n log n)
View Answer

Answer: a
Explanation: The worst case in a bin insert operation occurs when all the candidates are concentrated in one bin. So in this case the time complexity is O(1).
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6. What is computational geometry?
a) study of geometry using a computer
b) study of geometry
c) study of algorithms
d) study of algorithms related to geometry
View Answer

Answer: d
Explanation: Computational geometry deals with the study of algorithms which can be expressed in terms of geometry. Bin data structure is an example of it.

7. What will be the time complexity of query operation if all the candidates are evenly spaced so that each bin has constant no. of candidates? (k = number of bins query rectangle intersects)
a) O(1)
b) O(k)
c) O(k2)
d) O(log k)
View Answer

Answer: b
Explanation: The process of query becomes faster in a case when the number of candidates are equally distributed among the bins. In such a case the query operation becomes O(k).
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8. What will be the time complexity of delete operation if all the candidates are evenly spaced so that each bin has constant no. of candidates? (m = number of bins intersecting candidate intersects)
a) O(1)
b) O(m)
c) O(m2)
d) O(log m)
View Answer

Answer: b
Explanation: The process of deletion becomes faster in a case when the number of candidates are equally distributed among the bins. In such a case the query operation becomes O(m). It is practically slower than insertion in this case.

9. What will be the time complexity of insertion operation if all the candidates are evenly spaced so that each bin has constant no. of candidates? (m = number of bins intersecting candidate intersects)
a) O(1)
b) O(m)
c) O(m2)
d) O(log m)
View Answer

Answer: b
Explanation: The process of insertion becomes faster in the case when the number of candidates are equally distributed among the bins. In such a case the query operation becomes O(m). It is practically faster than deletion in this case.
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10. Efficiency of bin depends upon ___________
a) size of query and candidates
b) location of query and candidates
c) location and size of query and candidates
d) depends on the input
View Answer

Answer: c
Explanation: Efficiency of bin depends upon the location and size of query and candidates. It is similar to that of a hash table.

11. Bigger the query rectangle the better is the query efficiency.
a) true
b) false
View Answer

Answer: b
Explanation: Efficiency of bin depends upon the location and size of query and candidates. Also, the smaller is the query rectangle the better is the query efficiency.

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Manish Bhojasia - Founder & CTO at Sanfoundry
Manish Bhojasia, a technology veteran with 20+ years @ Cisco & Wipro, is Founder and CTO at Sanfoundry. He is Linux Kernel Developer & SAN Architect and is passionate about competency developments in these areas. He lives in Bangalore and delivers focused training sessions to IT professionals in Linux Kernel, Linux Debugging, Linux Device Drivers, Linux Networking, Linux Storage, Advanced C Programming, SAN Storage Technologies, SCSI Internals & Storage Protocols such as iSCSI & Fiber Channel. Stay connected with him @ LinkedIn | Youtube | Instagram | Facebook | Twitter